Shrinkage estimation in nonparametric Bayesian survival analysis: a simulation study

Rai, Kamta; Susarla, V.; Ryzin, John Van

doi:10.1080/03610918008812154

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…Also, as far as the conventional KM is concerned, in three sets of trials the new procedure was more than 100 times better (in terms of AISE) than the KM. This finding seems in line with results found by Rai, Susarla, and Van Ryzin (1980), which dealt with the overall performance of the KM estimator obtained from small to moderate samples.…”

supporting

confidence: 93%

Modular Nonparametric Subsurvival Estimation

Tarter

Freeman²,

Polissar

1990

Journal of the American Statistical Association

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This article describes new methods for estimating survival distributions based on nonparametric curve estimators. One approach improves the estimation of long-term survival rates. Simulation studies using Weibull and lognormal data show that even in the case found to be least favorable, the new method has less than one-seventh the prediction error of all conventional lifetable (LT) or Kaplan-Meier (KM) estimators, even when the LT and KM techniques a~e optimized for t~e purp~se o~long-term survival estimation. In addition to conventional survival applications, one can also estimate the probability of being diseasefree at different ages and following different exposures to possibly harmful environmental contaminants. This approach is particularly useful in situations where the effects of a confounding, nuisance, or effect-modifying variable cannot be confidently modeled in a parametric form. The new techniques are based on a closed-form nonparametric maximum likelihood curve estimator expressed in terms of separate curve estimates obtained from samples of randomly censored and uncensored times to failure-that is, subsurvival populations.

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supporting

confidence: 93%

Modular Nonparametric Subsurvival Estimation

Tarter

Freeman²,

Polissar

1990

Journal of the American Statistical Association

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“…To study this question thoroughly, Rai, Susarla and Van Ryzin [47] carry out a simulation study to compare the performance of these estimators for small sample sizes. In this study the prior survival Ŝ0 (t) and the weight β are chosen using the following empirical Bayes approach:…”

Section: Comparison With the Kaplan-meier Estimatormentioning

confidence: 99%

“…To obtain a consistent estimator it is necessary to take β(n) = O(n k ) with 0 < k < 1. In particular, the suggested choice of Rai, Susarla and Van Ryzin [47]…”

Section: Comparison With the Kaplan-meier Estimatormentioning

confidence: 99%

The analysis of interval-censored survival data. From a Nonparametric perspective to a nonparametric Bayesian approach

Calle

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This work concerns some problems in the area of survival analysis that arise in real clinical or epidemiological studies. In particular, we approach the problem of estimating the survival function based on interval-censored data or doubly-censored data. We will start defining these concepts and presenting a brief review of different methodologies to deal with this kind of censoring patterns.<br/>Survival analysis is the term used to describe the analysis of data that correspond to the time from a well defined origin time until the occurrence of some particular event of interest. This event need not necessarily be death, but could, for example, be the response to a treatment, remission from a disease, or the occurrence of a symptom

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Bayes Estimator of Survival Probability from Randomly Censored Observations with Weibull Distribution of Time to Death

Pandey

1987

Biometrical J

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SummaryThis paper deals with Beyea estimation of survival probability when the data are randomly cenmred. such a situation arks in came of a clbkal trial which extends for 8 limited period T. A fixed number of patienta (n) are observed whose times to death have identical Weibull distribution with parameters B and 8. The maximum times of observation for different patients are also independent uniform variables as the patienta arrive randomly throughout the trial. For the joint prior dietribution of (/?. 8) as suggested by Snnu and UE (1980. page 137) Bayes estimator of survival probability a t time t (O

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Shrinkage estimation in nonparametric Bayesian survival analysis: a simulation study

Cited by 8 publications

References 13 publications

Modular Nonparametric Subsurvival Estimation

Modular Nonparametric Subsurvival Estimation

The analysis of interval-censored survival data. From a Nonparametric perspective to a nonparametric Bayesian approach

Bayes Estimator of Survival Probability from Randomly Censored Observations with Weibull Distribution of Time to Death

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