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Abstract:
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In a study of provider specialty in the treatment of ovarian cancer, a late divergence in the Kaplan-Meier survival curves hinted at superior survival among patients of gynecological oncologists when compared to patients of medical oncologists; we ask whether this late divergence should be taken seriously. Specifically, we develop exact, permutation tests, and exact confidence intervals formed by inverting the tests, for late effects in matched pairs subject to random but heterogeneous censoring without requiring knowledge of censoring times for patients who die. Exact distributions are consequences of three results about signs, signed ranks, and their conditional independence properties. A simulation shows that the late effects signed rank test has substantially more power to detect late effects than do conventional tests.
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