Bootstrap- and permutation-based inference for the Mann–Whitney effect for right-censored and tied data
DOBLER, Dennis - Personal Name
PAULY, Markus - Personal Name
The Mann–Whitney effect is an intuitive measure for discriminating two
survival distributions. Here we analyse various inference techniques for this parameter
in a two-sample survival setting with independent right-censoring, where the survival
times are even allowed to be discretely distributed. This allows for ties in the data and
requires the introduction of normalized versions of Kaplan–Meier estimators from
which adequate point estimates are deduced. Asymptotically exact inference proce-
dures based on standard normal, bootstrap, and permutation quantiles are developed
and compared in simulations. Here, the asymptotically robust and—under exchange-
able data—even finitely exact permutation procedure turned out to be the best. Finally,
all procedures are illustrated using a real data set.
survival distributions. Here we analyse various inference techniques for this parameter
in a two-sample survival setting with independent right-censoring, where the survival
times are even allowed to be discretely distributed. This allows for ties in the data and
requires the introduction of normalized versions of Kaplan–Meier estimators from
which adequate point estimates are deduced. Asymptotically exact inference proce-
dures based on standard normal, bootstrap, and permutation quantiles are developed
and compared in simulations. Here, the asymptotically robust and—under exchange-
able data—even finitely exact permutation procedure turned out to be the best. Finally,
all procedures are illustrated using a real data set.
Availability
| EB00000004157K | Available |
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E-Jurnal
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Subject(s)
Heteroscedasticity
Counting process
Efron’s bootstrap
Kaplan–Meier estimator
Permutation technique
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Statement of Responsibility
Dennis Dobler 1, Markus Pauly 1
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