to treatment groups
Just for emphasis, the means from Table 1 are presented in the next two figures (Fig. 1 and Fig. 2).
Figure 1. Age of subjects by groups (A = blue, B = red) with and without randomized assignment of subjects to treatment groups
Figure 2. BMI of subjects by groups (A = blue, B = red) with and without randomized assignment of subjects to treatment groups
Note that the apparent difference between A and B for BMI disappear once proper randomization of subjects was accomplished. In conclusion, a random sample is an approach to experimental design that helps to reduce the influence other factors may have on the outcome variable (e.g., change in blood pressure after 16 weeks of exercise). In principle, randomization should protect a project because, on average, these influences will be represented randomly for the two groups of individuals. This reasoning extends to unmeasured and unknown causal factors as well.
This discussion was illustrated by random assignment of subjects to treatment groups. The same logic applies to how to select subjects from a population. If the sampling is large enough, then a random sample of subjects will tend to be representative of the variability of the outcome variable for the population and representative also of the additional and unmeasured cofactors that may contribute to the variability of the outcome variable.
However, if you do cannot obtain a random sample, then conclusions reached may be sample-specific, biased . …perhaps the group of individuals that likes to exercise on treadmills just happens to have a higher cardiac output because they are larger than the individuals that like to exercise on bicycles. This nonrandom sample will bias your results and can lead to incorrect interpretation of results. Random sampling is CRUCIAL in epidemiology, opinion survey work, most aspects of health, drug studies, medical work with human subjects. It’s difficult and very costly to do… so most surveys you hear about, especially polls reported from Internet sites, are NOT conducted using random sampling (included in the catch-all term “ probability sampling “)!! As an aside, most opinion survey work involves complex sample designs involving some form of geographic clustering (e.g., all phone numbers in a city, random sample among neighborhoods).
Random sampling is the ideal if generalizations are to be made about data, but strictly random sampling is not appropriate for all kinds of studies. Consider the question of whether or not EMF exposure is a risk factor for developing cancer (Pool 1990). These kinds of studies are observational: at least in principle, we wouldn’t expect that housing and therefore exposure to EMF is manipulated (cf. discussion Walker 2009). Thus, epidemiologists will look for patterns: if EMF exposure is linked to cancer, then more cases of cancer should occur near EMF sources compared to areas distant from EMF sources. Thus, the hypothesis is that an association between EMF exposure and cancer occurs non-randomly, whereas cancers occurring in people not exposed to EMF are random. Unfortunately, clusters can occur even if the process that generates the data is random.
Compare Graph A and Graph B (Fig. 3). One of the graphs resulted from a random process and the other was generated by a non-random process . Note that the claim can be rephrased about the probability that each grid has a point, e.g., it’s like Heads/Tails of 16 tosses of a coin. We can see clusters of points in Graph B; Graph A lacks obvious clusters of points — there is a point in each of the 16 cells of the grid. Although both patterns could be random, the correct answer in this case is Graph B.
Figure 3. An example of clustering resulting from a random sampling process (Graph B). In contrast, Graph A was generated so that a point was located within each grid.
The graphic below shows the transmission grid in the continental United States (Fig. 4). How would one design a random sampling scheme overlaid against the obviously heterogeneous distribution of the grid itself? If a random sample was drawn, chances are good that no population would be near a grid in many of the western states, but in contrast, the likelihood would increase in the eastern portion of the United States where the population and therefore transmission grid is more densely placed.
Figure 4. Map of electrical transmission grid for continental United States of America. Image source https://openinframap.org/#3/24.61/-101.16
For example, you want to test whether or not EMF affects human health, and your particular interest is in whether or not there exists a relationship between living close to high voltage towers or transfer stations and brain cancer. How does one design a study, keeping in mind the importance of randomization for our ability to generalize and assign causation? This is a part of epidemiology which strives to detect whether clusters of disease are related to some environmental source. It is an extremely difficult challenge. For the record, no clear link to EMF and cancer has been found, but reports do appear from time to time (e.g., report on a cluster of breast cancer in men working in office adjacent to high EMF, Milham 2004).
1. I claimed that Graph B in Figure 8 was generated by a random process while Graph B was not. The results are: Graph A, each cell in the grid has a point; In graph B, ten cells have at least one point, six cells are empty. Which probability _____ distribution applies? A. beta B. binomial C. normal D. poisson
2. True or False. If sample with replacement is used, a subject may be included more than once.
3. Use the sample() with and without replacement on the object (see help with R below)
a) set of 3
b) set of 4
4. Confirm the claim by calculating the probability of Graph A result vs Graph B result (see R script below).
Code you type is shown in red; responses or output from R are shown in blue. Recall that statements preceded by the hash # are comments and are not read by R (i.e., no need for you tp type them).
First, create some variables. Vectors aa and bb contain my two age sequences.
Second, append vector bb to the end of vector aa
Third, get the average age for the first group (the aa sequence) and for the second group (the bb sequence). Lots of ways to do this, I made a two subsets from the combined age variable; could have just as easily taken the mean of aa and the mean of bb (same thing!).
Fourth, start building a data frame, then sort it by age. Will be adding additional variables to this data frame
Fifth, divide the variable again into two subsets of 30 and get the averages
Sixth, create an index variable, random order without replacement
Add the new variable to our existing data frame, then print it to check that all is well
Seventh, select for our first treatment group the first 30 subjects from the randomized index. There are again other ways to do this, but sorting on the index variable means that the subject order will be change too.
Print the new data frame to confirm that the sorting worked. It did. we can see that the rows have been sorted by ascending order based on the index variable.
Eighth, create our new treatment groups, again of n = 30 each, then get the means ages for each group.
Get the minimum and maximum values for the groups
Ninth, create a BMI variable drawn from a normal distribution with coefficient of variation equal to 20%. The first group with we will call cc
The second group called dd
Create a new variable called BMI by joining cc and dd
Add the BMI variable to our data frame.
Tenth, repeat our protocol from before: Set up two groups each with 30 subjects, calculate the means for the variables and then sort by the random index and get the new group means.
All we did was confirm that the unsorted groups had mean BMI of around 27.5 and 37.5 respectively. Now, proceed to sort by the random index variable. Go ahead and create a new data frame
Get the means of the new groups
That’s all of the work!
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Completely Randomized Design (CRD) is a research methodology in which experimental units are randomly assigned to treatments without any systematic bias. CRD gained prominence in the early 20th century, largely attributed to the pioneering work of statistician Ronald A. Fisher . His method addressed the inherent variability in experimental units by randomly assigning treatments, thus countering potential biases. Today, CRD serves as an indispensable tool in various domains, including agriculture, medicine, industrial engineering, and quality control analysis.
CRD is particularly favored in situations with limited control over external variables. By leveraging its inherent randomness, CRD neutralizes potentially confounding factors. As a result, each experimental unit has an equal likelihood of receiving any specific treatment, ensuring a level playing field. Such random allocation is pivotal in eliminating systematic bias and bolstering the validity of experimental conclusions.
While CRD may sometimes necessitate larger sample sizes , the improved accuracy and consistency it introduces to results often justify this requirement.
