Jeff A Sloan1, Daniel J Sargent1, Paul J Novotny2, Paul A Decker1, Randolph S Marks3, Heidi Nelson4. 1. Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA. 2. Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA. Electronic address: novotny@mayo.edu. 3. Department of Medical Oncology, Mayo Clinic, Rochester, Minnesota, USA. 4. Department of Colon and Rectal Surgery and Gastrointestinal Endoscopy, Mayo Clinic, Rochester, Minnesota, USA.
Abstract
CONTEXT: Quality-adjusted life year (QALY) estimation is a well-known but little used technique to compare survival adjusted for complications. Lack of calibration and interpretation guidance hinders implementation of QALY analyses. OBJECTIVES: We conducted simulation studies to assess the impact of differences in survival, toxicity rates, and utility values on QALY results. METHODS: Survival comparisons used both log-rank and Wilcoxon testing. We examined power considerations for a North Central Cancer Treatment Group Phase III lung cancer clinical trial (89-20-52). RESULTS: Sample sizes of 100 events per treatment have low power to generate a statistically significant difference in QALYs unless the toxicity rate is 44% higher in one arm. For sample sizes of 200 per arm and equal survival times, toxicity needs to be at least 38% more in one arm for the result to be statistically significant, using a utility of 0.3 for days with toxicity. Sample sizes of 300 (500)/arm provide 80% power if there is a 31% (25%) toxicity difference. If the overall survival hazard ratio between the two treatment arms is 1.25, then samples of at least 150 patients and 13% increased toxicity are necessary to have 80% power to detect QALY differences. In study 89-20-52, there was only 56% power to determine the statistical significance of the observed QALY differences, clarifying the enigmatic conclusion of no statistically significant difference in QALY despite an observed 14.5% increase in toxicity between treatments. CONCLUSION: This calibration allows researchers to interpret the clinical significance of QALY analyses and facilitates QALY inclusion in clinical trials through improved study design.
CONTEXT: Quality-adjusted life year (QALY) estimation is a well-known but little used technique to compare survival adjusted for complications. Lack of calibration and interpretation guidance hinders implementation of QALY analyses. OBJECTIVES: We conducted simulation studies to assess the impact of differences in survival, toxicity rates, and utility values on QALY results. METHODS: Survival comparisons used both log-rank and Wilcoxon testing. We examined power considerations for a North Central Cancer Treatment Group Phase III lung cancer clinical trial (89-20-52). RESULTS: Sample sizes of 100 events per treatment have low power to generate a statistically significant difference in QALYs unless the toxicity rate is 44% higher in one arm. For sample sizes of 200 per arm and equal survival times, toxicity needs to be at least 38% more in one arm for the result to be statistically significant, using a utility of 0.3 for days with toxicity. Sample sizes of 300 (500)/arm provide 80% power if there is a 31% (25%) toxicity difference. If the overall survival hazard ratio between the two treatment arms is 1.25, then samples of at least 150 patients and 13% increased toxicity are necessary to have 80% power to detect QALY differences. In study 89-20-52, there was only 56% power to determine the statistical significance of the observed QALY differences, clarifying the enigmatic conclusion of no statistically significant difference in QALY despite an observed 14.5% increase in toxicity between treatments. CONCLUSION: This calibration allows researchers to interpret the clinical significance of QALY analyses and facilitates QALY inclusion in clinical trials through improved study design.
Authors: Jeff A Sloan; Daniel J Sargent; Jed Lindman; Cristine Allmer; Delfino Vargas-Chanes; Edward T Creagan; James A Bonner; Michael J O'Connell; Robert J Dalton; Kendrith M Rowland; Burke J Brooks; John A Laurie Journal: Qual Life Res Date: 2002-02 Impact factor: 4.147
Authors: Martina Garau; Koonal K Shah; Anne R Mason; Qing Wang; Adrian Towse; Michael F Drummond Journal: Pharmacoeconomics Date: 2011-08 Impact factor: 4.981
Authors: Victor R Grann; Judith S Jacobson; Dustin Thomason; Dawn Hershman; Daniel F Heitjan; Alfred I Neugut Journal: J Clin Oncol Date: 2002-05-15 Impact factor: 44.544
Authors: E T Creagan; R J Dalton; D L Ahmann; S H Jung; R F Morton; R M Langdon; J Kugler; L J Rodrigue Journal: J Clin Oncol Date: 1995-11 Impact factor: 44.544
Authors: Brittny T Major-Elechi; Paul J Novotny; Jasvinder A Singh; James A Bonner; Amylou C Dueck; Daniel J Sargent; Axel Grothey; Jeff A Sloan Journal: Int J Stat Med Res Date: 2018-11-16