Clinical Utility of Metrics Based on Tumor Measurements in Phase II Trials to Predict Overall Survival Outcomes in Phase III Trials by Using Resampling Methods.
OBJECTIVE
Phase II clinical trials inform go/no-go decisions for proceeding to phase III trials, and appropriate end points in phase II trials are critical for facilitating this decision. Phase II solid tumor trials have traditionally used end points such as tumor response defined by Response Evaluation Criteria for Solid Tumors (RECIST). We previously reported that absolute and relative changes in tumor measurements demonstrated potential, but not convincing, improvement over RECIST to predict overall survival (OS). We have evaluated the metrics by using additional measures of clinical utility and data from phase III trials.


METHODS
Resampling methods were used to assess the clinical utility of metrics to predict phase III outcomes from simulated phase II trials. In all, 2,000 phase II trials were simulated from four actual phase III trials (two positive for OS and two negative for OS). Cox models for three metrics landmarked at 12 weeks and adjusted for baseline tumor burden were fit for each phase II trial: absolute changes, relative changes, and RECIST. Clinical utility was assessed by positive predictive value and negative predictive value, that is, the probability of a positive or negative phase II trial predicting an effective or ineffective phase III conclusion, by prediction error, and by concordance index (c-index).


RESULTS
Absolute and relative change metrics had higher positive predictive value and negative predictive value than RECIST in five of six treatment comparisons and lower prediction error curves in all six. However, differences were negligible. No statistically significant difference in c-index across metrics was found.


CONCLUSIONS
The absolute and relative change metrics are not meaningfully better than RECIST in predicting OS.
Ming-Wen An, Vassar College, Poughkeepsie, NY; Yu Han, Novartis Pharmaceuticals, East Hanover NJ; Jeffrey Meyers, Daniel J. Sargent, and Sumithra J. Mandrekar, Mayo Clinic, Rochester, MN; and Jan Bogaerts, European Organisation for Research and Treatment of Cancer, Brussels, Belgium. mian@vassar.edu.
Ming-Wen
MW
An
Ming-Wen An, Vassar College, Poughkeepsie, NY; Yu Han, Novartis Pharmaceuticals, East Hanover NJ; Jeffrey Meyers, Daniel J. Sargent, and Sumithra J. Mandrekar, Mayo Clinic, Rochester, MN; and Jan Bogaerts, European Organisation for Research and Treatment of Cancer, Brussels, Belgium.
Yu
Y
Han
Ming-Wen An, Vassar College, Poughkeepsie, NY; Yu Han, Novartis Pharmaceuticals, East Hanover NJ; Jeffrey Meyers, Daniel J. Sargent, and Sumithra J. Mandrekar, Mayo Clinic, Rochester, MN; and Jan Bogaerts, European Organisation for Research and Treatment of Cancer, Brussels, Belgium.
Jeffrey P
JP
Meyers
Ming-Wen An, Vassar College, Poughkeepsie, NY; Yu Han, Novartis Pharmaceuticals, East Hanover NJ; Jeffrey Meyers, Daniel J. Sargent, and Sumithra J. Mandrekar, Mayo Clinic, Rochester, MN; and Jan Bogaerts, European Organisation for Research and Treatment of Cancer, Brussels, Belgium.
Jan
J
Bogaerts
Ming-Wen An, Vassar College, Poughkeepsie, NY; Yu Han, Novartis Pharmaceuticals, East Hanover NJ; Jeffrey Meyers, Daniel J. Sargent, and Sumithra J. Mandrekar, Mayo Clinic, Rochester, MN; and Jan Bogaerts, European Organisation for Research and Treatment of Cancer, Brussels, Belgium.
Daniel J
DJ
Sargent
Ming-Wen An, Vassar College, Poughkeepsie, NY; Yu Han, Novartis Pharmaceuticals, East Hanover NJ; Jeffrey Meyers, Daniel J. Sargent, and Sumithra J. Mandrekar, Mayo Clinic, Rochester, MN; and Jan Bogaerts, European Organisation for Research and Treatment of Cancer, Brussels, Belgium.
Sumithra J
SJ
Mandrekar
D002289:Carcinoma, Non-Small-Cell Lung / Q000401:mortality* / Q000473:pathology; D015331:Cohort Studies; D015179:Colorectal Neoplasms / Q000401:mortality* / Q000473:pathology; D020000:Decision Support Systems, Clinical; D018450:Disease Progression; D005500:Follow-Up Studies; D006801:Humans; D008175:Lung Neoplasms / Q000401:mortality* / Q000473:pathology; D015233:Models, Statistical; D009367:Neoplasm Staging; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D015996:Survival Rate; D047368:Tumor Burden