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Korean Journal of Anesthesiology Jun 2019Randomized controlled trial is widely accepted as the best design for evaluating the efficacy of a new treatment because of the advantages of randomization (random... (Review)
Review
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. Despite its importance, randomization has not been properly understood. This article introduces the different randomization methods with examples: simple randomization; block randomization; adaptive randomization, including minimization; and response-adaptive randomization. Ethics related to randomization are also discussed. The study is helpful in understanding the basic concepts of randomization and how to use R software.
Topics: Bias; Humans; Random Allocation; Randomized Controlled Trials as Topic; Research Design; Selection Bias
PubMed: 30929415
DOI: 10.4097/kja.19049 -
Journal of Clinical Epidemiology Jan 1999Trialists argue about the usefulness of stratified randomization. For investigators designing trials and readers who use them, the argument has created uncertainty... (Review)
Review
Trialists argue about the usefulness of stratified randomization. For investigators designing trials and readers who use them, the argument has created uncertainty regarding the importance of stratification. In this paper, we review stratified randomization to summarize its purpose, indications, accomplishments, and alternatives. In order to identify research papers, we performed a Medline search for 1966-1997. The search yielded 33 articles that included original research on stratification or included stratification as the major focus. Additional resources included textbooks. Stratified randomization prevents imbalance between treatment groups for known factors that influence prognosis or treatment responsiveness. As a result, stratification may prevent type I error and improve power for small trials (<400 patients), but only when the stratification factors have a large effect on prognosis. Stratification has an important effect on sample size for active control equivalence trials, but not for superiority trials. Theoretical benefits include facilitation of subgroup analysis and interim analysis. The maximum desirable number of strata is unknown, but experts argue for keeping it small. Stratified randomization is important only for small trials in which treatment outcome may be affected by known clinical factors that have a large effect on prognosis, large trials when interim analyses are planned with small numbers of patients, and trials designed to show the equivalence of two therapies. Once the decision to stratify is made, investigators need to chose factors carefully and account for them in the analysis.
Topics: Bias; Data Interpretation, Statistical; Effect Modifier, Epidemiologic; Guidelines as Topic; Humans; Prognosis; Random Allocation; Randomized Controlled Trials as Topic; Reproducibility of Results; Research Design; Treatment Outcome
PubMed: 9973070
DOI: 10.1016/s0895-4356(98)00138-3 -
International Journal of Environmental... Jan 2011When planning a randomized clinical trial, careful consideration must be given to how participants are selected for various arms of a study. Selection and accidental...
When planning a randomized clinical trial, careful consideration must be given to how participants are selected for various arms of a study. Selection and accidental bias may occur when participants are not assigned to study groups with equal probability. A simple random allocation scheme is a process by which each participant has equal likelihood of being assigned to treatment versus referent groups. However, by chance an unequal number of individuals may be assigned to each arm of the study and thus decrease the power to detect statistically significant differences between groups. Block randomization is a commonly used technique in clinical trial design to reduce bias and achieve balance in the allocation of participants to treatment arms, especially when the sample size is small. This method increases the probability that each arm will contain an equal number of individuals by sequencing participant assignments by block. Yet still, the allocation process may be predictable, for example, when the investigator is not blind and the block size is fixed. This paper provides an overview of blocked randomization and illustrates how to avoid selection bias by using random block sizes.
Topics: Bias; Humans; Random Allocation; Randomized Controlled Trials as Topic; Sample Size; Selection Bias
PubMed: 21318011
DOI: 10.3390/ijerph8010015 -
International Journal of Epidemiology Jun 2014Sample size calculations are an important tool for planning epidemiological studies. Large sample sizes are often required in Mendelian randomization investigations.
BACKGROUND
Sample size calculations are an important tool for planning epidemiological studies. Large sample sizes are often required in Mendelian randomization investigations.
METHODS AND RESULTS
Resources are provided for investigators to perform sample size and power calculations for Mendelian randomization with a binary outcome. We initially provide formulae for the continuous outcome case, and then analogous formulae for the binary outcome case. The formulae are valid for a single instrumental variable, which may be a single genetic variant or an allele score comprising multiple variants. Graphs are provided to give the required sample size for 80% power for given values of the causal effect of the risk factor on the outcome and of the squared correlation between the risk factor and instrumental variable. R code and an online calculator tool are made available for calculating the sample size needed for a chosen power level given these parameters, as well as the power given the chosen sample size and these parameters.
CONCLUSIONS
The sample size required for a given power of Mendelian randomization investigation depends greatly on the proportion of variance in the risk factor explained by the instrumental variable. The inclusion of multiple variants into an allele score to explain more of the variance in the risk factor will improve power, however care must be taken not to introduce bias by the inclusion of invalid variants.
Topics: Humans; Mendelian Randomization Analysis; Monte Carlo Method; Random Allocation; Risk Factors; Sample Size
PubMed: 24608958
DOI: 10.1093/ije/dyu005 -
Kidney International Feb 2008As confounding obscures the 'real' effect of an exposure on outcome, investigators performing etiological studies do their utmost best to prevent or control confounding....
