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Proceedings of the National Academy of... Sep 2018Although complete randomization ensures covariate balance on average, the chance of observing significant differences between treatment and control covariate...
Although complete randomization ensures covariate balance on average, the chance of observing significant differences between treatment and control covariate distributions increases with many covariates. Rerandomization discards randomizations that do not satisfy a predetermined covariate balance criterion, generally resulting in better covariate balance and more precise estimates of causal effects. Previous theory has derived finite sample theory for rerandomization under the assumptions of equal treatment group sizes, Gaussian covariate and outcome distributions, or additive causal effects, but not for the general sampling distribution of the difference-in-means estimator for the average causal effect. We develop asymptotic theory for rerandomization without these assumptions, which reveals a non-Gaussian asymptotic distribution for this estimator, specifically a linear combination of a Gaussian random variable and truncated Gaussian random variables. This distribution follows because rerandomization affects only the projection of potential outcomes onto the covariate space but does not affect the corresponding orthogonal residuals. We demonstrate that, compared with complete randomization, rerandomization reduces the asymptotic quantile ranges of the difference-in-means estimator. Moreover, our work constructs accurate large-sample confidence intervals for the average causal effect.
Topics: Models, Theoretical; Random Allocation
PubMed: 30150408
DOI: 10.1073/pnas.1808191115 -
BMJ (Clinical Research Ed.) Jun 2006Restrictions during randomisation make it easier for investigators to guess the next allocation. Statistical correction of any imbalance in confounders at the end of the... (Review)
Review
Restrictions during randomisation make it easier for investigators to guess the next allocation. Statistical correction of any imbalance in confounders at the end of the study is equally accurate for most trials and would be safer
Topics: Confounding Factors, Epidemiologic; Random Allocation; Randomized Controlled Trials as Topic
PubMed: 16793819
DOI: 10.1136/bmj.332.7556.1506 -
Fertility and Sterility Nov 2020
Topics: Fertilization in Vitro; Humans; Life Style; Mentoring; Random Allocation; Telemedicine
PubMed: 33032815
DOI: 10.1016/j.fertnstert.2020.09.029 -
Henry Ford Hospital Medical Journal 1983
Clinical Trial Randomized Controlled Trial
Topics: Adolescent; Adult; Clinical Trials as Topic; Diabetes Mellitus; Humans; Prospective Studies; Random Allocation
PubMed: 6629834
DOI: No ID Found -
Computational and Mathematical Methods... 2014Minimization is a case allocation method for randomized controlled trials (RCT). Evidence suggests that the minimization method achieves balanced groups with respect to...
BACKGROUND
Minimization is a case allocation method for randomized controlled trials (RCT). Evidence suggests that the minimization method achieves balanced groups with respect to numbers and participant characteristics, and can incorporate more prognostic factors compared to other randomization methods. Although several automatic allocation systems exist (e.g., randoWeb, and MagMin), the minimization method is still difficult to implement, and RCTs seldom employ minimization. Therefore, we developed the minimization allocation controlled trials (MACT) system, a generic manageable minimization allocation system. SYSTEM OUTLINE: The MACT system implements minimization allocation by Web and email. It has a unified interface that manages trials, participants, and allocation. It simultaneously supports multitrials, multicenters, multigrouping, multiple prognostic factors, and multilevels.
METHODS
Unlike previous systems, MACT utilizes an optimized database that greatly improves manageability. SIMULATIONS AND RESULTS: MACT was assessed in a series of experiments and evaluations. Relative to simple randomization, minimization produces better balance among groups and similar unpredictability.
APPLICATIONS
MACT has been employed in two RCTs that lasted three years. During this period, MACT steadily and simultaneously satisfied the requirements of the trial.
CONCLUSIONS
MACT is a manageable, easy-to-use case allocation system. Its outstanding features are attracting more RCTs to use the minimization allocation method.
Topics: Algorithms; Computer Simulation; Databases, Factual; Humans; Random Allocation; Randomized Controlled Trials as Topic; Research Design; Software
PubMed: 24701251
DOI: 10.1155/2014/645064 -
Health Services Research Feb 2000
Topics: Bayes Theorem; Bias; Data Interpretation, Statistical; Health Services Research; Humans; Random Allocation; Reproducibility of Results; Research Design; Sample Size
PubMed: 10654829
DOI: No ID Found -
Clinical Trials (London, England) Jun 2023An ongoing cluster-randomized trial for the prevention of arboviral diseases utilizes covariate-constrained randomization to balance two treatment arms across four...
