Confirmatory Adaptive Designs for Clinical Trials With Multiple Time‐to‐Event Outcomes in Multi‐state Markov Models
ABSTRACT The analysis of multiple time‐to‐event outcomes in a randomized controlled clinical trial can be accomplished with existing methods. However, depending on the characteristics of the disease under investigation and the circumstances in which the study is planned, it may be of interest to con...
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| Veröffentlicht in: | Biometrical journal 2024-10, Vol.66 (7), p.e202300181-n/a |
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| creator | Danzer, Moritz Fabian Faldum, Andreas Simon, Thorsten Hero, Barbara Schmidt, Rene |
| description | ABSTRACT
The analysis of multiple time‐to‐event outcomes in a randomized controlled clinical trial can be accomplished with existing methods. However, depending on the characteristics of the disease under investigation and the circumstances in which the study is planned, it may be of interest to conduct interim analyses and adapt the study design if necessary. Due to the expected dependency of the endpoints, the full available information on the involved endpoints may not be used for this purpose. We suggest a solution to this problem by embedding the endpoints in a multistate model. If this model is Markovian, it is possible to take the disease history of the patients into account and allow for data‐dependent design adaptations. To this end, we introduce a flexible test procedure for a variety of applications, but are particularly concerned with the simultaneous consideration of progression‐free survival (PFS) and overall survival (OS). This setting is of key interest in oncological trials. We conduct simulation studies to determine the properties for small sample sizes and demonstrate an application based on data from the NB2004‐HR study. |
| doi_str_mv | 10.1002/bimj.202300181 |
| format | Article |
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The analysis of multiple time‐to‐event outcomes in a randomized controlled clinical trial can be accomplished with existing methods. However, depending on the characteristics of the disease under investigation and the circumstances in which the study is planned, it may be of interest to conduct interim analyses and adapt the study design if necessary. Due to the expected dependency of the endpoints, the full available information on the involved endpoints may not be used for this purpose. We suggest a solution to this problem by embedding the endpoints in a multistate model. If this model is Markovian, it is possible to take the disease history of the patients into account and allow for data‐dependent design adaptations. To this end, we introduce a flexible test procedure for a variety of applications, but are particularly concerned with the simultaneous consideration of progression‐free survival (PFS) and overall survival (OS). This setting is of key interest in oncological trials. 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The analysis of multiple time‐to‐event outcomes in a randomized controlled clinical trial can be accomplished with existing methods. However, depending on the characteristics of the disease under investigation and the circumstances in which the study is planned, it may be of interest to conduct interim analyses and adapt the study design if necessary. Due to the expected dependency of the endpoints, the full available information on the involved endpoints may not be used for this purpose. We suggest a solution to this problem by embedding the endpoints in a multistate model. If this model is Markovian, it is possible to take the disease history of the patients into account and allow for data‐dependent design adaptations. To this end, we introduce a flexible test procedure for a variety of applications, but are particularly concerned with the simultaneous consideration of progression‐free survival (PFS) and overall survival (OS). This setting is of key interest in oncological trials. We conduct simulation studies to determine the properties for small sample sizes and demonstrate an application based on data from the NB2004‐HR study.</abstract><cop>Germany</cop><pub>Wiley - VCH Verlag GmbH & Co. KGaA</pub><pmid>39402846</pmid><doi>10.1002/bimj.202300181</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0002-0512-4227</orcidid><orcidid>https://orcid.org/0000-0003-4808-6707</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Biometry - methods clinical trial Clinical trials Clinical Trials as Topic - methods Disease control Embedding Endpoint Determination Humans log‐rank test Markov Chains Models, Statistical Progression-Free Survival Randomized Controlled Trials as Topic Research Design sample size recalculation Survival survival analysis |
| title | Confirmatory Adaptive Designs for Clinical Trials With Multiple Time‐to‐Event Outcomes in Multi‐state Markov Models |
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