Latent Subgroup Analysis of a Randomized Clinical Trial through a Semiparametric Accelerated Failure Time Mixture Model
This article studies a semiparametric accelerated failure time mixture model for estimation of a biological treatment effect on a latent subgroup of interest with a time‐to‐event outcome in randomized clinical trials. Latency is induced because membership is observable in one arm of the trial and un...
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| Published in | Biometrics Vol. 69; no. 1; pp. 52 - 61 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Blackwell Publishers
01.03.2013
Blackwell Publishing Ltd Wiley-Blackwell |
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| Online Access | Get full text |
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| Abstract | This article studies a semiparametric accelerated failure time mixture model for estimation of a biological treatment effect on a latent subgroup of interest with a time‐to‐event outcome in randomized clinical trials. Latency is induced because membership is observable in one arm of the trial and unidentified in the other. This method is useful in randomized clinical trials with all‐or‐none noncompliance when patients in the control arm have no access to active treatment and in, for example, oncology trials when a biopsy used to identify the latent subgroup is performed only on subjects randomized to active treatment. We derive a computational method to estimate model parameters by iterating between an expectation step and a weighted Buckley–James optimization step. The bootstrap method is used for variance estimation, and the performance of our method is corroborated in simulation. We illustrate our method through an analysis of a multicenter selective lymphadenectomy trial for melanoma. |
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| AbstractList | Summary
This article studies a semiparametric accelerated failure time mixture model for estimation of a biological treatment effect on a latent subgroup of interest with a time‐to‐event outcome in randomized clinical trials. Latency is induced because membership is observable in one arm of the trial and unidentified in the other. This method is useful in randomized clinical trials with all‐or‐none noncompliance when patients in the control arm have no access to active treatment and in, for example, oncology trials when a biopsy used to identify the latent subgroup is performed only on subjects randomized to active treatment. We derive a computational method to estimate model parameters by iterating between an expectation step and a weighted Buckley–James optimization step. The bootstrap method is used for variance estimation, and the performance of our method is corroborated in simulation. We illustrate our method through an analysis of a multicenter selective lymphadenectomy trial for melanoma. Summary This article studies a semiparametric accelerated failure time mixture model for estimation of a biological treatment effect on a latent subgroup of interest with a time-to-event outcome in randomized clinical trials. Latency is induced because membership is observable in one arm of the trial and unidentified in the other. This method is useful in randomized clinical trials with all-or-none noncompliance when patients in the control arm have no access to active treatment and in, for example, oncology trials when a biopsy used to identify the latent subgroup is performed only on subjects randomized to active treatment. We derive a computational method to estimate model parameters by iterating between an expectation step and a weighted Buckley-James optimization step. The bootstrap method is used for variance estimation, and the performance of our method is corroborated in simulation. We illustrate our method through an analysis of a multicenter selective lymphadenectomy trial for melanoma. [PUBLICATION ABSTRACT] This paper studies a semiparametric accelerated failure time mixture model for estimation of a biological treatment effect on a latent subgroup of interest with a time-to-event outcome in randomized clinical trials. Latency is induced because membership is observable in one arm of the trial and unidentified in the other. This method is useful in randomized clinical trials with all-or-none noncompliance when patients in the control arm have no access to active treatment and in, for example, oncology trials when a biopsy used to identify the latent subgroup is performed only on subjects randomized to active treatment. We derive a computational method to estimate model parameters by iterating between an expectation step and a weighted Buckley-James optimization step. The bootstrap method is used for variance estimation, and the performance of our method is corroborated in simulation. We illustrate our method through an analysis of a multicenter selective lymphadenectomy trial for melanoma. This article studies a semiparametric accelerated failure time mixture model for estimation of a biological treatment effect on a latent subgroup of interest with a time-to-event outcome in randomized clinical trials. Latency is induced because membership is observable in one arm of the trial and unidentified in the other. This method is useful in randomized clinical trials with all-or-none noncompliance when patients in the control arm have no access to active treatment and in, for example, oncology trials when a biopsy used to identify the latent subgroup is performed only on subjects randomized to active treatment. We derive a computational method to estimate model parameters by iterating between an expectation step and a weighted Buckley-James optimization step. The bootstrap method is used for variance estimation, and the performance of our method is corroborated in simulation. We illustrate our method through an analysis of a multicenter selective lymphadenectomy trial for melanoma. This article studies a semiparametric accelerated failure time mixture model for estimation of a biological treatment effect on a latent subgroup of interest with a time-to-event outcome in randomized clinical trials. Latency is induced because membership is observable in one arm of the trial and unidentified in the other. This method is useful in randomized clinical trials with all-or-none noncompliance when patients in the control arm have no access to active treatment and in, for example, oncology trials when a biopsy used to identify the latent subgroup is performed only on subjects randomized to active treatment. We derive a computational method to estimate model parameters by iterating between an expectation step and a weighted Buckley-James optimization step. The bootstrap method is used for variance estimation, and the performance of our method is corroborated in simulation. We illustrate our method through an analysis of a multicenter selective lymphadenectomy trial for melanoma.This article studies a semiparametric accelerated failure time mixture model for estimation of a biological treatment effect on a latent subgroup of interest with a time-to-event outcome in randomized clinical trials. Latency is induced because membership is observable in one arm of the trial and unidentified in the other. This method is useful in randomized clinical trials with all-or-none noncompliance when patients in the control arm have no access to active treatment and in, for example, oncology trials when a biopsy used to identify the latent subgroup is performed only on subjects randomized to active treatment. We derive a computational method to estimate model parameters by iterating between an expectation step and a weighted Buckley-James optimization step. The bootstrap method is used for variance estimation, and the performance of our method is corroborated in simulation. We illustrate our method through an analysis of a multicenter selective lymphadenectomy trial for melanoma. |
| Author | Li, G. Altstein, L. |
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| Snippet | This article studies a semiparametric accelerated failure time mixture model for estimation of a biological treatment effect on a latent subgroup of interest... Summary This article studies a semiparametric accelerated failure time mixture model for estimation of a biological treatment effect on a latent subgroup of... Summary This article studies a semiparametric accelerated failure time mixture model for estimation of a biological treatment effect on a latent subgroup of... This paper studies a semiparametric accelerated failure time mixture model for estimation of a biological treatment effect on a latent subgroup of interest... |
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| SubjectTerms | All-or-none noncompliance biological treatment BIOMETRIC METHODOLOGY Biometrics biometry biopsy Buckley-James estimator Censored data Censorship Clinical trials Competing risks Computer Simulation Data Interpretation, Statistical Disease-Free Survival EM algorithm Estimation methods Humans Lymph node excision Lymph Node Excision - standards melanoma Melanoma - surgery Modeling Models, Statistical Nonproportional hazards model Parametric models Patient Compliance patients randomized clinical trials Randomized Controlled Trials as Topic - methods Simulation Simulations Skin cancer Statistical estimation Statistical models Treatment efficacy variance |
| Title | Latent Subgroup Analysis of a Randomized Clinical Trial through a Semiparametric Accelerated Failure Time Mixture Model |
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