Tobias Bluhmki1, Peter Bramlage2, Michael Volk3, Matthias Kaltheuner4, Thomas Danne5, Wolfgang Rathmann6, Jan Beyersmann7. 1. Institute of Statistics, Ulm University, Helmholtzstrasse 20, 89081 Ulm, Germany. Electronic address: tobias.bluhmki@uni-ulm.de. 2. Institute for Pharmacology and Preventive Medicine, Menzelstrasse 21, 15831 Mahlow, Germany. 3. axaris software & systeme GmbH, Max-Eyth-Weg 2, 89160 Dornstadt, Germany. 4. Gemeinschaftspraxis Kaltheuner-v. Boxberg, Kalkstraße 117, 51377 Leverkusen, Germany; winDiab GmbH, Kehler Strasse 24, 40468 Düsseldorf, Germany. 5. Children's and Youth Hospital "Auf der Bult", Janusz-Korczak-Allee 12, 30173 Hannover, Germany. 6. Institute for Biometrics and Epidemiology, German Diabetes Center (DDZ) Düsseldorf, Auf'm Hennekamp 65, 40225 Düsseldorf, Germany. 7. Institute of Statistics, Ulm University, Helmholtzstrasse 20, 89081 Ulm, Germany.
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.
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.