Statistical methods of indirect comparison with real-world data for survival endpoint under non-proportional hazards

被引：5
|
作者：
Lin, Zihan [1 ]
Zhao, Dan [2 ]
Lin, Junjing [3 ]
Ni, Ai [1 ]
Lin, Jianchang [3 ]
机构：
[1] Ohio State Univ, Coll Publ Hlth, Div Biostat, Columbus, OH 43210 USA
[2] Servier Pharmaceut, Biometr Dept, Boston, MA 02210 USA
[3] Takeda Pharmaceut, Stat & Quantitat Sci, Cambridge, MA 02139 USA
来源：
JOURNAL OF BIOPHARMACEUTICAL STATISTICS | 2022年 / 32卷 / 04期
关键词：
causal inference; non-proportional hazards; real-world data; survival analysis; propensity score; PROPENSITY-SCORE; RANK TEST; CAUSAL; SUBCLASSIFICATION; ESTIMATOR; OUTCOMES; ROBUST; BIAS;
D O I：
10.1080/10543406.2022.2080696
中图分类号：
R9 [药学];
学科分类号：
1007 ;
摘要：
In clinical studies that utilize real-world data, time-to-event outcomes are often germane to scientific questions of interest. Two main obstacles are the presence of non-proportional hazards and confounding bias. Existing methods that could adjust for NPH or confounding bias, but no previous work delineated the complexity of simultaneous adjustments for both. In this paper, a propensity score stratified MaxCombo and weighted Cox model is proposed. This model can adjust for confounding bias and NPH and can be pre-specified when NPH pattern is unknown in advance. The method has robust performance as demonstrated in simulation studies and in a case study.
引用
收藏
页码：582 / 599
页数：18
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