As confounding obscures the 'real' effect of an exposure on outcome, investigators performing etiological studies do their utmost best to prevent or control confounding. Unfortunately, in this process, errors are frequently made. This paper explains that to be a potential confounder, a variable needs to satisfy all three of the following criteria: (1) it must have an association with the disease, that is, it should be a risk factor for the disease; (2) it must be associated with the exposure, that is, it must be unequally distributed between exposure groups; and (3) it must not be an effect of the exposure; this also means that it may not be part of the causal pathway. In addition, a number of different techniques are described that may be applied to prevent or control for confounding: randomization, restriction, matching, and stratification. Finally, a number of examples outline commonly made errors, most of which result from 'overadjustment' for variables that do not satisfy the criteria for potential confounders. Such an example of an error frequently occurring in the literature is the incorrect adjustment for blood pressure while studying the relationship between body mass index and the development of end-stage renal disease. Such errors will introduce new bias instead of preventing it.
Topics: Confounding Factors, Epidemiologic; Humans; Kidney Diseases; Random Allocation
PubMed: 17978811
DOI: 10.1038/sj.ki.5002650 -
Journal of the Society For Integrative... 2006Randomized trials are an important method for deciding whether integrative oncology therapies do more good than harm. Many investigators do not pay sufficient attention... (Review)
Review
Randomized trials are an important method for deciding whether integrative oncology therapies do more good than harm. Many investigators do not pay sufficient attention to randomization procedures, and several studies have shown that only a fraction of trial reports describe randomization adequately. The purpose of randomization is to prevent selection bias: randomization procedures must therefore ensure that researchers are unable to predict the group to which a patient will be randomized until the patient is unambiguously registered on study; moreover, researchers must be unable to change a patient's allocation after the patients are registered. The use of telephone randomization and opaque envelopes has been suggested as a good randomization method, but both can be subverted. Randomization should be conducted either by a pharmaceutical company, which sends blinded medication to the hospital pharmacy, or by a secure, password-protected database system. Computer randomization can easily incorporate extensions of randomization, such as blocking, stratification, and minimization, which can help ensure balance between groups.
Topics: Humans; Medical Oncology; Patient Selection; Random Allocation; Randomized Controlled Trials as Topic; Research Design; Selection Bias
PubMed: 17022927
DOI: 10.2310/7200.2006.023 -
Family Medicine Feb 2007Randomization in randomized controlled trials involves more than generation of a random sequence by which to assign subjects. For randomization to be successfully...
Randomization in randomized controlled trials involves more than generation of a random sequence by which to assign subjects. For randomization to be successfully implemented, the randomization sequence must be adequately protected (concealed) so that investigators, involved health care providers, and subjects are not aware of the upcoming assignment. The absence of adequate allocation concealment can lead to selection bias, one of the very problems that randomization was supposed to eliminate. Authors of reports of randomized trials should provide enough details on how allocation concealment was achieved so the reader can determine the likelihood of success. Fortunately, a plan of allocation concealment can always be incorporated into the design of a randomized trial. Certain methods minimize the risk of concealment failing more than others. Keeping knowledge of subjects' assignment after allocation from subjects, investigators/health care providers, or those assessing outcomes is referred to as masking (also known as blinding). The goal of masking is to prevent ascertainment bias. In contrast to allocation concealment, masking cannot always be incorporated into a randomized controlled trial. Both allocation concealment and masking add to the elimination of bias in randomized controlled trials.
Topics: Humans; Random Allocation; Randomized Controlled Trials as Topic; Selection Bias; United States
PubMed: 17273956
DOI: No ID Found -
BMJ (Clinical Research Ed.) Jun 1991
Topics: Bias; Random Allocation; Randomized Controlled Trials as Topic
PubMed: 1855013
DOI: 10.1136/bmj.302.6791.1481 -
Circulation Research Jun 2016
Topics: Atherosclerosis; Endothelium, Vascular; Humans; Lipoproteins; Lipoproteins, HDL; Lipoproteins, LDL; Nitric Oxide; Random Allocation
PubMed: 27340266
DOI: 10.1161/CIRCRESAHA.116.309116 -
Statistics in Medicine May 2022A practical limitation of cluster randomized controlled trials (cRCTs) is that the number of available clusters may be small, resulting in an increased risk of baseline...
A practical limitation of cluster randomized controlled trials (cRCTs) is that the number of available clusters may be small, resulting in an increased risk of baseline imbalance under simple randomization. Constrained randomization overcomes this issue by restricting the allocation to a subset of randomization schemes where sufficient overall covariate balance across comparison arms is achieved. However, for multi-arm cRCTs, several design and analysis issues pertaining to constrained randomization have not been fully investigated. Motivated by an ongoing multi-arm cRCT, we elaborate the method of constrained randomization and provide a comprehensive evaluation of the statistical properties of model-based and randomization-based tests under both simple and constrained randomization designs in multi-arm cRCTs, with varying combinations of design and analysis-based covariate adjustment strategies. In particular, as randomization-based tests have not been extensively studied in multi-arm cRCTs, we additionally develop most-powerful randomization tests under the linear mixed model framework for our comparisons. Our results indicate that under constrained randomization, both model-based and randomization-based analyses could gain power while preserving nominal type I error rate, given proper analysis-based adjustment for the baseline covariates. Randomization-based analyses, however, are more robust against violations of distributional assumptions. The choice of balance metrics and candidate set sizes and their implications on the testing of the pairwise and global hypotheses are also discussed. Finally, we caution against the design and analysis of multi-arm cRCTs with an extremely small number of clusters, due to insufficient degrees of freedom and the tendency to obtain an overly restricted randomization space.
Topics: Cluster Analysis; Humans; Random Allocation; Randomized Controlled Trials as Topic; Research Design
PubMed: 35146788
DOI: 10.1002/sim.9333