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Modeling approaches for cross-sectional integrative data analysis: Evaluations and recommendations.

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Integrative data analysis (IDA) using random-slopes multilevel modeling (MLM) offers well-controlled error rates by modeling between-study heterogeneity. MLMs are feasible for IDA with 3-6 studies and appropriate estimation methods.

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Area of Science:

  • Psychology
  • Statistical Modeling
  • Data Analysis

Background:

  • Integrative data analysis (IDA) combines data from multiple studies.
  • Existing fixed-effects and multilevel modeling (MLM) approaches for IDA lack comprehensive performance evaluations.
  • Cross-sectional IDA performance of various models is not well-understood.

Purpose of the Study:

  • Evaluate five models for cross-sectional integrative data analysis (IDA).
  • Compare fixed-effects regressions (aggregated, disaggregated, study-specific coefficients) and MLMs (fixed-slope, random-slopes).
  • Assess estimation bias and type I error rates for participant- and study-level effects.

Main Methods:

  • Simulation study with sample sizes and study numbers mirroring applied IDA (2-35 studies).
  • Evaluated fixed-effects models: aggregated, disaggregated, study-specific coefficients regressions.
  • Evaluated MLMs: fixed-slope and random-slopes, with various estimation methods (variance estimation, degrees of freedom).

Main Results:

  • Disaggregated, study-specific regressions, and both MLMs showed minimal bias in fixed effects estimates.
  • Random-slopes MLM effectively modeled between-study heterogeneity, controlling type I error rates for fixed effects.
  • MLMs demonstrated feasibility for IDA with 3-6 studies when using suitable estimation techniques.

Conclusions:

  • Random-slopes MLM is recommended for cross-sectional IDA due to its ability to model heterogeneity and control error rates.
  • MLMs are viable for IDA with a small to moderate number of studies (3-6) with careful method selection.
  • Findings guide researchers in selecting optimal statistical models for IDA applications.