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Related Experiment Videos

Stratified testing for treatment effects with missing data

W J Shih1, H Quan

  • 1Biostatistics and Research Data Systems, Rahway, New Jersey 07065, U.S.A. joe_shih@merck.com

Biometrics
|July 11, 1998
PubMed
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This study addresses issues with stratified testing for repeated measures data with missing values. We propose a less stringent condition for maintaining test accuracy and offer solutions when initial conditions aren't met.

Area of Science:

  • Biostatistics
  • Statistical Methods

Background:

  • Repeated measures data frequently contain missing observations, complicating statistical analysis.
  • Dawson and Lagakos (1993) proposed a stratified test for such data, recommending stratification based on missing data patterns.
  • They identified sufficient conditions to ensure the test's size (Type I error rate) is properly controlled.

Purpose of the Study:

  • To identify practical problems with the sufficient conditions proposed by Dawson and Lagakos (1993) for stratified tests.
  • To propose a less stringent condition for maintaining the test's size.
  • To provide guidance for situations where none of the specified conditions are met.

Main Methods:

  • Analysis of the conditions for stratified testing of repeated measures data with missing observations.

Related Experiment Videos

  • Illustration of identified problems using the CD4 count example and a stimulation study.
  • Development and evaluation of a relaxed condition for test size control.

    • Main Results:

    • Practical issues were identified with the original sufficient conditions for stratified testing.
    • A less stringent condition was derived, demonstrating its utility in maintaining test size.
    • The study provides a framework for handling cases where initial conditions are not satisfied.

    Conclusions:

    • The original conditions for stratified testing of missing data in repeated measures may not always be practical.
    • A revised, less stringent condition offers a more flexible approach to test size control.
    • The findings offer valuable insights for biostatisticians dealing with incomplete repeated measures data.