At its core, CRD is centered on harnessing randomness to achieve objective experimental outcomes. This approach effectively addresses unanticipated extraneous variables —those not included in the study design but that can still influence the response variable. In the context of CRD, these extraneous variables are expected to be uniformly distributed across treatments, thereby mitigating their potential influence.
A key aspect of CRD is the single-factor experiment. This means that the experiment revolves around changing or manipulating one primary independent variable (or factor) to ascertain its effect on the dependent variable . Consider these examples across different fields:
For each of these scenarios, only one key factor or independent variable is intentionally varied, while any changes or outcomes in another variable (the dependent variable) are observed and recorded. This distinct focus on a single variable, while keeping all others constant or controlled, underscores the essence of the single-factor experiment in CRD.
Understanding the strengths of Completely Randomized Design is pivotal for effectively applying this research tool and interpreting results accurately. Below is an exploration of the benefits of employing CRD in research studies.
While CRD is marked by simplicity, flexibility, robustness, and enhanced generalizability, it is essential to carefully consider its limitations. A thoughtful analysis of these aspects will guide researchers in making informed decisions about the applicability of CRD to their specific research context.
CRD stands out in the realm of research designs due to its foundational simplicity. While its essence lies in the random assignment of experimental units to treatments without any systematic bias, other designs introduce varying layers of complexity tailored to specific experimental needs.
For instance, consider the Randomized Block Design (RBD) . Unlike the straightforward approach of CRD, RBD divides experimental units into homogenous blocks, based on known sources of variability, before assigning treatments. This method is especially useful when there's an identifiable source of variability that researchers wish to control for. Similarly, the Latin Square Design , while also involving random assignment, operates on a grid system to simultaneously control for two lurking variables , adding another dimension of complexity not found in CRD.
Factorial Design investigates the effects and interactions of multiple independent variables. This design can reveal interactions that might be overlooked in simpler designs. Then there's the Crossover Design , often used in medical trials. Unlike CRD, where each unit experiences only one treatment, in Crossover Design, participants receive multiple treatments over different periods, allowing each participant to serve as their own control.
The choice of research design, whether it be CRD, RBD, Latin Square, or any of the other methods available, is fundamentally guided by the nature of the research question , the characteristics of the experimental units, and the specific objectives the study aims to achieve. However, it's the inherent simplicity and flexibility of CRD that often makes it the go-to choice, especially in scenarios with many units or treatments, where intricate stratification or blocking isn't necessary.
Let us further explore the advantages and disadvantages of each method.
Research Design | Description | Key Features | Advantages | Disadvantages |
---|---|---|---|---|
Completely Randomized Design (CRD) | Employs random assignment of experimental units to treatments without any systematic bias. | Simple and flexible Each unit experiences only one treatment | Simple structure makes it easy to implement | Does not control for any other variables; may require a larger sample size |
Randomized Block Design (RBD) | Divides experimental units into homogenous blocks based on known sources of variability before assigning treatments. | Controls for one source of variability More complex than CRD | Controls for known variability, potentially increasing the precision of the experiment | More complex to implement and analyze |
Latin Square Design | Uses a grid system to control for two lurking variables. | Controls for two sources of variability Adds complexity not found in CRD | Controls for two sources of variability | Complex design; may not be practical for all experiments |
Factorial Design | Investigates the effects and interactions of multiple independent variables. | Reveals interactions More complex design | Can assess interactions between factors | Complex and may require a large sample size |
Crossover Design | Participants receive multiple treatments over different periods. | Each participant serves as their own control Often used in medical trials | Each participant can serve as their own control, potentially reducing variability | Period effects and carryover effects can complicate results |
While CRD's simplicity and flexibility make it a popular choice for many research scenarios, the optimal design depends on the specific needs, objectives, and contexts of the study. Researchers must carefully consider these factors to select the most suitable research design method.
Within the framework of experimental research, extraneous variables persistently challenge the validity of findings, potentially compromising the established relationship between independent and dependent variables . CRD is a methodological safeguard that systematically addresses these extraneous variables. Below, we describe specific types of extraneous variables and how CRD counteracts their potential influence:
The foundational principle underpinning the Completely Randomized Design—randomization—serves as a bulwark against the influences of extraneous variables. By uniformly distributing these variables across experimental conditions, CRD enhances the validity and reliability of experimental outcomes. However, researchers should exercise caution and continuously evaluate potential extraneous influences, even in randomized designs.
The selection of the independent variable is crucial for research design . This pivotal step not only shapes the direction and quality of the research but also underpins the understanding of causal relationships within the studied system, influencing the dependent variable or response. When choosing this essential component of experimental design , several critical considerations emerge:
Identifying the independent variable necessitates a methodical and structured approach where each step aligns with the overarching research objective:
In academic discourse, while CRD is praised for its rigor and clarity, the effectiveness of the design relies heavily on the meticulous selection of the independent variable. Making this choice with thorough consideration ensures the research offers valuable insights with both academic and wider societal implications.
CRD has found wide and varied applications in several areas of research. Its versatility and fundamental simplicity make it an attractive option for scientists and researchers across a multitude of disciplines.
Agricultural research was among the earliest fields to adopt the use of Completely Randomized Design. The broad application of CRD within agriculture not only encompasses crop improvement but also the systematic analysis of various fertilizers, pesticides, and cropping techniques. Agricultural scientists leverage the CRD framework to scrutinize the effects on yield enhancement and bolstered disease resistance. The fundamental randomization in CRD effectively mitigates the influence of nuisance variables such as soil variations and microclimate differences, ensuring more reliable and valid experimental outcomes.
Additionally, CRD in agricultural research paves the way for robust testing of new agricultural products and methods. The unbiased allocation of treatments serves as a solid foundation for accurately determining the efficacy and potential downsides of innovative fertilizers, genetically modified seeds, and novel pest control methods, contributing to informed decision-making and policy formulation in agricultural development.
However, the limitations of CRD within the agricultural context warrant acknowledgment. While it offers an efficient and straightforward approach for experimental design, CRD may not always capture spatial variability within large agricultural fields adequately. Such unaccounted variations can potentially skew results, underscoring the necessity for employing more intricate experimental designs, such as the Randomized Complete Block Design (RCBD), where necessary. This adaptation enhances the reliability and generalizability of the research findings, ensuring their applicability to real-world agricultural challenges.
The fields of medical and health research substantially benefit from the application of Completely Randomized Design, especially in executing randomized control trials. Within this context, participants, whether patients or others, are randomly assigned to either the treatment or control groups. This structured random allocation minimizes the impact of extraneous variables, ensuring that the groups are comparable. It fortifies the assertion that any discernible differences in outcomes are genuinely attributable to the treatment being analyzed, enhancing the robustness and reliability of the research findings.
CRD's randomized nature in medical research allows for a more objective assessment of varied medical treatments and interventions. By mitigating the influence of extraneous variables, researchers can more accurately gauge the effectiveness and potential side effects of novel medical approaches, including pharmaceuticals and surgical techniques. This precision is crucial for the continual advancement of medical science, offering a solid empirical foundation for the refinement of treatments that improve health outcomes and patient quality of life.
