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Design and analysis of two-phase studies with multivariate longitudinal data.

Chiara Di Gravio1, Ran Tao1,2, Jonathan S Schildcrout1

  • 1Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

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Summary

This study introduces efficient two-phase study designs for multivariate longitudinal outcomes. The methods enhance parameter estimation for time-varying exposures, applicable to secondary data analyses.

Keywords:
Lung Health Studyascertainment corrected maximum likelihoodmissing datamultiple imputationoutcome-dependent samplingsecondary outcome analysis

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Longitudinal Data Analysis

Background:

  • Two-phase studies are valuable when exposure data is costly to collect retrospectively.
  • Existing methods efficiently handle longitudinal outcomes by stratifying based on outcome summaries.
  • Challenges remain in extending these designs to multivariate longitudinal continuous outcomes.

Purpose of the Study:

  • To extend efficient two-phase study designs to multivariate longitudinal continuous outcomes.
  • To present and evaluate two novel analysis approaches for these extended designs.
  • To demonstrate the applicability of these methods to secondary data analysis.

Main Methods:

  • Developed efficient two-phase study designs for multivariate longitudinal continuous outcomes.
  • Proposed a multiple imputation analysis combining complete and incomplete data.
  • Proposed a conditional maximum likelihood analysis for phase-two data only.
  • Examined finite sample operating characteristics of both approaches.

Main Results:

  • The proposed two-phase designs and analysis methods are effective for multivariate longitudinal continuous outcomes.
  • Both multiple imputation and conditional maximum likelihood approaches demonstrated utility.
  • The methods were successfully applied to secondary analysis of the Lung Health Study data.
  • Genetic associations with lung function decline were examined using the developed methods.

Conclusions:

  • Efficient two-phase study designs can be successfully extended to multivariate longitudinal continuous outcomes.
  • The presented multiple imputation and conditional maximum likelihood analyses offer flexible options for researchers.
  • These approaches enhance the efficiency of estimating exposure effects in longitudinal studies.
  • The methodology is applicable to secondary analyses, maximizing the value of existing datasets.