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Time-to-event methodology improved statistical evaluation in register-based health services research.

Abstract：

OBJECTIVES:Complex longitudinal sampling and the observational structure of patient registers in health services research are associated with methodological challenges regarding data management and statistical evaluation. We exemplify common pitfalls and want to stimulate discussions on the design, development, and deployment of future longitudinal patient registers and register-based studies. STUDY DESIGN AND SETTING:For illustrative purposes, we use data from the prospective, observational, German DIabetes Versorgungs-Evaluation register. One aim was to explore predictors for the initiation of a basal insulin supported therapy in patients with type 2 diabetes initially prescribed to glucose-lowering drugs alone. RESULTS:Major challenges are missing mortality information, time-dependent outcomes, delayed study entries, different follow-up times, and competing events. We show that time-to-event methodology is a valuable tool for improved statistical evaluation of register data and should be preferred to simple case-control approaches. CONCLUSION:Patient registers provide rich data sources for health services research. Analyses are accompanied with the trade-off between data availability, clinical plausibility, and statistical feasibility. Cox' proportional hazards model allows for the evaluation of the outcome-specific hazards, but prediction of outcome probabilities is compromised by missing mortality information.

journal_name

J Clin Epidemiol

journal_title

Journal of clinical epidemiology

authors

Bluhmki T,Bramlage P,Volk M,Kaltheuner M,Danne T,Rathmann W,Beyersmann J

doi

10.1016/j.jclinepi.2016.11.001

subject

Has Abstract

pub_date

2017-02-01 00:00:00

pages

103-111

eissn

0895-4356

issn

1878-5921

pii

S0895-4356(16)30645-X

journal_volume

82

pub_type

杂志文章,多中心研究

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