Geraldine Rauch1,2, Meinhard Kieser3, Harald Binder4,5, Antoni Bayes-Genis6, Antje Jahn-Eimermacher4,7. 1. Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Biometry and Clinical Epidemiology, Berlin, Germany. geraldine.rauch@charite.de. 2. Institute of Medical Biometry and Informatics, University of Heidelberg, Berlin, Germany. geraldine.rauch@charite.de. 3. Institute of Medical Biometry and Informatics, University of Heidelberg, Berlin, Germany. 4. Institute of Medical Biostatistics, Epidemiology and Informatics, University of Mainz, Mainz, Germany. 5. Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg im Breisgau, Germany. 6. Heart Failure Clinic, Department of Medicine, Hospital Universitari Germans Trias i Pujol, Barcelona, Spain. 7. University of Applied Sciences Darmstadt, Darmstadt, Germany.
Abstract
BACKGROUND: Composite endpoints combining several event types of clinical interest often define the primary efficacy outcome in cardiologic trials. They are commonly evaluated as time-to-first-event, thereby following the recommendations of regulatory agencies. However, to assess the patient's full disease burden and to identify preventive factors or interventions, subsequent events following the first one should be considered as well. This is especially important in cohort studies and RCTs with a long follow-up leading to a higher number of observed events per patients. So far, there exist no recommendations which approach should be preferred. DESIGN: Recently, the Cardiovascular Round Table of the European Society of Cardiology indicated the need to investigate "how to interpret results if recurrent-event analysis results differ […] from time-to-first-event analysis" (Anker et al., Eur J Heart Fail 18:482-489, 2016). This work addresses this topic by means of a systematic simulation study. METHODS: This paper compares two common analysis strategies for composite endpoints differing with respect to the incorporation of recurrent events for typical data scenarios motivated by a clinical trial. RESULTS: We show that the treatment effects estimated from a time-to-first-event analysis (Cox model) and a recurrent-event analysis (Andersen-Gill model) can systematically differ, particularly in cardiovascular trials. Moreover, we provide guidance on how to interpret these results and recommend points to consider for the choice of a meaningful analysis strategy. CONCLUSIONS: When planning trials with a composite endpoint, researchers, and regulatory agencies should be aware that the model choice affects the estimated treatment effect and its interpretation.
BACKGROUND: Composite endpoints combining several event types of clinical interest often define the primary efficacy outcome in cardiologic trials. They are commonly evaluated as time-to-first-event, thereby following the recommendations of regulatory agencies. However, to assess the patient's full disease burden and to identify preventive factors or interventions, subsequent events following the first one should be considered as well. This is especially important in cohort studies and RCTs with a long follow-up leading to a higher number of observed events per patients. So far, there exist no recommendations which approach should be preferred. DESIGN: Recently, the Cardiovascular Round Table of the European Society of Cardiology indicated the need to investigate "how to interpret results if recurrent-event analysis results differ […] from time-to-first-event analysis" (Anker et al., Eur J Heart Fail 18:482-489, 2016). This work addresses this topic by means of a systematic simulation study. METHODS: This paper compares two common analysis strategies for composite endpoints differing with respect to the incorporation of recurrent events for typical data scenarios motivated by a clinical trial. RESULTS: We show that the treatment effects estimated from a time-to-first-event analysis (Cox model) and a recurrent-event analysis (Andersen-Gill model) can systematically differ, particularly in cardiovascular trials. Moreover, we provide guidance on how to interpret these results and recommend points to consider for the choice of a meaningful analysis strategy. CONCLUSIONS: When planning trials with a composite endpoint, researchers, and regulatory agencies should be aware that the model choice affects the estimated treatment effect and its interpretation.
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