| scholarly article | Q13442814 |
| P356 | DOI | 10.1002/PST.1684 |
| P698 | PubMed publication ID | 25914330 |
| P50 | author | Tzu-Pin Lu | Q60496620 |
| P2093 | author name string | James J Chen | |
| P2860 | cites work | Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival Forests | Q22673963 |
| Class-imbalanced classifiers for high-dimensional data | Q26822875 | ||
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| The challenge of subgroup analyses--reporting without distorting | Q28235374 | ||
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| A framework for the use of genomics data at the EPA. | Q31060259 | ||
| Clinical trial designs for predictive marker validation in cancer treatment trials | Q33213015 | ||
| Development of biomarker classifiers from high-dimensional data | Q33426749 | ||
| Performance of novel kidney biomarkers in preclinical toxicity studies | Q33917932 | ||
| Subgroup identification from randomized clinical trial data | Q33981752 | ||
| Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: criteria, validation, strategies | Q34192717 | ||
| Power for tests of interaction: effect of raising the Type I error rate | Q35874880 | ||
| Idiosyncratic drug hepatotoxicity | Q36148442 | ||
| Blood gene expression signatures predict exposure levels. | Q36156901 | ||
| Why drugs fail--a study on side effects in new chemical entities. | Q36296698 | ||
| Development of the 21-gene assay and its application in clinical practice and clinical trials | Q37079600 | ||
| Developing and Validating Continuous Genomic Signatures in Randomized Clinical Trials for Predictive Medicine | Q37096663 | ||
| Clinical trial designs for predictive biomarker validation: theoretical considerations and practical challenges. | Q37325013 | ||
| Adaptive Clinical Trial Designs for Simultaneous Testing of Matched Diagnostics and Therapeutics | Q37368881 | ||
| Integrating biomarkers in clinical trials | Q37853183 | ||
| Clinical trials for predictive medicine | Q38020126 | ||
| Pharmacogenomic Biomarkers for Personalized Medicine | Q38112822 | ||
| A regulatory perspective on essential considerations in design and analysis of subgroups when correctly classified | Q38176063 | ||
| Tests for interaction in epidemiologic studies: A review and a study of power | Q40164602 | ||
| Analysis and interpretation of treatment effects in subgroups of patients in randomized clinical trials | Q41187186 | ||
| Identification and Elucidation of the Biology of Adverse Events: The Challenges of Safety Assessment and Translational Medicine | Q46018188 | ||
| The liver toxicity biomarker study: phase I design and preliminary results. | Q46145573 | ||
| Subgroup analyses in randomized trials: risks of subgroup-specific analyses; power and sample size for the interaction test | Q47423638 | ||
| Bayesian methods for design and analysis of safety trials | Q51180919 | ||
| Biomarker Classifiers for Identifying Susceptible Subpopulations for Treatment Decisions | Q51467325 | ||
| The mean does not mean as much anymore: finding sub-groups for tailored therapeutics | Q51675471 | ||
| Toxicogenomics strategies for predicting drug toxicity | Q51930594 | ||
| A network solution | Q53527430 | ||
| Gene Selection for Cancer Classification using Support Vector Machines | Q56535529 | ||
| Toxicogenomics in Predictive Toxicology in Drug Development | Q56854644 | ||
| FDA-approved drug labeling for the study of drug-induced liver injury | Q57258176 | ||
| P433 | issue | 4 | |
| P921 | main subject | biomarker | Q864574 |
| P304 | page(s) | 284-293 | |
| P577 | publication date | 2015-04-27 | |
| P1433 | published in | Pharmaceutical Statistics | Q7180744 |
| P1476 | title | Identification of drug-induced toxicity biomarkers for treatment determination | |
| P478 | volume | 14 |
| Q35863520 | Subgroup identification for treatment selection in biomarker adaptive design | cites work | P2860 |
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