Utility-Assessment for Co-Morbidities in Prostate Cancer

描述（由申请人提供）：鉴于我国人口老龄化，前列腺癌和其他与年龄相关的疾病的患病率正在迅速增加。衡量合并症对生活质量（QOL）的影响将是评估未来癌症治疗的关键。生活质量以效用评估的形式用于成本效益分析（CEA），在前列腺癌的决策中至关重要。然而，直接询问合并症或关节健康状况（JS）的公用事业可能会造成重大的答辩负担。基于单一运行状况（SS）实用程序的JS实用程序预测模型将是非常有价值的。目前用于从SS效用中估计JS效用的预测模型与我们关于前列腺癌风险男性的数据不一致。我们在之前的研究中开发并验证了一个简单的线性指数模型，该模型对现有的其他模型进行了改进。考虑到效用损失l（.）相对于完美健康的效用，我们的线性指数是：E{l(JS)} = 0.05 + 0.72max{l(SS1),l(SS2)} + 0.33min{l(SS1),l(SS2)} - 0.18l(SS1)l（SS2）。它的参数有一个理论的心理学基础，更重权重的JS的更严重的组成部分，表明该模型可能是相当普遍的。需要测试该模型的扩展以预测JS实用程序。使用与前列腺癌风险男性相关的普遍健康状态的效用的个人水平数据，我们建议以三种方式测试我们的新模型的普遍性。首先，我们将测试老年男性中普遍存在的疾病特异性和非特异性合并症。其次，我们将对每种共病的严重程度进行更大范围的测试。第三，我们在更普通的男性人群中进行测试，包括那些没有前列腺癌但仍处于我们将要询问的风险年龄范围内的人。这些附加场景包括三个不同的运行状况域和每个域中的两个严重性。更具体地说，新的合并症包括转移性疾病扩散（生化癌复发和疼痛性转移性疾病）、中风（轻度和重度）和功能丧失（对单一日常生活活动（ADL）的转移依赖和对所有6种标准ADL的依赖），每一种都将被添加到阳痿的常见前列腺癌结局中。它们将为我们的模型的可推广性提供重要的检验。我们提出了一项重要的测试，以扩展我们的预测模型，为前列腺癌高危男性建立更好的CEA生活质量测量。如果这个模型被证明更普遍，那么它可以在更广泛的背景下进行测试，包括癌症部位和其他普遍的非癌症合并症。如果它是更有限的，它仍将指导我们关于合并合并症纳入CEA的流行疾病。无论哪种方式，我们都能将与合并症相关的生活质量纳入CEA。公共卫生相关性：前列腺癌的治疗决策需要权衡疾病对生活质量损害的影响、治疗副作用以及在有患前列腺癌风险的老年男性中普遍存在的相关或不相关的合并症。如何测量同时存在的因素对生活质量的影响以纳入成本效益分析尚不清楚。我们已经开发了一个直接的预测模型，用于估计患有前列腺癌和各种合并症的男性在不同状态下的生活质量。本研究旨在检验该估计方法在常见合并症中的稳健性。

DESCRIPTION (provided by applicant): Given the aging of our population, the prevalence of both prostate cancer and other age-associated diseases is rapidly increasing. Measuring the impact of comorbidities on quality of life (QOL) will be crucial for assessing cancer treatments in the future. QOL in the form of utility-assessment for use in cost-effectiveness analysis (CEA) is of central importance in decision-making for prostate cancer. However, direct elicitation of utilities for comorbidities, or joint health states (JS), may pose substantial respondent burden. A prediction model for JS utilities based on single health state (SS) utilities would be extremely valuable. Current prediction models for estimating JS utilities from SS utilities are inconsistent with our data for men at risk for prostate cancer. We developed and validated a straightforward linear index model in a prior study, which improves upon other current models. Considering utilities in terms of utility losses l(.) relative to prefect health, our linear index is: E{l(JS)} = 0.05 + 0.72max{l(SS1),l(SS2)} + 0.33min{l(SS1),l(SS2)} - 0.18l(SS1)l(SS2). Its parameters have a theoretical psychological basis in more heavily weighting the more severe component of a JS, suggesting the model may be quite general. Testing the extension of this model for predicting JS utilities is needed. Using individual-level data on utilities for prevalent health states associated with men at risk for prostate cancer, we propose to test the generalizability of our new model in three ways. First, we will test across both disease-specific and non-specific comorbidities prevalent in older men. Second, we will test across a wider range of severities in each comorbid condition. Third, we test in a more general male population to include those without prostate cancer but still in the age range to be at risk for the conditions we will ask about. These additional scenarios include three different health domains and two severities within each domain. More specifically, the new comorbidities include metastatic disease spread (biochemical cancer recurrence and painful metastatic disease), stroke (mild and severe), and functional losses (dependence in the single activity of daily living (ADL) of transferring and dependence in all 6 standard ADLs), each of which will be added to the common prostate cancer outcome of impotence. They will provide an important test of the generalizability of our model. We propose an important test of the extension of our prediction model for establishing better QOL measurement for CEA for men at-risk for prostate cancer. If this model proves more general, it could next be tested in an even broader context across cancer sites and other prevalent non-cancer comorbidities. If it is more limited, it will still guide us regarding the incorporation of comorbidities into CEA for prevalent diseases. Either way, we will be far more able to incorporate QOL associated with comorbidities into CEA. PUBLIC HEALTH RELEVANCE: Prostate cancer treatment decisions require weighing the impact of damages to quality of life from the disease, treatment side-effects, and related or unrelated comorbidities that are prevalent in older men at risk for the disease. How to measure the effects of simultaneous factors on quality of life for incorporation into cost- effectiveness analysis is not yet clear. We have developed a straight-forward prediction model for estimating quality of life for men in various states of having and treating prostate cancer and various comorbidities. This study proposes to test the robustness of this estimation methodology across common comorbidities.