PURPOSE: The Quality-adjusted Time Without Symptoms of disease and Toxicity (Q-TWiST) analysis method is frequently applied to evaluating outcomes in cancer clinical trials, but there is little information on what constitutes a clinically important difference (CID). We reviewed the Q-TWiST, health-related quality of life (HRQL) and utility measurement literature to develop recommendations for CID for the Q-TWiST. We also provide recommendations for measuring health utilities and for the design of Q-TWiST studies. METHODS: The English language literature was searched between 1986 and 2003 for Q-TWiST studies in oncology. We estimated the percent differences between treatments based on median follow-up duration for overall, progression-free and quality-adjusted survival. We also reviewed the relevant HRQL and utility literature on clinical importance. RESULTS: The overall differences between treatments for most (56%) of the observed, published values for Q-TWiST analyses ranged between 12% and 19%. Three-fourths of the Q-TWiST studies had gains in survival of 12%-17%, while differences in progression-free survival ranged from 12% to 26%. Studies that have evaluated the clinical importance of changes in HRQL scores suggest that changes of 5%-10% are clinically meaningful, and other research suggests that 0.5 standard deviation is a reasonable threshold for changes in HRQL for chronic diseases. Similarly, one guideline from the health state utility literature is that a 5%-10% difference in standard gamble utility scores is clinically important. Various sources are available for health utilities for Q-TWiST studies and the most valid are derived from patients or the general public, although most studies rely on sensitivity analyses with no collection of utilities. CONCLUSIONS: We recommend that the CID for Q-TWiST is 10% of overall survival in a study, and differences of 15% are clearly clinically important. If less is known about a specific treatment and/or disease area, the CID should be greater than 5% but not more than 10% in planning sample size and statistical power. These CID estimates should be interpreted with caution, pending confirmation in future studies by direct patient assessment of the clinically relevant health states for Q-TWiST.
PURPOSE: The Quality-adjusted Time Without Symptoms of disease and Toxicity (Q-TWiST) analysis method is frequently applied to evaluating outcomes in cancer clinical trials, but there is little information on what constitutes a clinically important difference (CID). We reviewed the Q-TWiST, health-related quality of life (HRQL) and utility measurement literature to develop recommendations for CID for the Q-TWiST. We also provide recommendations for measuring health utilities and for the design of Q-TWiST studies. METHODS: The English language literature was searched between 1986 and 2003 for Q-TWiST studies in oncology. We estimated the percent differences between treatments based on median follow-up duration for overall, progression-free and quality-adjusted survival. We also reviewed the relevant HRQL and utility literature on clinical importance. RESULTS: The overall differences between treatments for most (56%) of the observed, published values for Q-TWiST analyses ranged between 12% and 19%. Three-fourths of the Q-TWiST studies had gains in survival of 12%-17%, while differences in progression-free survival ranged from 12% to 26%. Studies that have evaluated the clinical importance of changes in HRQL scores suggest that changes of 5%-10% are clinically meaningful, and other research suggests that 0.5 standard deviation is a reasonable threshold for changes in HRQL for chronic diseases. Similarly, one guideline from the health state utility literature is that a 5%-10% difference in standard gamble utility scores is clinically important. Various sources are available for health utilities for Q-TWiST studies and the most valid are derived from patients or the general public, although most studies rely on sensitivity analyses with no collection of utilities. CONCLUSIONS: We recommend that the CID for Q-TWiST is 10% of overall survival in a study, and differences of 15% are clearly clinically important. If less is known about a specific treatment and/or disease area, the CID should be greater than 5% but not more than 10% in planning sample size and statistical power. These CID estimates should be interpreted with caution, pending confirmation in future studies by direct patient assessment of the clinically relevant health states for Q-TWiST.
Authors: Mirjam A G Sprangers; Carol M Moinpour; Timothy J Moynihan; Donald L Patrick; Dennis A Revicki Journal: Mayo Clin Proc Date: 2002-06 Impact factor: 7.616
Authors: S K Parsons; S Gelber; B F Cole; Y Ravindranath; A Ogden; A M Yeager; M Chang; J Shuster; H J Weinstein; R D Gelber Journal: J Clin Oncol Date: 1999-07 Impact factor: 44.544
Authors: Kerry L Kilbridge; Bernard F Cole; John M Kirkwood; Frank G Haluska; Michael A Atkins; John C Ruckdeschel; Dana E Sock; Robert F Nease; Jane C Weeks Journal: J Clin Oncol Date: 2002-03-01 Impact factor: 44.544
Authors: Janel Hanmer; Dasha Cherepanov; Mari Palta; Robert M Kaplan; David Feeny; Dennis G Fryback Journal: Med Decis Making Date: 2015-08-27 Impact factor: 2.583
Authors: Taroh Satoh; Yung-Jue Bang; Evgeny A Gotovkin; Yasuo Hamamoto; Yoon-Koo Kang; Vladimir M Moiseyenko; Atsushi Ohtsu; Eric Van Cutsem; Nedal Al-Sakaff; Alexa Urspruch; Julie Hill; Harald A Weber; Hyun-Cheol Chung Journal: Oncologist Date: 2014-06-20
Authors: David F McDermott; Ruchit Shah; Komal Gupte-Singh; Javier Sabater; Linlin Luo; Marc Botteman; Sumati Rao; Meredith M Regan; Michael Atkins Journal: Qual Life Res Date: 2018-09-06 Impact factor: 4.147
Authors: William Furlong; Charlene Rae; David Feeny; Richard D Gelber; Caroline Laverdiere; Bruno Michon; Lewis Silverman; Stephen Sallan; Ronald Barr Journal: Pediatr Blood Cancer Date: 2012-01-31 Impact factor: 3.167
Authors: Arthur S Zbrozek; Gary Hudes; Donna Levy; Andrew Strahs; Anna Berkenblit; Robert DeMarinis; Shreekant Parasuraman Journal: Pharmacoeconomics Date: 2010 Impact factor: 4.981
Authors: Jennifer L Beaumont; John M Salsman; Jose Diaz; Keith C Deen; Lauren McCann; Thomas Powles; Michelle D Hackshaw; Robert J Motzer; David Cella Journal: Cancer Date: 2016-01-27 Impact factor: 6.860
Authors: Jeff A Sloan; Daniel J Sargent; Paul J Novotny; Paul A Decker; Randolph S Marks; Heidi Nelson Journal: J Pain Symptom Manage Date: 2013-11-15 Impact factor: 3.612