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

ABSTRACT

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.

KEYWORDS:

• causal inference
• non-proportional hazards
• real-world data
• survival analysis
• propensity score

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Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.