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Related Concept Videos

Stratified Sampling Method01:16

Stratified Sampling Method

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. 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.
To choose a stratified sample, divide the population into groups called strata and then take a...
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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Cluster Sampling Method01:20

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Related Experiment Video

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Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

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Applying optimal model selection in principal stratification for causal inference.

Lang'o Odondi1, Roseanne McNamee

  • 1School of Social and Community Medicine, University of Bristol, 39 Whatley Road, Bristol BS8 2PS, UK. Lango.Odondi@bristol.ac.uk

Statistics in Medicine
|October 9, 2012
PubMed
Summary
This summary is machine-generated.

Noncompliance in clinical trials can bias results. This study uses principal stratification to adjust for noncompliance in hormone replacement therapy trials, offering more accurate causal risk estimates.

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Last Updated: May 18, 2026

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06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Causal Inference

Background:

  • Noncompliance to treatment allocation complicates causal inference in clinical trials.
  • Intention-to-treat estimates can be biased due to noncompliance, even with homogeneous treatment effects.

Purpose of the Study:

  • To extend principal stratification methods for adjusting noncompliance in two-arm trials.
  • To develop model selection for covariates predicting compliance.
  • To analyze data from the Esprit study on hormone replacement therapy and cardiac events.

Main Methods:

  • Applied an extended principal stratification method.
  • Utilized model selection for compliance-predicting covariates.
  • Employed a Bayesian framework for analysis.
  • Adjusted for noncompliance in both treatment arms.

Main Results:

  • Principal stratification suggested compliance with hormone replacement therapy (HRT) alone reduced death risk by 47% and myocardial reinfarction by 25%.
  • Compliance with either treatment reduced death risk by 13% and reinfarction by 60% among the most compliant.
  • Results indicated sensitivity to the chosen sensitivity parameter.

Conclusions:

  • Principal stratification provides a robust method for adjusting noncompliance in clinical trials.
  • Accurate causal risk estimates require accounting for patient compliance.
  • The findings highlight the potential benefits of HRT in reducing cardiac events post-myocardial infarction, contingent on compliance.