However, like other fields, the application of CRD in medical research has its limitations. Despite its effectiveness in controlling various factors, CRD may not always consider the complexity of human health conditions where multiple variables often interact in intricate ways. Hence, while CRD remains a valuable tool for medical research, it is crucial to apply it judiciously and alongside other research designs to ensure comprehensive and reliable insights into medical treatments and interventions.
In industrial engineering, Completely Randomized Design plays a significant role in process and product testing, offering a reliable structure for the evaluation and improvement of industrial systems. Engineers often employ CRD in single-factor experiments to analyze the effects of a particular factor on a certain outcome, enhancing the precision and objectivity of the assessment.
For example, to discern the impact of varying temperatures on the strength of a metal alloy, engineers might utilize CRD. In this scenario, the different temperatures represent the single factor, and the alloy samples are randomly allocated to be tested at each designated temperature. This random assignment minimizes the influence of extraneous variables, ensuring that the observed effects on alloy strength are primarily attributable to the temperature variations.
CRD's implementation in industrial engineering also assists in the optimization of manufacturing processes. Through random assignment and structured testing, engineers can effectively evaluate process parameters, such as production speed, material quality, and machine settings. By accurately assessing the influence of these factors on production efficiency and product quality, engineers can implement informed adjustments and enhancements, promoting optimal operational performance and superior product standards. This systematic approach, anchored by CRD, facilitates consistent and robust industrial advancements, bolstering overall productivity and innovation in industrial engineering.
Despite these advantages, it's crucial to acknowledge the limitations of CRD in industrial engineering contexts. The design is efficient for single-factor experiments but may falter with experiments involving multiple factors and interactions, common in industrial settings. This limitation underscores the importance of combining CRD with other experimental designs. Doing so navigates the complex landscape of industrial engineering research, ensuring insights are comprehensive, accurate, and actionable for continuous innovation in industrial operations.
Completely Randomized Design is also beneficial in quality control analysis, where ensuring the consistency of products is paramount.
For instance, a manufacturer keen on minimizing product defects may deploy CRD to empirically assess the effectiveness of various inspection techniques. By randomly assigning different inspection methods to identical or similar production batches, the manufacturer can gather data regarding the most effective techniques for identifying and mitigating defects, bolstering overall product quality and consumer satisfaction.
Furthermore, the utility of CRD in quality control extends to the analysis of materials, machinery settings, or operational processes that are pivotal to final product quality. This design enables organizations to rigorously test and compare assorted conditions or settings, ensuring the selection of parameters that optimize both quality and efficiency. This approach to quality analysis not only bolsters the reliability and performance of products but also significantly augments the optimization of organizational resources, curtailing wastage and improving profitability.
However, similar to other CRD applications, it is crucial to understand its limitations. While CRD can significantly aid in the analysis and optimization of various aspects of quality control, its effectiveness may be constrained when dealing with multi-factorial scenarios with complex interactions. In such situations, other experimental designs, possibly in tandem with CRD, might offer more robust and comprehensive insights, ensuring that quality control measures are not only effective but also adaptable to evolving industrial and market demands.
The breadth of applications for Completely Randomized Design continues to expand. Emerging fields such as data science, business analytics, and environmental studies are increasingly recognizing the value of CRD in conducting reliable and uncomplicated experiments. In the realm of data science, CRD can be invaluable in assessing the performance of different algorithms, models, or data processing techniques. It enables researchers to randomize the variables, minimizing biases and providing a clearer understanding of the real-world applicability and effectiveness of various data-centric solutions.
In the domain of business analytics, CRD is paving the way for robust analysis of business strategies and initiatives. Businesses can employ CRD to randomly assign strategies or processes across various departments or teams, allowing for a comprehensive assessment of their impact. The insights from such assessments empower organizations to make data-driven decisions, optimizing their operations, and enhancing overall productivity and profitability. This approach is particularly crucial in the business environment of today, characterized by rapid changes, intense competition, and escalating customer expectations, where informed and timely decision-making is a key determinant of success.
Moreover, in environmental studies, CRD is increasingly being used to evaluate the impact of various factors on environmental health and sustainability. For example, researchers might use CRD to study the effects of different pollutants, conservation strategies, or land use patterns on ecosystem health. The randomized design ensures that the conclusions drawn are robust and reliable, providing a solid foundation for the development of policies and initiatives. As environmental concerns continue to mount, the role of reliable experimental designs like CRD in facilitating meaningful research and informed policy-making cannot be overstated.
A CRD experiment involves meticulous planning and execution, outlined in the following structured steps. Each phase, from the preparatory steps to data collection and analysis, plays a pivotal role in bolstering the integrity and success of the experiment, ensuring that the findings stand as a valuable contribution to scientific knowledge and understanding.
While the Completely Randomized Design offers numerous advantages, researchers often encounter specific challenges when implementing it in real-world experiments. Recognizing these challenges early and being prepared with strategies to address them can significantly improve the integrity and success of the CRD experiment. Let's delve into some of the most common challenges and explore potential solutions:
While CRD is a powerful tool in experimental research, its successful implementation hinges on the researcher's ability to anticipate, recognize, and navigate challenges that might arise. By being proactive and employing strategies to mitigate potential pitfalls, researchers can maximize the reliability and validity of their CRD experiments, ensuring meaningful and impactful results.
In summary, the Completely Randomized Design holds a pivotal place in the field of research owing to its simplicity and straightforward approach. Its essence lies in the unbiased random assignment of experimental units to various treatments, ensuring the reliability and validity of the results. Although it may not control for other variables and often requires larger sample sizes, its ease of implementation frequently outweighs these drawbacks, solidifying it as a preferred choice for researchers across many fields.
Looking ahead, the future of CRD remains bright. As research continues to evolve, we anticipate the integration of CRD with more sophisticated design techniques and advanced analytical tools. This synergy will likely enhance the efficiency and applicability of CRD in varied research contexts, perpetuating its legacy as a fundamental research design method. While other designs might offer more control and complexity, the fundamental simplicity of CRD will continue to hold significant value in the rapidly evolving research landscape.
Moving forward, it is imperative to champion continuous learning and exploration in the field of CRD. Engaging in educational opportunities, staying abreast of the latest research and advancements, and actively participating in pertinent discussions and forums can markedly enrich understanding and expertise in CRD. Embracing this ongoing learning journey will not only bolster individual research skills but also make a significant contribution to the broader scientific community, fueling innovation and discovery in numerous fields of study.
Header image by Alex Shuper .
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Randomization in an experiment refers to a random assignment of participants to the treatment in an experiment. OR, for instance we can say that randomization is assignment of treatment to the participants randomly.
For example : a teacher decides to take a viva in the class and randomly starts asking the students.
Here, all the participants have equal chance of getting into the experiment. Like with our example, every student has equal chance of getting a question asked by the teacher. Randomization helps you stand a chance against biases. It can be a case when you select a group using some category, there can be personal biases or accidental biases. But when the selection is random, you don’t get a chance to look into each participant and hence the groups are fairly divided.
