, Volume 15, Issue 3, pp 411-423

Analyzing Oncology Clinical Trial Data Using the Q-TWiST Method: Clinical Importance and Sources for Health State Preference Data*

Purchase on Springer.com

$39.95 / €34.95 / £29.95*

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access


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.

*Timothy Hunt was formerly employed by Pfizer and this work was supported by a grant from Pfizer, Bridgewater, New Jersey.