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The aim of this work therefore is to evaluate the performance of existing recurrent event models for the specific data situation of a composite endpoint which is commonly characterized by the following properties:įor each event type, recurrent or terminal, there exist separate event processes that might be correlated or not. Whereas the time to first event approach defines the current standard and is therefore already well understood, the application of recurrent event models to composite endpoints is rather rare. On the other hand, the different event processes are usually rather complex and as a consequence a corresponding effect measure will be a mixture of the treatment’s direct and indirect effects making the interpretation more difficult. Including recurrent events to quantify the treatment effect seems appealing as the information from each patient is fully exhausted. However, one obviously neglects that an individual may experience more than one non-fatal event which leads to a loss of information. The resulting treatment effect also denoted as the all-cause hazard ratio has the advantage that it has a rather intuitive interpretation from a clinical perspective as only the direct effect of a treatment is measured. The most common and also most simple approach to analyze a composite endpoint is to investigate the time to the first event by the common Cox model. The components of a composite endpoint ideally correspond to the same treatment effect however this often is not the case in clinical application.
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Thereby, the expected number of events increases and, as a consequence, the power increases as well. This can be avoided by considering not only one type of event but several different event types of clinical interest which can be combined within a so-called composite endpoint. To demonstrate a relevant effect and reach an acceptable power, a high number of patients has to be included in the study and observed for a long time period. In many clinical trials, the comparison of a rarely occurring event between different treatment groups is of primary interest.
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Conclusionīased on the conducted simulation study, this paper helps to understand the pros and cons of the investigated methods in the context of composite endpoints and provides therefore recommendations for an adequate statistical analysis strategy and a meaningful interpretation of results. We demonstrate that the Andersen-Gill model and the Prentice- Williams-Petersen models show similar results under various data scenarios whereas the Wei-Lin-Weissfeld model delivers effect estimators which can considerably deviate under commonly met data scenarios.
#DIFFERENT TYPES OF LICENSES IN BMC REMEDY TRIAL#
Within this work a simulation-based comparison of recurrent event models applied to composite endpoints is provided for different realistic clinical trial scenarios. Although some of the methods were already compared within the literature there exists no systematic investigation for the special requirements regarding composite endpoints. regression models based on count data or Cox-based models such as the approaches of Andersen and Gill, Prentice, Williams and Peterson or, Wei, Lin and Weissfeld. There exists a number of such models, e.g. As an alternative, composite endpoints could be analyzed by models for recurrent events. This approach neglects that an individual may experience more than one event which leads to a loss of information. Commonly, a composite endpoint is analyzed with standard survival analysis techniques by assessing the time to the first occurring event. For feasibility issues, it is therefore often considered to include several event types of interest, non-fatal or fatal, and to combine them within a composite endpoint. In this situation, showing a relevant effect with an acceptable power requires the observation of a large number of patients over a long period of time. Many clinical trials focus on the comparison of the treatment effect between two or more groups concerning a rarely occurring event.