Utility-Assessment for Co-Morbidities in Prostate Cancer

描述(申请人提供)：由于我们的人口老龄化，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。测量合并症对生活质量(QOL)的影响将是未来评估癌症治疗的关键。以效用评估的形式用于成本-效果分析(CEA)的生活质量在前列腺癌的决策中具有核心重要性。然而，直接引出共病或联合健康状态(JS)的公用事业可能会造成很大的应诉负担。基于单一健康状态(SS)效用的JS效用预测模型将是非常有价值的。目前从SS公用事业估算JS效用的预测模型与我们针对前列腺癌高危男性的数据不一致。在之前的研究中，我们开发并验证了一个简单的线性指数模型，该模型改进了现有的其他模型。从公用事业损失的角度考虑公用事业L(.)相对于完全健康，我们的线性指数为：E{L(JS)}=0.0 5+0.72 max{L(SS1)，L(SS2)}+0.33min{L(SS1)，L(SS2)}-0.181(SS1)L(SS2)。它的参数有一个理论心理学基础，即对JS中更严重的组成部分进行更大的权重，这表明该模型可能非常通用。需要对该模型的扩展进行测试，以预测JS效用。使用与前列腺癌风险男性相关的流行健康状态效用的个人水平数据，我们建议通过三种方式测试我们的新模型的普适性。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共病。其次，我们将在每种并存情况下测试更广泛的严重度。第三，我们在更一般的男性人群中进行测试，以包括那些没有前列腺癌但仍处于我们将询问的疾病风险范围内的人。这些附加方案包括三个不同的健康域和每个域中的两个严重性。更具体地说，新的并存包括转移性疾病传播(生化癌症复发和痛苦的转移性疾病)、中风(轻度和严重)以及功能丧失(对转移的单一日常生活能力(ADL)的依赖和所有6种标准ADL的依赖)，其中每一种都将被添加到前列腺癌常见的阳萎结果中。它们将对我们的模型的普适性进行重要的测试。我们建议对我们的预测模型的扩展进行一项重要的测试，以便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是更普遍的，下一步可能会在更广泛的背景下，在癌症部位和其他流行的非癌症并存的情况下进行测试。如果它是更有限的，它仍然将指导我们在流行疾病的CEA中纳入共病。无论哪种方式，我们都将更有能力将与共病相关的生活质量纳入CEA。 公共卫生相关性：前列腺癌的治疗决定需要权衡疾病对生活质量的损害的影响、治疗副作用以及相关或不相关的并发症，这些疾病在有患病风险的老年男性中很普遍。如何衡量同时因素对生活质量的影响，以便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，用于评估患有和治疗前列腺癌以及各种并发症的不同状态的男性的生活质量。这项研究建议在常见的并存情况下测试这种估计方法的稳健性。

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.