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As mentioned earlier, randomization minimizes the biases. But apart from that it also provides various benefits when adopted as a selection method in experiments.
Randomization can be subject to errors when it comes to “randomly” selecting the participants. As for our example, the teacher surely said she will ask questions to random students, but it is possible that she might subconsciously target mischievous students. This means we think the selection is random, but most of the times it isn’t.
Hence, to avoid these unintended biases, there are three techniques that researchers use commonly:
In simple random sampling. The selection of the participants is completely luck and probability based. Every participant has an equal chance of getting into the sample.
This method is theoretically easy to understand and works best against a sample size of 100 or more. The main factor here is that every participants gets an equal chance of being included in a treatment, and this is why it is also called the method of chances.
Methods of simple random sampling:
Example : A teacher wants to know how good her class is in mathematics. So she will give each student a number and will draw numbers from a bunch of chits. This will include a randomly selected sample size and It won’t have any biases depending on teachers interference.
It is a method of randomly assigning participants to the treatment groups. A block is a group is randomly ordered treatment group. All the blocks have a fair balance of treatment assignment throughout.
Example : A teacher wants to enroll student in two treatments A and B. and she plans to enroll 6 students per week. The blocks would look like this:
Week 1- AABABA
Week 2- BABAAB
Week 3- BBABAB
Each block has 9 A and 9 B. both treatments have been balanced even though their ordering is random.
There are two types of block assignment in permuted block randomization:
Generate a random number for each treatment that is assign in the block. In our example, the block “Week 1” would look like- A(4), A(5), B(56), A(33), B(40), A(10)
Then arrange these treatments according to their number is ascending order, the new treatment could be- AAABB
This includes listing the permutations for the block. Simply, writing down all possible variations.
The formula is b! / ((b/2)! (b/2)!)
For our example, the block sixe is 6, so the possible arrangements would be:
6! / ((6/2)! (6/2)!)
6! / (3)! x (3)!
6x5x4x3x2x1 / (3x2x1) x (3x2x1)
20 possible arrangements.
The word “strata” refers to characteristics. Every population has characteristics like gender, cast, age, background etc. Stratified random sampling helps you consider these stratum while sampling the population. The stratum can be pre-defined or you can define them yourself any way you think is best suitable for your study.
Example: you want to categorize population of a state depending on literacy. Your categories would be- (1) Literate (2) Intermediate (3) Illiterate.
Steps to conduct stratified random sampling:
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Research methods for multilevel interventions to reduce health disparities.
There is a growing recognition that successful preventive interventions need to address social determinants of health. Effectively reducing health disparities often requires the use of study designs that go beyond the individual level or traditional randomized controlled trial; however, studies that evaluate multilevel interventions face unique challenges and require specialized design and analytical approaches.
Researchers can use the information and resources below to learn more about appropriate research methods for evaluating complex multilevel interventions to reduce health disparities.
ODP sponsored a supplemental issue of Prevention Science in July 2024 that brings together new ideas and examples of strong applications of existing design and analytic methods for studies aimed at reducing health disparities. Papers also include strategies for developing multilevel interventions that balance methodological rigor with design feasibility, acceptability, and ethical considerations.
See the list below for the 12 open-access articles featured in the supplemental issue . Guest editors David M. Murray, Ph.D. (ODP), and Melody S. Goodman, Ph.D. (New York University), discuss key points of the papers and provide more background about the issue in their accompanying commentary.
You can also refer to the quick guide below that ODP developed to help you navigate the issue and identify the papers that are most relevant to your work.
Note: The links in this document take you to an external, non-federal website. ODP is not responsible for, nor can we endorse, external content. You will be subject to the external website’s privacy policy when you leave our site.
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An overview of randomization techniques: an unbiased assessment of outcome in clinical research.
Department of Biostatics, National Institute of Animal Nutrition & Physiology (NIANP), Adugodi, Bangalore, India
Randomization as a method of experimental control has been extensively used in human clinical trials and other biological experiments. It prevents the selection bias and insures against the accidental bias. It produces the comparable groups and eliminates the source of bias in treatment assignments. Finally, it permits the use of probability theory to express the likelihood of chance as a source for the difference of end outcome. This paper discusses the different methods of randomization and use of online statistical computing web programming ( www.graphpad.com /quickcalcs or www.randomization.com ) to generate the randomization schedule. Issues related to randomization are also discussed in this paper.
A good experiment or trial minimizes the variability of the evaluation and provides unbiased evaluation of the intervention by avoiding confounding from other factors, which are known and unknown. Randomization ensures that each patient has an equal chance of receiving any of the treatments under study, generate comparable intervention groups, which are alike in all the important aspects except for the intervention each groups receives. It also provides a basis for the statistical methods used in analyzing the data. The basic benefits of randomization are as follows: it eliminates the selection bias, balances the groups with respect to many known and unknown confounding or prognostic variables, and forms the basis for statistical tests, a basis for an assumption of free statistical test of the equality of treatments. In general, a randomized experiment is an essential tool for testing the efficacy of the treatment.
In practice, randomization requires generating randomization schedules, which should be reproducible. Generation of a randomization schedule usually includes obtaining the random numbers and assigning random numbers to each subject or treatment conditions. Random numbers can be generated by computers or can come from random number tables found in the most statistical text books. For simple experiments with small number of subjects, randomization can be performed easily by assigning the random numbers from random number tables to the treatment conditions. However, in the large sample size situation or if restricted randomization or stratified randomization to be performed for an experiment or if an unbalanced allocation ratio will be used, it is better to use the computer programming to do the randomization such as SAS, R environment etc.[ 1 – 6 ]
Researchers in life science research demand randomization for several reasons. First, subjects in various groups should not differ in any systematic way. In a clinical research, if treatment groups are systematically different, research results will be biased. Suppose that subjects are assigned to control and treatment groups in a study examining the efficacy of a surgical intervention. If a greater proportion of older subjects are assigned to the treatment group, then the outcome of the surgical intervention may be influenced by this imbalance. The effects of the treatment would be indistinguishable from the influence of the imbalance of covariates, thereby requiring the researcher to control for the covariates in the analysis to obtain an unbiased result.[ 7 , 8 ]
Second, proper randomization ensures no a priori knowledge of group assignment (i.e., allocation concealment). That is, researchers, subject or patients or participants, and others should not know to which group the subject will be assigned. Knowledge of group assignment creates a layer of potential selection bias that may taint the data.[ 9 ] Schul and Grimes stated that trials with inadequate or unclear randomization tended to overestimate treatment effects up to 40% compared with those that used proper randomization. The outcome of the research can be negatively influenced by this inadequate randomization.
