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Related Experiment Videos

Applying sample survey methods to clinical trials data.

L M LaVange1, G G Koch, T A Schwartz

  • 1Quintiles, Inc., Research Triangle Park, NC 27709, U.S.A.

Statistics in Medicine
|August 28, 2001
PubMed
Summary

Sample survey statistical methods effectively analyze complex clinical trial data, particularly for repeated measures and clustered observations. These robust techniques offer improved analysis for logistic, ordinal, and survival data in clinical research.

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Statistical Modeling

Background:

  • Clinical trials often involve complex data structures, including repeated measures and clustered observations, which traditional statistical methods may not adequately handle.
  • Sample survey statistical methods, designed for analyzing data from finite populations with complex sampling designs, offer a robust alternative for clinical trial data.
  • Existing methods for repeated measures analysis can be less effective with complicated data structures common in clinical studies.

Purpose of the Study:

  • To outline the utility of sample survey statistical methods for analyzing clinical trial data.
  • To demonstrate how survey data analysis approaches can accommodate clustering and repeated measures in clinical studies.
  • To present regression methods for analyzing various data types (dichotomous, ordinal, continuous, time-to-event) in clinical trials.

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Main Methods:

  • Application of sample survey regression methods, including logistic, proportional odds, and linear models, to clinical trial data.
  • Utilizing robust estimates of standard errors to account for intraclass correlation in repeated measures.
  • Adapting time-to-event analysis methods from sample surveys for survival endpoints in clinical trials.
  • Comparison with traditional multivariate repeated measures analysis and generalized estimating equations (GEE).

Main Results:

  • Sample survey regression methods provide more effective analysis for repeated measures studies with complex data structures compared to traditional approaches.
  • These methods account for intraclass correlation through robust standard error estimates.
  • The analytic approach is applicable to logistic, proportional odds, and linear models, yielding interpretable population average parameters.
  • Survey methods for time-to-event data are shown to be useful for survival endpoints in late-phase drug development studies.

Conclusions:

  • Sample survey statistical methods offer a powerful and flexible framework for analyzing complex clinical trial data, especially with repeated measures and clustering.
  • These methods provide robust and interpretable results, enhancing the analysis of various data types, including survival data.
  • The application of these survey techniques can improve the rigor and efficiency of clinical trial data analysis.