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Related Concept Videos

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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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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
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Re-randomisation trials in multi-episode settings: Estimands and independence estimators.

Brennan C Kahan1,2, Ian R White2, Richard Hooper1

  • 1Pragmatic Clinical Trials Unit, 4617Queen Mary University of London, London, UK.

Statistical Methods in Medical Research
|April 15, 2022
PubMed
Summary

The re-randomisation design allows patients to be re-enrolled in clinical trials for multiple treatment episodes. This approach offers a useful method for analysing multi-episode studies, guiding appropriate analysis choices.

Keywords:
Estimandinformative cluster sizemulti-episode settingrandomised trialre-randomisation design

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Epidemiology

Background:

  • Patients often require repeated treatments, necessitating specialized trial designs.
  • Traditional trial designs may not adequately capture outcomes in multi-episode treatment settings.

Purpose of the Study:

  • To propose novel estimands for multi-episode clinical trials using re-randomisation.
  • To address unique challenges such as episode weighting and handling patient treatment history.

Main Methods:

  • Development of a set of estimands tailored for multi-episode settings.
  • Proposal of independence estimators for the defined estimands.
  • Analysis of existing re-randomisation trial methodologies.

Main Results:

  • The common analysis of comparing all intervention vs. control episodes corresponds to a per-episode added-benefit estimand.
  • The proposed independence estimator is generally unbiased.
  • Conditions for the unbiasedness of alternative estimators are described.

Conclusions:

  • Consideration of proposed estimands aids in selecting appropriate analysis methods for multi-episode trials.
  • The re-randomisation design combined with independence estimators provides a valuable framework for multi-episode settings.