Accommodating informative dropout and death: a joint modelling approach for longitudinal and semi-competing risks data.

Qiuju Li,Li Su

doi:10.1111/rssc.12210

Abstract

Summary Both dropout and death can truncate observation of a longitudinal outcome. Since extrapolation beyond death is often not appropriate, it is desirable to obtain the longitudinal outcome profile of a population given being alive. We propose a new likelihood‐based approach to accommodating informative dropout and death by jointly modelling the longitudinal outcome and semicompeting event times of dropout and death, with an important feature that the conditional longitudinal profile of being alive can be conveniently obtained in a closed form. We use proposed methods to estimate different longitudinal profiles of CD4 cell count for patients from the ‘HIV Epidemiology Research Study’.

Highlights

CD4 cell count within baseline viral load groups, whereas Table 2 indicates that the hazards of dropout and death are both higher for those with higher baseline human immunodeficiency virus (HIV) viral load
linear mixed model (LMM) results suggest that patients with antiretroviral therapy at baseline had a less steep decline in CD4 cell count given other covariates, but the joint model (JM) analysis did not find enough evidence to support this finding
The estimates for the unconditional model from our JM analysis are very similar to the results from the pattern–mixture model analysis that was reported in Hogan et al (2004)

Summary

Introduction

In a study of human immunodeficiency virus (HIV), CD4 cell count as the primary outcome of disease progression is scheduled to be repeatedly measured at planned follow-up visits, but both patients’ withdrawal and death can terminate the observation of CD4 cell counts for a patient during the study period. This ‘dropout mixed with death’ problem started to attract attention in the literature a decade ago, primarily driven by applications in studies of aging (Dufouil et al, 2004; Rajan and Leurgans, 2010). Using a multiple inverse probability weighting approach, Rajan and Leurgans (2010) developed an unconditional model to account for both dropout and death (Harel and Demirtas, 2011)

Objectives

Results

Discussion

Conclusion