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Quality-Adjusted Time without Symptoms or Toxicity (Q-TWiST)
Quality-adjusted time without symptoms or toxicity (Q-TWiST) is an outcome measure for cancer clinical trials, which was developed in the mid-1980s by Richard Gelber and Aron Goldhirsch to evaluate adjuvant chemotherapy. It is an adaptation of the QALY, the quality-adjusted life year. It is calculated as follows:

where TOX is the months spent with any burden of subjective treatment side effects, REL is the months following disease relapse, TWiST is defined as time without symptoms of disease and toxicity of treatment, and u is a utility coefficient, taking values between 0 and 1, to represent the value, relative to TWiST, of TOX and REL, respectively, and the subscript “t” is toxicity and “r” is relapse. The developers proposed to use arbitrary values for u and to show the effect of different values by performing threshold analyses, also called sensitivity analyses. Such analyses result in combinations of utilities for TOX and REL (relative to TWiST) whereby one treatment strategy is superior to the other. Clinicians should then assess these utilities with their patients to decide which treatment is superior. An example is Q-TWiST for radiation therapy in the treatment of patients with poor-prognosis resectable rectal cancer:

Outcome Measurement in Oncology
TWiST
Overall survival time is the most definitive end-point used to evaluate treatment effectiveness for cancer patients. Other measures such as tumor-free interval, duration of response, or time to progression of disease are also considered for making therapeutic decisions. A value judgment is often made by weighing benefits in terms of these measures against the risks of undesirable side effects of treatment. In the second half of the 1980s, Gelber and Goldhirsch developed a quality-of-life-oriented endpoint for assessing adjuvant therapies in oncology. This end-point was obtained by subtracting the amount of time of poor quality of life from each unit time interval to adjust the measure of benefits. It reflected the amount of good quality time enjoyed by the patients. Specifically, the time without symptomatic relapse of cancer was adjusted by subtracting units of time during which toxic effects of treatment were experienced. The measure defined time without symptoms of disease and toxicity of treatment (TWiST). It was calculated for each patient by subtracting from overall survival periods of time during which treatment or disease reduced quality of life. These periods included months with any burden of subjective treatment side effects (TOX) and all months following disease relapse (REL). Average TWiST could be calculated for several treatments and compared over time to see when (if ever) after start of treatment the risk-benefit ratio began for a treatment with more early toxicity.
Quality-Adjusted Survival: Q-TWiST
The all-or-none analysis of TWiST was deemed somewhat unrealistic by the developers, as it assigned no value to both the period of life with toxicity and the period following relapse. A refinement was created to TWiST to include in the analysis times spent with toxicity or relapse but with intermediate weightings based on their value relative to TWiST.
Q-TWiST and QALY are equivalent concepts, and depending on the elicitation of the utility coefficient u and on the way the data are analyzed they will return similar or different results. A utility is defined as the level of desirability that people associate with a particular outcome. It is a cardinal number that represents the strength of an individual's preference for a particular outcome when faced with uncertainty. Utilities are assigned to each outcome, on a scale that is established by assigning a value of 1 to the state of optimal health and a value of 0 to death. In QALYs, each year of survival is multiplied by its utility, and the thus adjusted life years are summed. In Q-TWiST, life years are assigned to specific health state categories (TOX, REL, TWiST) and multiplied with a fixed utility for that category. The categorization may make the analysis more appealing to clinicians.
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