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Longitudinal measures enhance causal interpretation in mediation analysis. This study introduces SAS macros for designing studies, calculating sample sizes, and estimating mediated effects, improving statistical power.

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Area of Science:

  • Statistics
  • Biostatistics
  • Causal Inference

Background:

  • Mediation analysis assesses how independent variables influence dependent variables through mediators.
  • Causal interpretation in mediation is challenging due to difficulties in randomizing mediators.
  • Longitudinal data offers a promising approach to strengthen causal claims in mediation analysis.

Purpose of the Study:

  • To investigate how longitudinal measures improve causal interpretation of mediated effects.
  • To provide practical SAS tools for mediation analysis study design and power calculations.
  • To facilitate the detection of mediated effects in research.

Main Methods:

  • Development of three SAS macros utilizing REG, CALIS, and SURVEYSELECT procedures.
  • Implementation of statistical models for estimating mediated effects in pretest-posttest control group designs.
  • Conducting prospective and retrospective power analyses for sample size determination.

Main Results:

  • SAS macros are provided for estimating mediated effects using established statistical models.
  • Tools enable prospective power analysis to determine adequate sample sizes for detecting mediated effects.
  • Retrospective power analysis is available for assessing required sample sizes in completed studies.

Conclusions:

  • Incorporating longitudinal measures aids in the causal interpretation of mediated effects.
  • The developed SAS macros offer practical solutions for designing and analyzing mediation studies.
  • These tools support researchers in achieving adequate statistical power to detect mediated effects.