Ming-Wen An1, Xinxin Dong2, Jeffrey Meyers2, Yu Han2, Axel Grothey2, Jan Bogaerts2, Daniel J Sargent2, Sumithra J Mandrekar2. 1. Department of Mathematics, Vassar College, Poughkeepsie, NY (MWA); Department of Biostatistics, Analytical Science, Takeda Pharmaceuticals, Deerfield, IL (XD); Department of Health Sciences Research, Mayo Clinic, Rochester, MN (JM, DJS, SJM); Biometrics and Data Management Department, Novartis Pharmaceuticals Corporation, East Hanover, NJ (YH); Department of Oncology, Mayo Clinic, Rochester, MN (AG); European Organisation for Research and Treatment of Cancer (EORTC), Brussels, Belgium (JB). mian@vassar.edu. 2. Department of Mathematics, Vassar College, Poughkeepsie, NY (MWA); Department of Biostatistics, Analytical Science, Takeda Pharmaceuticals, Deerfield, IL (XD); Department of Health Sciences Research, Mayo Clinic, Rochester, MN (JM, DJS, SJM); Biometrics and Data Management Department, Novartis Pharmaceuticals Corporation, East Hanover, NJ (YH); Department of Oncology, Mayo Clinic, Rochester, MN (AG); European Organisation for Research and Treatment of Cancer (EORTC), Brussels, Belgium (JB).
Abstract
BACKGROUND: We sought to develop and validate clinically relevant, early assessment continuous tumor measurement-based metrics for predicting overall survival (OS) using the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 data warehouse. METHODS: Data from 13 trials representing 2096 patients with breast cancer, non-small cell lung cancer (NSCLC), or colorectal cancer were used in a complete case analysis. Tumor measurements from weeks 0-6-12 assessments were used to evaluate the ability of slope (absolute change in tumor size from 0-6 and 6-12 weeks) and percent change (relative change in tumor size from 0-6 and 6-12 weeks) metrics to predict OS using Cox models, adjusted for average baseline tumor size. Metrics were evaluated by discrimination (via concordance or c-index), calibration (goodness-of-fit type statistics), association (hazard ratios), and likelihood (Bayesian Information Criteria), with primary focus on the c-index. All statistical tests were two-sided. RESULTS: Comparison of c-indices suggests slight improvement in predictive ability for the continuous tumor measurement-based metrics vs categorical RECIST response metrics, with slope metrics performing better than percent change metrics for breast cancer and NSCLC. However, these differences were not statistically significant. The goodness-of-fit statistics for the RECIST metrics were as good as or better than those for the continuous metrics. In general, all the metrics performed poorly in breast cancer, compared with NSCLC and colorectal cancer. CONCLUSION: Absolute and relative change in tumor measurements do not demonstrate convincingly improved overall survival predictive ability over the RECIST model. Continued work is necessary to address issues of missing tumor measurements and model selection in identifying improved tumor measurement-based metrics.
BACKGROUND: We sought to develop and validate clinically relevant, early assessment continuous tumor measurement-based metrics for predicting overall survival (OS) using the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 data warehouse. METHODS: Data from 13 trials representing 2096 patients with breast cancer, non-small cell lung cancer (NSCLC), or colorectal cancer were used in a complete case analysis. Tumor measurements from weeks 0-6-12 assessments were used to evaluate the ability of slope (absolute change in tumor size from 0-6 and 6-12 weeks) and percent change (relative change in tumor size from 0-6 and 6-12 weeks) metrics to predict OS using Cox models, adjusted for average baseline tumor size. Metrics were evaluated by discrimination (via concordance or c-index), calibration (goodness-of-fit type statistics), association (hazard ratios), and likelihood (Bayesian Information Criteria), with primary focus on the c-index. All statistical tests were two-sided. RESULTS: Comparison of c-indices suggests slight improvement in predictive ability for the continuous tumor measurement-based metrics vs categorical RECIST response metrics, with slope metrics performing better than percent change metrics for breast cancer and NSCLC. However, these differences were not statistically significant. The goodness-of-fit statistics for the RECIST metrics were as good as or better than those for the continuous metrics. In general, all the metrics performed poorly in breast cancer, compared with NSCLC and colorectal cancer. CONCLUSION: Absolute and relative change in tumor measurements do not demonstrate convincingly improved overall survival predictive ability over the RECIST model. Continued work is necessary to address issues of missing tumor measurements and model selection in identifying improved tumor measurement-based metrics.
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