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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Introduction to Test of Independence01:21

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In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
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Body:Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to...
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Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Independence estimators for re-randomisation trials in multi-episode settings: a simulation study.

Brennan C Kahan1,2, Ian R White3, Sandra Eldridge4

  • 1MRC Clinical Trials Unit at UCL, London, UK. b.kahan@ucl.ac.uk.

BMC Medical Research Methodology
|October 31, 2021
PubMed
Summary
This summary is machine-generated.

Independence estimators are unbiased for the per-episode added-benefit estimand in re-randomisation trials. Careful estimand selection ensures these trials address relevant clinical questions, making independence estimators a valuable default choice.

Keywords:
EstimandsIndependence estimatorsRe-randomisationRe-randomisation trialsSimulation study

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Epidemiology

Background:

  • Re-randomisation trials re-enroll patients for each treatment episode.
  • These trials assess intervention effects across all episodes of use.
  • Independence estimators are commonly used but under-researched in re-randomisation.

Purpose of the Study:

  • Evaluate independence estimators in re-randomisation trials.
  • Focus on continuous outcomes and independent treatment allocation.
  • Assess various treatment effect and non-enrolment mechanisms.

Main Methods:

  • Simulation study design.
  • Evaluated four independence estimators targeting different estimands (per-episode, per-patient, added-benefit, policy-benefit).
  • Considered scenarios with varying treatment effects and non-enrolment.

Main Results:

  • Independence estimators were unbiased for the per-episode added-benefit estimand across all scenarios.
  • Estimators for other estimands were unbiased unless differential non-enrolment occurred.
  • Robust standard errors provided near-nominal coverage when estimators were unbiased.

Conclusions:

  • Choosing the correct estimand is crucial for clinically relevant re-randomisation trial questions.
  • Independence estimators are a practical default choice for re-randomisation trials.
  • Further evaluation of alternative estimators is needed.