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Abstract Details
Comparison of seven models for the progression patterns of multiple chronic conditions in longitudinal studies.
Baneshi, Mohammad Reza (MR);Mishra, Gita (G);Dobson, Annette (A);
INTRODUCTION: Studies investigating the relationship between patterns of multimorbidity and risk of a new condition have typically defined the patterns at a baseline time and used Kaplan-Meier (KM) or Cox proportional hazards regression. These methods do not consider the competing risk of death or the changes in the patterns of conditions over time. This study illustrates how these methodological limitations can be overcome in the setting of progression from cardiometabolic conditions to dementia.
METHODS: Data from 11 930 women who participated in the Australian Longitudinal Study on Women's Health were used to define patterns of diabetes, heart disease and stroke and estimate the cumulative incidence or HRs of subsequent dementia. Seven methods were compared. For cumulative incidence these were KM method, cumulative incidence function (CIF) (to account for the competing risk of death) and multistate model with Aalen-Johansen estimates (to account also for the progression of conditions over time). For HRs, the corresponding methods were Cox model and Fine and Gray model (for sub-HRs) with the cardiometabolic patterns treated as time-invariant (from baseline) or as time-varying predictors.
RESULTS: The estimated cumulative incidence of dementia using the KM method declined when the competing risk of death was considered. For example, for women with no cardiometabolic condition at baseline, the KM and CIF estimates were 35.7% (95% CI 34.6%, 36.8%) and 27.3% (26.4%, 28.2%) but these women may have developed cardiometabolic conditions during the study which would increase their risk. The Aalen-Johansen multistate estimate for women with no cardiometabolic condition over the whole study period was 11.0% (10.4%, 11.7%). Comparing models to estimate HRs, the estimates in the Fine and Gray models were lower than those in the Cox models.
CONCLUSIONS: Multistate and time-varying survival analysis models should be used to study the natural development of multimorbidity.
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