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

Published:February 18, 2021DOI:https://doi.org/10.1016/j.jval.2020.11.018

      Highlights

      • Standard methods to handle missing data in trial-based economic evaluations discard some of the observed responses. In this article, we illustrate how joint longitudinal models provide an alternative and potentially less biased approach for handling missing data with respect to current practice under a missing at random assumption.
      • Methods that ignore some of the available information may be associated with biased results and mislead the decision-making process. Given the common problem of missing data, many study conclusions could be based on imprecise economic evidence.
      • This is a potentially serious issue for those who use these evaluations in their decision making, thus possibly leading to incorrect policy decisions about the cost-effectiveness of new treatment options.

      Abstract

      Objectives

      In trial-based economic evaluation, some individuals are typically associated with missing data at some time point, so that their corresponding aggregated outcomes (eg, quality-adjusted life-years) cannot be evaluated. Restricting the analysis to the complete cases is inefficient and can result in biased estimates, while imputation methods are often implemented under a missing at random (MAR) assumption. We propose the use of joint longitudinal models to extend standard approaches by taking into account the longitudinal structure to improve the estimation of the targeted quantities under MAR.

      Methods

      We compare the results from methods that handle missingness at an aggregated (case deletion, baseline imputation, and joint aggregated models) and disaggregated (joint longitudinal models) level under MAR. The methods are compared using a simulation study and applied to data from 2 real case studies.

      Results

      Simulations show that, according to which data affect the missingness process, aggregated methods may lead to biased results, while joint longitudinal models lead to valid inferences under MAR. The analysis of the 2 case studies support these results as both parameter estimates and cost-effectiveness results vary based on the amount of data incorporated into the model.

      Conclusions

      Our analyses suggest that methods implemented at the aggregated level are potentially biased under MAR as they ignore the information from the partially observed follow-up data. This limitation can be overcome by extending the analysis to a longitudinal framework using joint models, which can incorporate all the available evidence.

      Keywords

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