Statistical techniques such as analysis of covariance (ANCOVA), multivariate ANCOVA, or both, are often used to adjust for covariate imbalance in the analysis stage of the clinical research. However, the interpretation of this post adjustment approach is often difficult because imbalance of covariates frequently leads to unanticipated interaction effects, such as unequal slopes among subgroups of covariates.[ 1 ] One of the critical assumptions in ANCOVA is that the slopes of regression lines are the same for each group of covariates. The adjustment needed for each covariate group may vary, which is problematic because ANCOVA uses the average slope across the groups to adjust the outcome variable. Thus, the ideal way of balancing covariates among groups is to apply sound randomization in the design stage of a clinical research (before the adjustment procedure) instead of post data collection. In such instances, random assignment is necessary and guarantees validity for statistical tests of significance that are used to compare treatments.
Many procedures have been proposed for the random assignment of participants to treatment groups in clinical trials. In this article, common randomization techniques, including simple randomization, block randomization, stratified randomization, and covariate adaptive randomization, are reviewed. Each method is described along with its advantages and disadvantages. It is very important to select a method that will produce interpretable and valid results for your study. Use of online software to generate randomization code using block randomization procedure will be presented.
Randomization based on a single sequence of random assignments is known as simple randomization.[ 3 ] This technique maintains complete randomness of the assignment of a subject to a particular group. The most common and basic method of simple randomization is flipping a coin. For example, with two treatment groups (control versus treatment), the side of the coin (i.e., heads - control, tails - treatment) determines the assignment of each subject. Other methods include using a shuffled deck of cards (e.g., even - control, odd - treatment) or throwing a dice (e.g., below and equal to 3 - control, over 3 - treatment). A random number table found in a statistics book or computer-generated random numbers can also be used for simple randomization of subjects.
This randomization approach is simple and easy to implement in a clinical research. In large clinical research, simple randomization can be trusted to generate similar numbers of subjects among groups. However, randomization results could be problematic in relatively small sample size clinical research, resulting in an unequal number of participants among groups.
The block randomization method is designed to randomize subjects into groups that result in equal sample sizes. This method is used to ensure a balance in sample size across groups over time. Blocks are small and balanced with predetermined group assignments, which keeps the numbers of subjects in each group similar at all times.[ 1 , 2 ] The block size is determined by the researcher and should be a multiple of the number of groups (i.e., with two treatment groups, block size of either 4, 6, or 8). Blocks are best used in smaller increments as researchers can more easily control balance.[ 10 ]
After block size has been determined, all possible balanced combinations of assignment within the block (i.e., equal number for all groups within the block) must be calculated. Blocks are then randomly chosen to determine the patients’ assignment into the groups.
Although balance in sample size may be achieved with this method, groups may be generated that are rarely comparable in terms of certain covariates. For example, one group may have more participants with secondary diseases (e.g., diabetes, multiple sclerosis, cancer, hypertension, etc.) that could confound the data and may negatively influence the results of the clinical trial.[ 11 ] Pocock and Simon stressed the importance of controlling for these covariates because of serious consequences to the interpretation of the results. Such an imbalance could introduce bias in the statistical analysis and reduce the power of the study. Hence, sample size and covariates must be balanced in clinical research.
The stratified randomization method addresses the need to control and balance the influence of covariates. This method can be used to achieve balance among groups in terms of subjects’ baseline characteristics (covariates). Specific covariates must be identified by the researcher who understands the potential influence each covariate has on the dependent variable. Stratified randomization is achieved by generating a separate block for each combination of covariates, and subjects are assigned to the appropriate block of covariates. After all subjects have been identified and assigned into blocks, simple randomization is performed within each block to assign subjects to one of the groups.
The stratified randomization method controls for the possible influence of covariates that would jeopardize the conclusions of the clinical research. For example, a clinical research of different rehabilitation techniques after a surgical procedure will have a number of covariates. It is well known that the age of the subject affects the rate of prognosis. Thus, age could be a confounding variable and influence the outcome of the clinical research. Stratified randomization can balance the control and treatment groups for age or other identified covariates. Although stratified randomization is a relatively simple and useful technique, especially for smaller clinical trials, it becomes complicated to implement if many covariates must be controlled.[ 12 ] Stratified randomization has another limitation; it works only when all subjects have been identified before group assignment. However, this method is rarely applicable because clinical research subjects are often enrolled one at a time on a continuous basis. When baseline characteristics of all subjects are not available before assignment, using stratified randomization is difficult.[ 10 ]
One potential problem with small to moderate size clinical research is that simple randomization (with or without taking stratification of prognostic variables into account) may result in imbalance of important covariates among treatment groups. Imbalance of covariates is important because of its potential to influence the interpretation of a research results. Covariate adaptive randomization has been recommended by many researchers as a valid alternative randomization method for clinical research.[ 8 , 13 ] In covariate adaptive randomization, a new participant is sequentially assigned to a particular treatment group by taking into account the specific covariates and previous assignments of participants.[ 7 ] Covariate adaptive randomization uses the method of minimization by assessing the imbalance of sample size among several covariates.
Using the online randomization http://www.graphpad.com/quickcalcs/index.cfm , researcher can generate randomization plan for treatment assignment to patients. This online software is very simple and easy to implement. Up to 10 treatments can be allocated to patients and the replication of treatment can also be performed up to 9 times. The major limitations of this software is that once the randomization plan is generated, same randomization plan cannot be generated as this uses the seed point of local computer clock and is not displayed for further use. Other limitation of this online software Maximum of only 10 treatments can be assigned to patients. Entering the web address http://www.graphpad.com/quickcalcs/index.cfm on address bar of any browser, the page of graphpad appears with number of options. Select the option of “Random Numbers” and then press continue, Random Number Calculator with three options appears. Select the tab “Randomly assign subjects to groups” and press continue. In the next page, enter the number of subjects in each group in the tab “Assign” and select the number of groups from the tab “Subjects to each group” and keep number 1 in repeat tab if there is no replication in the study. For example, the total number of patients in a three group experimental study is 30 and each group will assigned to 10 patients. Type 10 in the “Assign” tab and select 3 in the tab “Subjects to each group” and then press “do it” button. The results is obtained as shown as below (partial output is presented)
Another randomization online software, which can be used to generate randomization plan is http://www.randomization.com . The seed for the random number generator[ 14 , 15 ] (Wichmann and Hill, 1982, as modified by McLeod, 1985) is obtained from the clock of the local computer and is printed at the bottom of the randomization plan. If a seed is included in the request, it overrides the value obtained from the clock and can be used to reproduce or verify a particular plan. Up to 20 treatments can be specified. The randomization plan is not affected by the order in which the treatments are entered or the particular boxes left blank if not all are needed. The program begins by sorting treatment names internally. The sorting is case sensitive, however, so the same capitalization should be used when recreating an earlier plan. Example of 10 patients allocating to two groups (each with 5 patients), first the enter the treatment labels in the boxes, and enter the total number of patients that is 10 in the tab “Number of subjects per block” and enter the 1 in the tab “Number of blocks” for simple randomization or more than one for Block randomization. The output of this online software is presented as follows.
