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Related Concept Videos

Randomized Experiments01:13

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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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A complete procedure for testing a claim about a population proportion is provided here.
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Criteria for Causality: Bradford Hill Criteria - II01:28

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The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
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The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
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An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory or independent variable. The affected variable is called the response or dependent variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The...
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Practical inference for a complier average causal effect in cluster randomised trials with a binary outcome.

Tansy Edwards1, Jennifer Thompson1, Charles Opondo2

  • 1MRC International Statistics and Epidemiology Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.

Clinical Trials (London, England)
|October 16, 2025
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A bootstrapping approach accurately estimates intervention effects among compliers in cluster randomized trials, even with non-compliance. This method supports intention-to-treat analyses by providing unbiased efficacy estimates.

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adherencecluster randomised trialcompliancecomplier average causal effect

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Non-compliance in cluster randomized trials (CRTs) can lead to underestimated intervention efficacy when using intention-to-treat (ITT) analysis.
  • The complier average causal effect (CACE) offers an unbiased efficacy estimate but poses inferential challenges in CRTs.

Purpose of the Study:

  • To evaluate a pragmatic bootstrapping approach for calculating the CACE confidence interval in CRTs.
  • To assess the performance of this method under various non-compliance and outcome prevalence scenarios.

Main Methods:

  • Simulated cluster randomized trials with varying non-compliance rates (5%-40%) and correlated cluster-level outcomes.
  • Employed a bootstrapping approach accounting for clustering to estimate CACE and its 95% confidence interval (CI).
  • Evaluated scenarios with sufficient clusters for 80% and 90% power to detect an ITT odds ratio of 0.5.

Main Results:

  • The bootstrapping approach demonstrated negligible bias in CACE estimation across all non-compliance levels.
  • No under-coverage was observed for bootstrap confidence intervals.
  • Confidence intervals were appropriately wide for outcome prevalences of 20%-40% but too wide for less common outcomes.
  • Power loss compared to ITT analysis increased with decreasing outcome prevalence, especially below 20%.

Conclusions:

  • The proposed bootstrapping method is accessible and computationally efficient for estimating CACE in CRTs.
  • This approach effectively supports intention-to-treat analyses by providing robust efficacy estimates.
  • It offers a valuable tool for evaluating interventions in the presence of non-compliance in clustered study designs.