A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations in the Presence of a Baseline Covariate
A Bayesian adaptive design for dose finding of a combination of two drugs in cancer phase I clinical trials that takes into account patients heterogeneity thought to be related to treatment susceptibility is described. The estimation of the maximum tolerated dose (MTD) curve is a function of a basel...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2018/8654173 |
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| _version_ | 1832545880484347904 |
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| author | Márcio Augusto Diniz Sungjin Kim Mourad Tighiouart |
| author_facet | Márcio Augusto Diniz Sungjin Kim Mourad Tighiouart |
| author_sort | Márcio Augusto Diniz |
| collection | DOAJ |
| description | A Bayesian adaptive design for dose finding of a combination of two drugs in cancer phase I clinical trials that takes into account patients heterogeneity thought to be related to treatment susceptibility is described. The estimation of the maximum tolerated dose (MTD) curve is a function of a baseline covariate using two cytotoxic agents. A logistic model is used to describe the relationship between the doses, baseline covariate, and the probability of dose limiting toxicity (DLT). Trial design proceeds by treating cohorts of two patients simultaneously using escalation with overdose control (EWOC), where at each stage of the trial, the next dose combination corresponds to the α quantile of the current posterior distribution of the MTD of one of two agents at the current dose of the other agent and the next patient’s baseline covariate value. The MTD curves are estimated as function of Bayes estimates of the model parameters at the end of trial. Average DLT, pointwise average bias, and percent of dose recommendation at dose combination neighborhoods around the true MTD are compared between the design that uses the covariate and the one that ignores the baseline characteristic. We also examine the performance of the approach under model misspecifications for the true dose-toxicity relationship. The methodology is further illustrated in the case of a prespecified discrete set of dose combinations. |
| format | Article |
| id | doaj-art-cb911e6dca4346c999ec6ca86006de95 |
| institution | Kabale University |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-cb911e6dca4346c999ec6ca86006de952025-02-03T07:24:32ZengWileyJournal of Probability and Statistics1687-952X1687-95382018-01-01201810.1155/2018/86541738654173A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations in the Presence of a Baseline CovariateMárcio Augusto Diniz0Sungjin Kim1Mourad Tighiouart2Biostatistics and Bioinformatics Research Center, Cedars-Sinai Medical Center 8700 Beverly Blvd, Los Angeles, CA 90048, USABiostatistics and Bioinformatics Research Center, Cedars-Sinai Medical Center 8700 Beverly Blvd, Los Angeles, CA 90048, USABiostatistics and Bioinformatics Research Center, Cedars-Sinai Medical Center 8700 Beverly Blvd, Los Angeles, CA 90048, USAA Bayesian adaptive design for dose finding of a combination of two drugs in cancer phase I clinical trials that takes into account patients heterogeneity thought to be related to treatment susceptibility is described. The estimation of the maximum tolerated dose (MTD) curve is a function of a baseline covariate using two cytotoxic agents. A logistic model is used to describe the relationship between the doses, baseline covariate, and the probability of dose limiting toxicity (DLT). Trial design proceeds by treating cohorts of two patients simultaneously using escalation with overdose control (EWOC), where at each stage of the trial, the next dose combination corresponds to the α quantile of the current posterior distribution of the MTD of one of two agents at the current dose of the other agent and the next patient’s baseline covariate value. The MTD curves are estimated as function of Bayes estimates of the model parameters at the end of trial. Average DLT, pointwise average bias, and percent of dose recommendation at dose combination neighborhoods around the true MTD are compared between the design that uses the covariate and the one that ignores the baseline characteristic. We also examine the performance of the approach under model misspecifications for the true dose-toxicity relationship. The methodology is further illustrated in the case of a prespecified discrete set of dose combinations.http://dx.doi.org/10.1155/2018/8654173 |
| spellingShingle | Márcio Augusto Diniz Sungjin Kim Mourad Tighiouart A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations in the Presence of a Baseline Covariate Journal of Probability and Statistics |
| title | A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations in the Presence of a Baseline Covariate |
| title_full | A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations in the Presence of a Baseline Covariate |
| title_fullStr | A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations in the Presence of a Baseline Covariate |
| title_full_unstemmed | A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations in the Presence of a Baseline Covariate |
| title_short | A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations in the Presence of a Baseline Covariate |
| title_sort | bayesian adaptive design in cancer phase i trials using dose combinations in the presence of a baseline covariate |
| url | http://dx.doi.org/10.1155/2018/8654173 |
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