Dealing with missing data under joint modelling: application to HIV data

Abstract

Background: Addressing missing data poses a significant challenge in clinical research, particularly in studies like those focused on HIV. Traditional methods employed by researchers to tackle this issue often yield biased estimates and unreliable recommendations. In this study, we assess the effectiveness of an innovative strategy that integrates both longitudinal and survival processes to handle missing data, applied specifically to HIV data from Kenya obtained from the Kenya Health Information System (KHIS). Methods: We conducted simulation studies by generating five datasets with varying percentages of missingness: 0% (representing a complete simulated dataset), 10%, 20%, 30%, and 40%. Subsequently, multiple imputation was performed on the four datasets containing missing values. This was followed by fitting a joint model to the imputed datasets. After the simulation studies, we applied the analysis to the real HIV data and conducted some diagnostic tests to the fitted joint models. Results: The results indicate that there is no discernible difference in the models post imputation across different percentages of missingness. Additionally, the joint model exhibits a good fit for our data compared to individual sub-models for longitudinal and survival analysis. Conclusion: Joint modeling integrating survival and longitudinal models, emerges as a powerful statistical approach in clinical research, particularly in HIV studies, to address complex data structures and missing data challenges. This study concludes with insights into its significant contributions and future directions in clinical research. Keywords: Missingness; Joint model; Time-to-event; Imputation; Simulations.