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 Methodoljournal_title
BMC medical research methodologyauthors
Parast L,Mathews M,Friedberg MWdoi
10.1186/s12874-019-0812-ysubject
Has Abstractpub_date
2019-08-14 00:00:00pages
175issue
1issn
1471-2288pii
10.1186/s12874-019-0812-yjournal_volume
19pub_type
杂志文章abstract:BACKGROUND:The Template for Intervention Description and Replication (TIDieR) checklist and guide was developed by an international team of experts to promote full and accurate description of trial interventions. It is now widely used in health research. The aim of this paper is to describe the experience of using TIDi...
journal_title:BMC medical research methodology
pub_type: 杂志文章
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journal_title:BMC medical research methodology
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pub_type: 杂志文章,多中心研究
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pub_type: 杂志文章
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