The benefits of randomization are numerous. It ensures against the accidental bias in the experiment and produces comparable groups in all the respect except the intervention each group received. The purpose of this paper is to introduce the randomization, including concept and significance and to review several randomization techniques to guide the researchers and practitioners to better design their randomized clinical trials. Use of online randomization was effectively demonstrated in this article for benefit of researchers. Simple randomization works well for the large clinical trails ( n >100) and for small to moderate clinical trials ( n <100) without covariates, use of block randomization helps to achieve the balance. For small to moderate size clinical trials with several prognostic factors or covariates, the adaptive randomization method could be more useful in providing a means to achieve treatment balance.
Source of Support: Nil
Conflict of Interest: None declared.
Coeliac disease and postpartum depression: are they linked a two-sample mendelian randomization study.
Background: To explore the potential causal associations between coeliac disease(CD) and postpartum depression(PPD) by using two-sample Mendelian randomization(MR) analysis.
Methods: The IEU OPEN GWAS project was utilized to identify genetic loci strongly associated with CD as instrumental variables (IVs), and MR analysis was performed using inverse variance weighting(IVW), weighted median, weighted model, and MR-Egger. MR analyses were used to examine whether there was a link between CD and PPD, with an OR and 95% CI. Meanwhile, the relationship between CD and depression(DP) was analyzed using MR. The sensitivity analysis was conducted using MR-Egger intercept analysis, Cochran’s Q test, and leave-one-out analysis.
Results: From the GWAS online database, 13 single-nucleotide polymorphisms (SNPs) were chosen as IVs. The IVW results showed a relationship between PPD and a genetically predicted risk of developing CD (OR = 1.022, 95% CI: 1.001–1.044, P = 0.043). However, the presence of DP was not linked with CD (OR=0.991, 95% CI: 0.978–1.003, P=0.151). Potential horizontal pleiotropy was not discovered using MR-Egger intercept analysis (PPD: P=0.725; DP: P=0.785), and Cochran’s Q test for heterogeneity revealed no significant heterogeneity (PPD: P=0.486; DP: P=0.909). A leave-one-out analysis found that individual SNPs had minimal effect on overall causal estimations.
Conclusion: MR research discovered a link between CD and PPD.
Postpartum depression (PPD) is a frequent puerperal mental illness in which women have major depressive symptoms or characteristic depressive episodes throughout the puerperium ( 1 ). It is a frequent puerperal mental condition characterized by a persistent and profoundly depressed mood throughout the puerperium, as well as a variety of symptoms such as despair, sorrow, irritability, and even suicidal ideation. These symptoms significantly impair the mother’s capacity to care for her unborn child ( 2 ). The aetiology and pathophysiology of PPD remain unknown, and the illness has a worldwide prevalence rate ranging from 7% to 9% ( 3 ). It is thought to be closely linked to genetic, neurobiochemical, and psychosocial factors, in addition to maternal helplessness, bad mood, lower energy, and other negative mental characteristics. A recent large-scale clinical study involving over 1 million women from 138 countries discovered that PPD symptoms were most commonly self-reported by women between the ages of 18 and 24, and that women were twice as likely as men to experience depression during childbirth. PPD prevalence decreases with age, reaching a low of 6.5% among persons aged 35 to 39. First-time mothers are more likely to develop comorbid depression than women who have previously given birth. Twin births had a greater symptom load than single births, with 11.3 percent of twin mothers experiencing depressive symptoms compared to 8.3 percent of single-birth mothers. This disparity is especially pronounced in women over the age of 40 ( 4 ). A separate study found that PPD can last up to three years, which is far longer than previously thought ( 5 ). PPD has a long duration and a significant impact, hurting relationships, families, society, and maternal health and placing mothers and newborns at risk. Identifying people at high risk of having PPD, changing behaviors, and developing preventative measures to minimize and eliminate risk factors for PPD are all effective approaches to reducing the condition’s prevalence.
Coeliac disease (CD), also known as gluten-sensitive enteropathy, is a common immune-mediated inflammatory illness of the small intestine characterized by the body’s sensitivity to dietary gluten and associated proteins in genetically susceptible individuals ( 6 ). According to epidemiological research, CD affects around 1% of the global population ( 7 ). CD is widespread in most countries at a rate of 1:300–1:70, according to an epidemiological study based on serological testing and biopsy confirmation ( 8 ). Another meta-analysis discovered an even higher worldwide frequency of 1.4% based on serological tests and 0.7% based on biopsies ( 9 ). Every year, the frequency of CD rises, and it has been associated with a variety of conditions, including unexplained infertility in women, intrauterine growth restriction, and recurrent miscarriage ( 10 ). Recent research has found that PPD is frequent in CD women on gluten-free diet GFD, particularly in those with previous menstrual disorders. we suggest screening for PPD in CD for early detection and treatment of this condition ( 11 ). Less study has been conducted on the relationship between CD and PPD, and it is still unclear whether one exists.
Mendelian randomization (MR) is a data analysis technique for evaluating etiological inferences in epidemiological studies that is based on whole genome sequencing data and uses genetic variants with strong correlations to exposures as instrumental variables to assess causal associations between exposures and outcomes ( 12 ). It is effective at reducing prejudice. In the current study, MR was used to look into any possible causal links between CD and PPD.
We evaluated the probable causal relationship between CD phenotypes and the occurrence of PPD in this study by using the genetic pooled dataset from the genome-wide association study (GWAS) for a two-sample MR analysis with CD-related phenotypes as the exposure factor and the presence of PPD as the outcome. Meanwhile, in the risk factor analysis of PPD, we used depression (DP) as the outcome to investigate whether CD mediates the causal relationship with PPD via DP.
The exposure variable is celiac disease, and the outcome variables are postpartum depression and depression. All GWAS data are sourced from the IEU OPEN GWAS project ( https://gwas.mrcieu.ac.uk/ ). Celiac disease (ID: finn-b-K11_COELIAC) includes 16,380,438 SNPs, encompassing 1,973 cases and 210,964 controls. Postpartum depression (ID: finn-b-O15_POSTPART_DEPR) includes 16,376,275 SNPs, with 7,604 cases and 59,601 controls. Depression (ID: finn-b-F5_DEPRESSIO) includes 16,380,457 SNPs, with 23,424 cases and 192,220 controls. To ensure ethnic homogeneity, all participants in the sample are of European ancestry, as detailed in Table 1 .
Table 1 Summary of genome-wide association study data in this Mendelian randomization study.
The instrument variables (IVs) in this study were needed to meet the following standards ( 13 ): (i) There was no linkage disequilibrium (LD) between single-nucleotide polymorphisms (SNPs), with r2<0.001; (ii) There was no genome-wide significant connection between SNPs and the occurrence of PPD; and (iii) SNPs did not achieve genome-wide significant association with PPD-associated symptoms. The development of puerperal sorrow was not related to SNPs across the genome (P<5×10 -8 ) ( Figure 1 ). Finally, 13 SNPs associated with CD were included. The F-value was determined using the following method to determine the IVs’ strength in order to rule out any potential weak instrumental variable bias between IVs and exposure factors ( 14 ). F = (R 2 /1-R 2 )(n-k-1/K), R 2 = 2X(1-MAF)X(MAF)X2, where N is the exposed GWAS sample size, K is the number of single nucleotide polymorphisms, R2 is the percentage of variation explained by single nucleotide polymorphisms in the exposed database, MAF is the effect allele frequency, and is the allele effect value. When F > 10, weak instrumental variance bias is expected to be less frequent.
