Abstract
BackgroundDynamic risk models, which incorporate disease-free survival and repeated measurements over time, might yield more accurate predictions of future health status compared to static models. The objective of this study was to develop and apply a dynamic prediction model to estimate the risk of developing type 2 diabetes mellitus.MethodsBoth a static prediction model and a dynamic landmark model were used to provide predictions of a 2-year horizon time for diabetes-free survival, updated at 1, 2, and 3 years post-baseline i.e., predicting diabetes-free survival to 2 years and predicting diabetes-free survival to 3 years, 4 years, and 5 years post-baseline, given the patient already survived past 1 year, 2 years, and 3 years post-baseline, respectively. Prediction accuracy was evaluated at each time point using robust non-parametric procedures. Data from 2057 participants of the Diabetes Prevention Program (DPP) study (1027 in metformin arm, 1030 in placebo arm) were analyzed.ResultsThe dynamic landmark model demonstrated good prediction accuracy with area under curve (AUC) estimates ranging from 0.645 to 0.752 and Brier Score estimates ranging from 0.088 to 0.135. Relative to a static risk model, the dynamic landmark model did not significantly differ in terms of AUC but had significantly lower (i.e., better) Brier Score estimates for predictions at 1, 2, and 3 years (e.g. 0.167 versus 0.099; difference − 0.068 95% CI − 0.083 to − 0.053, at 3 years in placebo group) post-baseline.ConclusionsDynamic prediction models based on longitudinal, repeated risk factor measurements have the potential to improve the accuracy of future health status predictions.
Highlights
Dynamic risk models, which incorporate disease-free survival and repeated measurements over time, might yield more accurate predictions of future health status compared to static models
A total of 182 participants assigned to the placebo arm (18%) and 126 participants assigned to the metformin arm (12%) were diagnosed with diabetes within 2 years of baseline
Among the 866 placebo participants and 914 metformin participants who survived to 1 year postbaseline without a diabetes diagnosis, 159 (18%) and 140 (15%) were diagnosed with diabetes within 2 years, respectively
Summary
Dynamic risk models, which incorporate disease-free survival and repeated measurements over time, might yield more accurate predictions of future health status compared to static models. Some available methods that allow for the use of such longitudinal measurements are often considered overly complex or undesirable due to restrictive parametric modeling assumptions or infeasible due to computational requirements [12,13,14,15]. That is, with these methods it is often necessary to specify a parametric model for the longitudinal measurements, and a parametric or semiparametric model characterizing the relationship between the time-to-event outcome and the longitudinal measurements and use, for example, a Bayesian framework to obtain parameter estimates
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