| Autor: |
Shengli An, Peter Zhang, Hong-Bin Fang |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Mathematics, Vol 11, Iss 13, p 2855 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2227-7390 |
| DOI: |
10.3390/math11132855 |
| Popis: |
Identifying a subgroup of patients who may have an enhanced treatment effect in a randomized clinical trial has received increasing attention recently. For time-to-event outcomes, it is a challenge to define the effectiveness of a treatment and to choose a cutoff time point for identifying subgroup membership, especially in trials in which the two treatment arms do not differ in overall survival. In this paper, we propose a mixture cure model to identify a subgroup for a new treatment that was compared to a classical treatment (or placebo) in a randomized clinical trial with respect to survival time. Using the concordance probability measurement (K-index), we propose a statistic to test the existence of subgroups with effective treatments in the treatment arm. Subsequently, the subgroup is defined by a limited number of covariates based on the estimated area under the curve (AUC). The performance of this method in different scenarios is assessed through simulation studies. A real data example is also provided for illustration. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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