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Unlike randomized experiments, observational studies suffer from both observed and unobserved confounding biases. To adjust for observed confounding in survival analysis, the commonly used methods are the Cox proportional hazards (PH) model, the weighted logrank test, and the inverse probability of treatment weighted Cox PH model. These methods do not rely on fully parametric models, but their practical performances are highly influenced by the validity of the PH assumption. Also, there are few methods addressing the hidden bias in causal survival analysis. We propose a strategy to test for survival function differences based on the matching design and explore sensitivity of the <jats:italic>P<\/jats:italic>\u2010values to assumptions about unmeasured confounding. Specifically, we apply the paired Prentice\u2010Wilcoxon (PPW) test or the modified PPW test to the propensity score matched data. Simulation studies show that the PPW\u2010type test has higher power in situations when the PH assumption fails. For potential hidden bias, we develop a sensitivity analysis based on the matched pairs to assess the robustness of our finding, following Rosenbaum's idea for nonsurvival data. For a real data illustration, we apply our method to an observational cohort of chronic liver disease patients from a Mayo Clinic study. The PPW test based on observed data initially shows evidence of a significant treatment effect. But this finding is not robust, as the sensitivity analysis reveals that the <jats:italic>P<\/jats:italic>\u2010value becomes nonsignificant if there exists an unmeasured confounder with a small impact.<\/jats:p>","DOI":"10.1002\/sim.7599","type":"journal-article","created":{"date-parts":[[2018,2,5]],"date-time":"2018-02-05T05:32:41Z","timestamp":1517808761000},"page":"1846-1858","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Testing causal effects in observational survival data using propensity score matching design"],"prefix":"10.1002","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3807-7869","authenticated-orcid":false,"given":"Bo","family":"Lu","sequence":"first","affiliation":[{"name":"Division of Biostatistics, College of Public Health The Ohio State University  Columbus OH 43210 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