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- 3+3 (1)
- ANCOVA; cross validation; efficiency augmentation; Mayo PBC data; semi-parametric efficiency (1)
- Average toxicity score (1)
- Bayesian phase I dose finding (1)
- Bivariate survival data; Copula model; Interval sampling; Semi-stationarity. (1)
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- Case-control study; cross-validation; threshold regression model (1)
- Clinical trail; Cox model; nonparametric estimation; presonalized medicine; perturbation-resampling method; stratified medicine; subgroup analysis; survival analysis (1)
- Cross-training-evaluation; Personalized medicine; Prediction; Stratified medicine; Subgroup analysis; Variable selection. (1)
- Discrete-time multi-state (1)
- Dose escalation (1)
- Follow-up cycles (1)
- Logistic regression (1)
- Overdose control (1)
- Severity level (1)
- Time-to-DLT (1)
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Articles 1 - 6 of 6
Full-Text Articles in Statistical Models
Assessing Association For Bivariate Survival Data With Interval Sampling: A Copula Model Approach With Application To Aids Study, Hong Zhu, Mei-Cheng Wang
Assessing Association For Bivariate Survival Data With Interval Sampling: A Copula Model Approach With Application To Aids Study, Hong Zhu, Mei-Cheng Wang
Johns Hopkins University, Dept. of Biostatistics Working Papers
In disease surveillance systems or registries, bivariate survival data are typically collected under interval sampling. It refers to a situation when entry into a registry is at the time of the first failure event (e.g., HIV infection) within a calendar time interval, the time of the initiating event (e.g., birth) is retrospectively identified for all the cases in the registry, and subsequently the second failure event (e.g., death) is observed during the follow-up. Sampling bias is induced due to the selection process that the data are collected conditioning on the first failure event occurs within a time interval. Consequently, the …
Effectively Selecting A Target Population For A Future Comparative Study, Lihui Zhao, Lu Tian, Tianxi Cai, Brian Claggett, L. J. Wei
Effectively Selecting A Target Population For A Future Comparative Study, Lihui Zhao, Lu Tian, Tianxi Cai, Brian Claggett, L. J. Wei
Harvard University Biostatistics Working Paper Series
When comparing a new treatment with a control in a randomized clinical study, the treatment effect is generally assessed by evaluating a summary measure over a specific study population. The success of the trial heavily depends on the choice of such a population. In this paper, we show a systematic, effective way to identify a promising population, for which the new treatment is expected to have a desired benefit, using the data from a current study involving similar comparator treatments. Specifically, with the existing data we first create a parametric scoring system using multiple covariates to estimate subject-specific treatment differences. …
Bayesian Phase I Dose Finding In Cancer Trials, Lin Yang
Bayesian Phase I Dose Finding In Cancer Trials, Lin Yang
Dissertations & Theses (Open Access)
This dissertation explores phase I dose-finding designs in cancer trials from three perspectives: the alternative Bayesian dose-escalation rules, a design based on a time-to-dose-limiting toxicity (DLT) model, and a design based on a discrete-time multi-state (DTMS) model.
We list alternative Bayesian dose-escalation rules and perform a simulation study for the intra-rule and inter-rule comparisons based on two statistical models to identify the most appropriate rule under certain scenarios. We provide evidence that all the Bayesian rules outperform the traditional ``3+3'' design in the allocation of patients and selection of the maximum tolerated dose.
The design based on a time-to-DLT model …
On The Covariate-Adjusted Estimation For An Overall Treatment Difference With Data From A Randomized Comparative Clinical Trial, Lu Tian, Tianxi Cai, Lihui Zhao, L. J. Wei
On The Covariate-Adjusted Estimation For An Overall Treatment Difference With Data From A Randomized Comparative Clinical Trial, Lu Tian, Tianxi Cai, Lihui Zhao, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Threshold Regression Models Adapted To Case-Control Studies, And The Risk Of Lung Cancer Due To Occupational Exposure To Asbestos In France, Antoine Chambaz, Dominique Choudat, Catherine Huber, Jean-Claude Pairon, Mark J. Van Der Laan
Threshold Regression Models Adapted To Case-Control Studies, And The Risk Of Lung Cancer Due To Occupational Exposure To Asbestos In France, Antoine Chambaz, Dominique Choudat, Catherine Huber, Jean-Claude Pairon, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Asbestos has been known for many years as a powerful carcinogen. Our purpose is quantify the relationship between an occupational exposure to asbestos and an increase of the risk of lung cancer. Furthermore, we wish to tackle the very delicate question of the evaluation, in subjects suffering from a lung cancer, of how much the amount of exposure to asbestos explains the occurrence of the cancer. For this purpose, we rely on a recent French case-control study. We build a large collection of threshold regression models, data-adaptively select a better model in it by multi-fold likelihood-based cross-validation, then fit the …
Estimating Subject-Specific Treatment Differences For Risk-Benefit Assessment With Competing Risk Event-Time Data, Brian Claggett, Lihui Zhao, Lu Tian, Davide Castagno, L. J. Wei
Estimating Subject-Specific Treatment Differences For Risk-Benefit Assessment With Competing Risk Event-Time Data, Brian Claggett, Lihui Zhao, Lu Tian, Davide Castagno, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.