The contribution of longitudinal comorbidity measurements to survival analysis

Abstract

Background: Many clinical and health services research studies are longitudinal, raising questions about how best to use an individual's comorbidity measurements over time to predict survival.

Objectives: To evaluate the performance of different approaches to longitudinal comorbidity measurement in predicting survival, and to examine strategies for addressing the inevitable issue of missing data.

Research design: Retrospective cohort study using Cox regression analysis to examine the association between various Romano-Charlson comorbidity measures and survival.

Subjects: Fifty thousand cancer-free individuals aged 66 or older enrolled in Medicare between 1991 and 1999 for at least 1 year.

Results: The best fitting model combined both time independent baseline comorbidity and the time dependent prior year comorbidity measure. The worst fitting model included baseline comorbidity only. Overall, the models fit best when using the "rolling" comorbidity measures that assumed chronic conditions persisted rather than measures using only prior year's recorded diagnoses.

Conclusions: Longitudinal comorbidity is an important predictor of survival, and investigators should make use of individuals' longitudinal comorbidity data in their regression modeling.

Figure 1

Longitudinal Comorbidity and Rolling Comorbidity Measurements for 8 Selected Non-survivors by Year.