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Nonparametric Comparison of Two Survival‐Time Distributions in the Presence of Dependent Censoring
Author(s) -
DiRienzo A. G.
Publication year - 2003
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/1541-0420.00059
Subject(s) - censoring (clinical trials) , null hypothesis , mathematics , log rank test , statistics , nonparametric statistics , statistical hypothesis testing , conditional independence , alternative hypothesis , econometrics , null distribution , survival analysis , test statistic
Summary . When testing the null hypothesis that treatment arm‐specific survival‐time distributions are equal, the log‐rank test is asymptotically valid when the distribution of time to censoring is conditionally independent of randomized treatment group given survival time. We introduce a test of the null hypothesis for use when the distribution of time to censoring depends on treatment group and survival time. This test does not make any assumptions regarding independence of censoring time and survival time. Asymptotic validity of this test only requires a consistent estimate of the conditional probability that the survival event is observed given both treatment group and that the survival event occurred before the time of analysis. However, by not making unverifiable assumptions about the data‐generating mechanism, there exists a set of possible values of corresponding sample‐mean estimates of these probabilities that are consistent with the observed data. Over this subset of the unit square, the proposed test can be calculated and a rejection region identified. A decision on the null that considers uncertainty because of censoring that may depend on treatment group and survival time can then be directly made. We also present a generalized log‐rank test that enables us to provide conditions under which the ordinary log‐rank test is asymptotically valid. This generalized test can also be used for testing the null hypothesis when the distribution of censoring depends on treatment group and survival time. However, use of this test requires semiparametric modeling assumptions. A simulation study and an example using a recent AIDS clinical trial are provided.