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Showing 1 to 4 of 4 for “"Cluster randomized trials"”.

  1. Ethical Challenges in Cluster Randomized Trials

    … Consent in CRTs is associated with smaller cluster sizes, and the use of individual-level experimental and data collection interventions. CRT researchers are most concerned with issues around informed consent, and less concerned with issues related to the analysis of harms and benefits in …

    uwo Repository record for Ethical Challenges in Cluster Randomized Trials (opens in a new tab)

  2. Marginal Methods for the Design and Analysis of Cluster Randomized Trials and Related Studies

    <p>Cluster randomized trials (CRTs) are used to study the effectiveness of complex or community-level interventions across a diverse range of contexts. These contexts present a range of logistical and statistical challenges to the design and analysis of CRTs and related studies, such as …

    duke Repository record for Marginal Methods for the Design and Analysis of Cluster Randomized Trials and Related Studies (opens in a new tab)

  3. Power and Sample Size for Three-Level Cluster Designs

    Over the past few decades, Cluster Randomized Trials (CRT) have become a design of choice in many research areas. One of the most critical issues in planning a CRT is to ensure that the study design is sensitive enough to capture the intervention effect. The assessment of power and sample size in …

    vcu Repository record for Power and Sample Size for Three-Level Cluster Designs (opens in a new tab)

  4. Causal inference for complex data: randomization inference for treatment effect heterogeneity, network outcomes, and subgroup specific effects

    … presents three new methodologies for analyzing randomized controlled trials using the researcher controlled randomization mechanism as the basis for inference. The first method extends inference for the ``attributable effect'', the total of the difference of outcomes if the treatment group had …

    uiuc Repository record for Causal inference for complex data: randomization inference for treatment effect heterogeneity, network outcomes, and subgroup specific effects (opens in a new tab)