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Joint Longitudinal Models for Dealing With Missing at Random Data in Trial-Based Economic Evaluations.

Andrea Gabrio1, Rachael Hunter2, Alexina J Mason3

  • 1Department of Statistical Science, University College London, London, UK.

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PubMed
Summary
This summary is machine-generated.

Joint longitudinal models improve economic evaluations by addressing missing data under the missing at random assumption. These advanced methods provide valid inferences, unlike aggregated approaches that may yield biased results.

Keywords:
Bayesian statisticscost-effectiveness analysislongitudinal modelsmissing at randommissing data

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Area of Science:

  • Health Economics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Missing data is common in trial-based economic evaluations, impacting outcome measures like quality-adjusted life-years.
  • Complete case analysis is inefficient and can lead to biased estimates.
  • Standard imputation methods often rely on the missing at random (MAR) assumption.

Purpose of the Study:

  • To propose and evaluate joint longitudinal models for handling missing data in economic evaluations.
  • To compare joint longitudinal models with aggregated methods under the MAR assumption.
  • To improve the estimation of aggregated outcomes using all available data.

Main Methods:

  • A simulation study comparing aggregated methods (case deletion, baseline imputation, joint aggregated models) and disaggregated joint longitudinal models.
  • Application of these methods to two real-world case studies.
  • Analysis under the missing at random (MAR) assumption.

Main Results:

  • Simulations demonstrated that aggregated methods can produce biased results depending on the missingness mechanism.
  • Joint longitudinal models yielded valid inferences under the MAR assumption across simulations.
  • Case study analyses showed variations in parameter and cost-effectiveness estimates based on data inclusion.

Conclusions:

  • Aggregated methods for handling missing data in economic evaluations are potentially biased as they disregard partially observed follow-up data.
  • Joint longitudinal models offer a superior approach by incorporating all available evidence within a longitudinal framework.
  • Utilizing joint models overcomes limitations of aggregated methods, leading to more robust and reliable economic evaluations.