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Stratified initial values and change scores

P A Lachenbruch1, T W Wilson, R D Cohen

  • 1Department of Biostatistics, UCLA School of Public Health 90024-1772.

Methods of Information in Medicine
|August 1, 1993
PubMed
Summary
This summary is machine-generated.

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Regressing variable change on initial values creates misleading associations. Stratifying data by initial values does not resolve this common statistical problem, as shown by theoretical and simulation results.

Area of Science:

  • Statistics
  • Biostatistics
  • Data Analysis

Background:

  • Relating a variable's change to its initial value is a frequent research question.
  • Regressing change on initial value is known to produce spurious associations.
  • Stratification by initial value is a proposed method to address this issue.

Purpose of the Study:

  • To evaluate the effectiveness of data stratification for addressing spurious associations when relating variable change to initial values.
  • To demonstrate that stratification does not resolve the problem of misleading associations.

Main Methods:

  • Theoretical analysis.
  • Simulation studies using independent normal and exponential random variables.

Main Results:

Related Experiment Videos

  • Demonstrated that regressing change on initial value leads to spurious associations.
  • Showcased that stratifying data based on initial values does not eliminate these misleading associations.
  • Provided theoretical and simulation evidence for independent normal and exponential variables.

Conclusions:

  • The common practice of stratifying data by initial values does not correct for the inherent spurious associations when analyzing variable change.
  • Researchers should be cautious of misleading results when examining the relationship between a variable's change and its initial value.