Abstract
This article presents an aid for monitoring clinical trials with failure-time endpoints based on the Bayesian nonparametric analyses of the data. The posterior distribution is a mixture of Dirichlet processes in the presence of censoring if one assumes a Dirichlet process prior for the survival distribution. Using Gibbs sampling, one can generate random samples from the posterior distribution. With samples from the posterior distributions of treatment-specific survival curves, one can evaluate the current evidence in favor of stopping or continuing the trial based on summary statistics of these survival curves. Because the method is nonparametric, it can easily be used, for example, in situations where hazards cross or are suspected to cross and where relevant clinical decisions might be based on estimating when the integral between the curves might be expected to become positive and in favor of the new but toxic therapy. An example based on an actual trial illustrates the method.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 239-245 |
| Number of pages | 7 |
| Journal | Biometrics |
| Volume | 61 |
| Issue number | 1 |
| DOIs | |
| State | Published - Mar 2005 |
| Externally published | Yes |
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Keywords
- Clinical trials
- Dirichlet process
- Gibbs sampling
- Survival analysis
ASJC Scopus subject areas
- Agricultural and Biological Sciences(all)
- Public Health, Environmental and Occupational Health
- Agricultural and Biological Sciences (miscellaneous)
- Applied Mathematics
- Statistics and Probability
Cite this
Bayesian monitoring of clinical trials with failure-time endpoints. / Rosner, Gary.
In: Biometrics, Vol. 61, No. 1, 03.2005, p. 239-245.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Bayesian monitoring of clinical trials with failure-time endpoints
AU - Rosner, Gary
PY - 2005/3
Y1 - 2005/3
N2 - This article presents an aid for monitoring clinical trials with failure-time endpoints based on the Bayesian nonparametric analyses of the data. The posterior distribution is a mixture of Dirichlet processes in the presence of censoring if one assumes a Dirichlet process prior for the survival distribution. Using Gibbs sampling, one can generate random samples from the posterior distribution. With samples from the posterior distributions of treatment-specific survival curves, one can evaluate the current evidence in favor of stopping or continuing the trial based on summary statistics of these survival curves. Because the method is nonparametric, it can easily be used, for example, in situations where hazards cross or are suspected to cross and where relevant clinical decisions might be based on estimating when the integral between the curves might be expected to become positive and in favor of the new but toxic therapy. An example based on an actual trial illustrates the method.
AB - This article presents an aid for monitoring clinical trials with failure-time endpoints based on the Bayesian nonparametric analyses of the data. The posterior distribution is a mixture of Dirichlet processes in the presence of censoring if one assumes a Dirichlet process prior for the survival distribution. Using Gibbs sampling, one can generate random samples from the posterior distribution. With samples from the posterior distributions of treatment-specific survival curves, one can evaluate the current evidence in favor of stopping or continuing the trial based on summary statistics of these survival curves. Because the method is nonparametric, it can easily be used, for example, in situations where hazards cross or are suspected to cross and where relevant clinical decisions might be based on estimating when the integral between the curves might be expected to become positive and in favor of the new but toxic therapy. An example based on an actual trial illustrates the method.
KW - Clinical trials
KW - Dirichlet process
KW - Gibbs sampling
KW - Survival analysis
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UR - http://www.scopus.com/inward/citedby.url?scp=15044360995&partnerID=8YFLogxK
U2 - 10.1111/j.0006-341X.2005.031037.x
DO - 10.1111/j.0006-341X.2005.031037.x
M3 - Article
C2 - 15737099
AN - SCOPUS:15044360995
VL - 61
SP - 239
EP - 245
JO - Biometrics
JF - Biometrics
SN - 0006-341X
IS - 1
ER -