Figure 1 Hypotheses for Mendelian randomization design.
In our two-sample MR, we used CD as the exposure and PPD and DP as the outcomes. For all studies, the “TwoSampleMR” analytic technique was utilized. For all analyses in R (4.3.1), the “TwoSampleMR” package, version 0.5.7, was utilized. The outcome-related SNPs were removed using PhenoScanner V2. The outliers were removed using MR-PRESSO. To study the causal influence, inverse variance weighting (IVW), weighted median, weighted model, and MR-Egger techniques were utilized. Odds ratios (OR) and 95% confidence intervals (CI) were calculated to assess the probable causal relationship between CD and the chance of developing prenatal depression. Possibility of a causal relationship. The sensitivity analysis was conducted using MR-Egger intercept analysis, Cochran’s Q test, and leave-one-out analysis.
In this work, the PhenoScanner database was utilized to search for phenotypes in line with the screening criteria for instrumental factors, and a total of 13 SNPs that were closely connected to one another without chain disequilibrium were originally screened out from the CD exposure data set. There were no SNPs with PPD significance, and the F-value statistic for 13 SNPs was larger than 10, suggesting that there were no weak instrumental factors in the MR analysis, and the MR-PRESSO analysis did not disclose any outliers. Finally, 13 SNPs were used as IVs (P<5×10 -8 , r2<0.001, clumping distance = 10,000 kb) to assess the relationship between CD and PPD.
Genetically predicted CD and PPD were shown to have a positive and statistically significant causative relationship using the IVW technique in MR analysis (OR = 1.022, 95% CI: 1.001–1.044, P = 0.043). Three additional analysis techniques Although the three methods did not support a causal relationship between CD and PPD, the four methods of IVW, weighted median, weighted mode, and MR-Egger were consistent in the direction of the results. These methods were also used to confirm the robustness of the results. The four studies’ findings all pointed in the same direction (OR > 1). Additionally, scatter plots demonstrated the general consistency of the regression lines discovered for the genetic prediction of CD on the risk of PPD ( Figure 2A ). The lack of possible horizontal pleiotropy detected by MR-Egger intercept analysis (P = 0.725) suggests that IVs do not significantly alter outcomes through mechanisms other than exposure. No significant heterogeneity was found in the Cochran’s Q test for heterogeneity (P = 0.486) ( Table 2 ; Figure 3 ). No specific single nucleotide polymorphism had an impact on the overall causal estimate, according to a leave-one-out analysis ( Figure 4A ). The preceding sensitivity analysis demonstrates that the effect OR values derived from the IVW method are quite robust.
Figure 2 Scatter plot of Mendelian randomization effect size for causal associations. (A) Coeliac disease and postpartum depression; (B) Coeliac disease and depression.
Table 2 The effects of coeliac disease on the risk of postpartum depression and depression as determined by Mendelian randomization.
Figure 3 Forest plot of Mendelian randomization effect sizes for causal associations. PPD, Postpartum depression; DP, Depression; OR, Odds ratio; CI, Confidence interval.
Figure 4 Leave-one-out plot to assess if a single variant is driving the association. (A) Coeliac disease and postpartum depression; (B) Coeliac disease and depression.
Genetically predicted CD and DP may not be causally related, as the IVW method showed in MR investigations (OR=0.991, 95% CI: 0.978–1.003, P=0.151). The other three methods confirmed that there is no causal relationship between CD and DP. Similar scatter plots illustrated how the regression lines produced for the genetically predicted risk of CD and DP were generally consistent ( Figure 2B ). The MR-Egger intercept analysis failed to detect any potential horizontal pleiotropy (P = 0.785). No significant heterogeneity was found using the Cochran’s Q test (P = 0.909). The leave-one-out analysis failed to identify any particular SNPs that had an effect on the overall causal estimates ( Figure 4B ).
Identifying the etiology of PPD is important for its prevention, diagnosis, and treatment. This study looked into the connection between CD and PPD using MR analysis. The results of the MR investigation showed a clear causal link between CD and PPD.
The association between CD and PPD has not been made clear till now. According to certain studies, people with CD can experience neurological or psychological symptoms such as anxiety, sadness, headache, peripheral neuropathy, ataxia, and epilepsy ( 15 , 16 ). These studies had some drawbacks, including small sample numbers, retrospective data, the possibility of an inaccurate CD diagnosis, and referral bias from tertiary care facilities. Some of these investigations used the presence of anti-antimelanocortin antibodies to make the diagnosis rather than duodenal histological signs or more specific autoantibodies. Concerning the connection between CD, DP, and epilepsy, there is conflicting information. Due to the superior research design, we were confident in proving causality in addition to bias in the current study employing MR techniques. It was difficult for prior observational studies to prevent bias due to confounding risk factors. This was the first study to do an MR analysis between CD and PPD risk. According to the research, CD may have a significant causal link that increases the risk of PPD.
Out of the many recognized potential risk factors for PPD, a past history of depression in the perinatal or non-perinatal era has the greatest impact and the strongest correlation with PPD ( 17 , 18 ). Depression that was present at high levels during the pregnancy may persist during labor and delivery ( 19 ). More than half of women with histories of prenatal depression (AD) also had postpartum depression, according to a study ( 20 ). On the other hand, a review of the literature showed that postpartum depression affected more than a third (39%) of women with AD ( 21 ). In order to exclude the influence of depression as a risk factor on the result, the correlation between CD and depression was examined in this study utilizing MR analysis. The results of the MR investigation showed that CD and DP did not clearly share a causal relationship. It was also proven that CD and PPD are causally connected.
This study’s strength stems from the fact that it is the first to use data from a substantial population sample in a systematic genetic technique to look into the potential of a connection between CD and the prevalence of PPD. The stability of the data was also confirmed using a number of statistical approaches, such as inverse variance weighting, weighted median, maximum likelihood ratio, MR-Egger regression analysis, and the MR-PRESSO method. However, this study also has limitations, such as the inclusion of populations from Europe, which may reduce population stratification bias, but the reliability of extrapolation to other ethnic groups may be insufficient. Therefore, it is necessary to further study the relationship between CD and PPD in other ethnic groups.
Ultimately, the present study employed two-sample MR research to carefully assess the potential causal associations between CD and the incidence of PPD at the genetic level. The results indicated that since CD was positively correlated with the chance of developing PPD, prevention of CD could have a prophylactic impact on the development of PPD.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
XY: Writing – original draft. MC: Writing – original draft, Data curation, Software. JZ: Funding acquisition, Visualization, Writing – original draft, Writing – review & editing.
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by Natural Science Foundation of Liaoning Province (2022-MS-394).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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Keywords: Mendelian randomization, coeliac disease, postpartum depression, depression, SNPs
Citation: Yu X, Cheng M and Zheng J (2024) Coeliac disease and postpartum depression: are they linked? A two-sample Mendelian randomization study. Front. Psychiatry 15:1312117. doi: 10.3389/fpsyt.2024.1312117
Received: 13 December 2023; Accepted: 08 July 2024; Published: 19 July 2024.