BACKGROUND
An ongoing cluster-randomized trial for the prevention of arboviral diseases utilizes covariate-constrained randomization to balance two treatment arms across four specified covariates and geographic sector. Each cluster is within a census tract of the city of Mérida, Mexico, and there were 133 eligible tracts from which to select 50. As some selected clusters may have been subsequently found unsuitable in the field, we desired a strategy to substitute new clusters while maintaining covariate balance.
METHODS
We developed an algorithm that successfully identified a subset of clusters that maximized the average minimum pairwise distance between clusters in order to reduce contamination and balanced the specified covariates both before and after substitutions were made.
SIMULATIONS
Simulations were performed to explore some limitations of this algorithm. The number of selected clusters and eligible clusters were varied along with the method of selecting the final allocation pattern.
CONCLUSION
The algorithm is presented here as a series of optional steps that can be added to the standard covariate-constrained randomization process in order to achieve spatial dispersion, cluster subsampling, and cluster substitution. Simulation results indicate that these extensions can be used without loss of statistical validity, given a sufficient number of clusters included in the trial.
Topics: Humans; Cluster Analysis; Random Allocation; Research Design; Computer Simulation; Algorithms
PubMed: 36932663
DOI: 10.1177/17407745231160556 -
PloS One 2020Umbrella trials have been suggested to increase trial conduct efficiency when investigating different biomarker-driven experimental therapies. An overarching platform is...
Umbrella trials have been suggested to increase trial conduct efficiency when investigating different biomarker-driven experimental therapies. An overarching platform is used for patient screening and subsequent subtrial allocation according to patients' biomarker status. Two subtrial allocation schemes for patients with a positive test result for multiple biomarkers are (i) the pragmatic allocation to the eligible subtrial with the currently fewest included patients and (ii) the random allocation to one of the eligible subtrials. Obviously, the subtrials compete for such patients which are consequently underrepresented in the subtrials. To address questions of the impact of an umbrella design in general as well as with respect to subtrial allocation and analysis method, we investigate an umbrella trial with two parallel group subtrials and discuss generalisations. First, we analytically quantify the impact of the umbrella design with random allocation on the number of patients needed to be screened, the biomarker status distribution and treatment effect estimates compared to the corresponding gold standard of an independent parallel group design. Using simulations and real data, we subsequently compare both allocation schemes and investigate weighted linear regression modelling as possible analysis method for the umbrella design. Our results show that umbrella designs are more efficient than the gold standard. However, depending on the biomarker status distribution in the disease population, an umbrella design can introduce differences in estimated treatment effects in the presence of an interaction between treatment and biomarker status. In principle, weighted linear regression together with the random allocation scheme can address this difference though it is difficult to assess if such an approach is applicable in practice. In any case, caution is required when using treatment effect estimates derived from umbrella designs for e.g. future trial planning or meta-analyses.
Topics: Biomarkers; Computer Simulation; Humans; Random Allocation; Research Design
PubMed: 32797088
DOI: 10.1371/journal.pone.0237441 -
Journal of the Royal Society of Medicine Apr 2018
Review
Topics: Humans; Random Allocation; Randomized Controlled Trials as Topic; Selection Bias
PubMed: 29648508
DOI: 10.1177/0141076818764320 -
Interactive Cardiovascular and Thoracic... Sep 2018Heterogeneity in meta-analysis describes differences in treatment effects between trials that exceed those we may expect through chance alone. Accounting for...
Heterogeneity in meta-analysis describes differences in treatment effects between trials that exceed those we may expect through chance alone. Accounting for heterogeneity drives different statistical methods for summarizing data and, if heterogeneity is anticipated, a random-effects model will be preferred to the fixed-effects model. Random-effects models assume that there may be different underlying true effects estimated in each trial which are distributed about an overall mean. The confidence intervals (CIs) around the mean include both within-study and between-study components of variance (uncertainty). Summary effects provide an estimation of the average treatment effect, and the CI depicts the uncertainty around this estimate. There are 5 statistics that are computed to identify and quantify heterogeneity. They have different meaning and give complementary information: Q statistic and its P-value simply test whether effect sizes depart from homogeneity, T2 and T quantify the amount of heterogeneity, and I2 expresses the proportion of dispersion due to heterogeneity. The point estimate and CIs for random-effects models describe the practical implications of the observed heterogeneity and may usefully be contrasted with the fixed-effects estimates.
Topics: Humans; Meta-Analysis as Topic; Models, Statistical; Random Allocation; Statistics as Topic
PubMed: 29868857
DOI: 10.1093/icvts/ivy163