Dynamic risk prediction for diabetes using biomarker change measurements.

Abstract:

BACKGROUND: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. The objective of this study was to develop and apply a dynamic prediction model to estimate the risk of developing type 2 diabetes mellitus. METHODS:Both 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. RESULTS:The 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. CONCLUSIONS:Dynamic prediction models based on longitudinal, repeated risk factor measurements have the potential to improve the accuracy of future health status predictions.

journal_name

BMC Med Res Methodol

authors

Parast L,Mathews M,Friedberg MW

doi

10.1186/s12874-019-0812-y

subject

Has Abstract

pub_date

2019-08-14 00:00:00

pages

175

issue

1

issn

1471-2288

pii

10.1186/s12874-019-0812-y

journal_volume

19

pub_type

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