Reviewed by:
Copyright © 2024 Yu, Cheng and Zheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jindan Zheng, [email protected]
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Simple randomisation is a fair way of ensuring that any differences that occur between the treatment groups arise completely by chance. But - and this is the first but of many here - simple randomisation can lead to unbalanced groups, that is, groups of unequal size. This is particularly true if the trial is only small.
Background. Various research designs can be used to acquire scientific medical evidence. The randomized controlled trial (RCT) has been recognized as the most credible research design for investigations of the clinical effectiveness of new medical interventions [1, 2].Evidence from RCTs is widely used as a basis for submissions of regulatory dossiers in request of marketing authorization for ...
Randomized controlled trial is widely accepted as the best design for evaluating the efficacy of a new treatment because of the advantages of randomization (random allocation). Randomization eliminates accidental bias, including selection bias, and provides a base for allowing the use of probability theory.
In performing randomization, it is important to consider the choice of methodology in the specific context of the trial/experiment. Sample size, population characteristics, longevity of treatment effect, number of treatment arms, and study design all factor into determining an applicable randomization schedule.
Various research designs can be used to acquire scientific medical evidence. The randomized controlled trial (RCT) has been recognized as the most credible research design for investigations of the clinical effectiveness of new medical interventions [1, 2].Evidence from RCTs is widely used as a basis for submissions of regulatory dossiers in request of marketing authorization for new drugs ...
Randomization is widely applied in various fields, especially in scientific research, statistical analysis, and resource allocation, to ensure fairness and validity in the outcomes. In various contexts, randomization may involve Generating Random Permutations: This is essential in various situations, such as shuffling cards. By randomly ...
Randomized controlled trials (RCTs) have traditionally been viewed as the gold standard of clinical trial design, residing at the top of the hierarchy of levels of evidence in clinical study; this is because the process of randomization can minimize differences in characteristics of the groups that may influence the outcome, thus providing the ...
In experimental research, random assignment is a way of placing participants from your sample into different treatment groups using randomization. With simple random assignment, ... In this research design, there's usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.
A completely randomized design vs a randomized block design. A between-subjects design vs a within-subjects design. Randomization. An experiment can be completely randomized or randomized within blocks (aka strata): In a completely randomized design, every subject is assigned to a treatment group at random.
How to design a randomised controlled trial ... or adaptive trial, your research question always returns to your PICO statement. Precision in defining a research question is a key skill; the ...
In the design of experiments, the simplest design for comparing treatments is the "completely randomized design". Some "restriction on randomization" can occur with blocking and experiments that have hard-to-change factors; additional restrictions on randomization can occur when a full randomization is infeasible or when it is desirable to ...
Simple randomisation is a fair way of ensuring that any differences that occur between the treatment groups arise completely by chance. But - and this is the first but of many here - simple ran-domisation can lead to unbalanced groups, that is, groups of unequal size. This is particularly true if the trial is only small.
The key to randomized experimental research design is in the random assignment of study subjects - for example, individual voters, precincts, media markets or some other group - into treatment or control groups. Randomization has a very specific meaning in this context. It does not refer to haphazard or casual choosing of some and not others.
Abstract. A randomized controlled trial is a prospective, comparative, quantitative study/experiment performed under controlled conditions with random allocation of interventions to comparison groups. The randomized controlled trial is the most rigorous and robust research method of determining whether a cause-effect relation exists between an ...
Usually, statistics textbooks introduce the core aspects of experimental design as the three key elements, the four principles and the design types, which run through the whole scientific research design and determine the overall success of the research. This article discusses the principle of randomization, which is one of the four principles ...
A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about: Your overall research objectives and approach. Whether you'll rely on primary research or secondary research. Your sampling methods or criteria for selecting subjects. Your data collection methods.
What Is Randomization? Randomization is the process of assigning participants to treatment and control groups, assuming that each participant has an equal chance of being assigned to any group. 12 Randomization has evolved into a fundamental aspect of scientific research methodology. Demands have increased for more randomized clinical trials in many areas of biomedical research, such as ...
Randomized controlled trials are the gold standard of clinical research for comparing therapies in well-defined groups of participants. 1 Randomization avoids confounding due to unmeasured variables or to treatment selection and enables a causal interpretation of the estimated treatment effect. It has long been recognized, however, that standard explanatory clinical trials are slow, costly ...
Completely randomized design. In the design of experiments, completely randomized designs are for studying the effects of one primary factor without the need to take other nuisance variables into account. This article describes completely randomized designs that have one primary factor. The experiment compares the values of a response variable ...
Permuted block randomization is a way to randomly allocate a participant to a treatment group, while keeping a balance across treatment groups. Each "block" has a specified number of randomly ordered treatment assignments. 3. Stratified Random Sampling. Stratified random sampling is useful when you can subdivide areas.
In principle, randomization should protect a project because, on average, these influences will be represented randomly for the two groups of individuals. This reasoning extends to unmeasured and unknown causal factors as well. This discussion was illustrated by random assignment of subjects to treatment groups.
Completely Randomized Design (CRD) is a research methodology in which experimental units are randomly assigned to treatments without any systematic bias. CRD gained prominence in the early 20th century, largely attributed to the pioneering work of statistician Ronald A. Fisher. His method addressed the inherent variability in experimental units by randomly assigning treatments, thus countering ...
Randomization in an experiment refers to a random assignment of participants to the treatment in an experiment. OR, for instance we can say that randomization is assignment of treatment to the participants randomly. For example: a teacher decides to take a viva in the class and randomly starts asking the students.
There is a growing recognition that successful preventive interventions need to address social determinants of health. Effectively reducing health disparities often requires the use of study designs that go beyond the individual level or traditional randomized controlled trial; however, studies that evaluate multilevel interventions face unique challenges and require specialized design and ...
A random number table found in a statistics book or computer-generated random numbers can also be used for simple randomization of subjects. This randomization approach is simple and easy to implement in a clinical research. In large clinical research, simple randomization can be trusted to generate similar numbers of subjects among groups.
Original Research. Dexmedetomidine for Reducing Mortality in Patients with Septic Shock: A Randomized Controlled Trial (DecatSepsis) ... Study Design and Methods. This open-label randomized controlled trial assessed the effects of a heart rate (HR)-calibrated DEX infusion on in-hospital mortality in patients with septic shock and HR >90 beats ...
1 Introduction. Postpartum depression (PPD) is a frequent puerperal mental illness in which women have major depressive symptoms or characteristic depressive episodes throughout the puerperium ().It is a frequent puerperal mental condition characterized by a persistent and profoundly depressed mood throughout the puerperium, as well as a variety of symptoms such as despair, sorrow ...
The recent replicability crisis in social and biomedical sciences has highlighted the need for improvement in the honest transmission of scientific content. We present the results of two studies investigating whether nudges and soft social incentives enhance participants' readiness to transmit high-quality scientific news. In two online randomized experiments (Total N = 2425), participants ...