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使用Q-TWiST方法分析肿瘤学临床试验数据:健康状态偏好数据的临床重要性及来源

Analyzing oncology clinical trial data using the Q-TWiST method: clinical importance and sources for health state preference data.

作者信息

Revicki Dennis A, Feeny David, Hunt Timothy L, Cole Bernard F

机构信息

Center for Health Outcomes Research, MEDTAP Institute, 7101 Wisconsin Avenue, Suite 600, Bethesda, MD 20814, USA.

出版信息

Qual Life Res. 2006 Apr;15(3):411-23. doi: 10.1007/s11136-005-1579-7.

Abstract

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.

摘要

目的

质量调整无疾病症状和毒性时间(Q-TWiST)分析方法常用于评估癌症临床试验的结果,但关于什么构成临床重要差异(CID)的信息很少。我们回顾了Q-TWiST、健康相关生活质量(HRQL)和效用测量文献,以制定Q-TWiST的CID建议。我们还提供了测量健康效用和设计Q-TWiST研究的建议。

方法

检索1986年至2003年间的英文文献,查找肿瘤学中的Q-TWiST研究。我们根据总体、无进展和质量调整生存的中位随访时间估计治疗之间的百分比差异。我们还回顾了有关临床重要性的相关HRQL和效用文献。

结果

对于大多数(56%)观察到的、已发表的Q-TWiST分析值,治疗之间的总体差异在12%至19%之间。四分之三的Q-TWiST研究的生存获益为12%至17%,而无进展生存的差异范围为12%至26%。评估HRQL评分变化临床重要性的研究表明,5%至10%的变化具有临床意义,其他研究表明,0.5标准差是慢性病HRQL变化的合理阈值。同样,健康状态效用文献中的一项指南是,标准博弈效用评分中5%至10%的差异具有临床重要性。Q-TWiST研究有多种健康效用来源,最有效的来源是患者或普通公众,尽管大多数研究依赖敏感性分析,未收集效用。

结论

我们建议,在一项研究中,Q-TWiST的CID为总生存的10%,15%的差异显然具有临床重要性。如果对特定治疗和/或疾病领域了解较少,在规划样本量和统计效力时,CID应大于5%但不超过10%。在通过患者直接评估Q-TWiST的临床相关健康状态在未来研究中得到证实时,这些CID估计值应谨慎解释。

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