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，前列腺癌和其他年龄相关疾病的患病率正在迅速增加。测量合并症对生活质量（QOL）的影响对于未来评估癌症治疗至关重要。以效用评估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，直接引出合并症或关节健康状态（JS）的效用可能会造成巨大的应答负担。基于单一健康状态（SS）实用程序的JS实用程序预测模型将非常有价值。目前的预测模型估计JS效用从SS效用是不一致的，我们的数据为男性前列腺癌的风险。我们在先前的研究中开发并验证了一个简单的线性指数模型，该模型改进了当前的其他模型。根据效用损失l（.）相对于健康状态，我们的线性指数为：E{l（JS）} = 0.05 + 0.72max{l（SS 1），l（SS 2）} + 0.33min{l（SS 1），l（SS 2）} - 0.18l（SS 1）l（SS 2）。它的参数有一个理论心理学基础，在更重的权重更严重的成分的JS，这表明该模型可能是相当普遍的。需要测试此模型的扩展以预测JS实用程序。使用个人水平的数据与前列腺癌风险的男性相关的普遍健康状况的效用，我们建议测试我们的新模型的推广在三个方面。首先，我们将测试老年男性中常见的疾病特异性和非特异性合并症。其次，我们将在每种共病的更广泛的严重程度范围内进行测试。第三，我们在更一般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询问的疾病风险的年龄范围内的人。这些额外的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，新的合并症包括转移性疾病扩散（生化癌症复发和疼痛性转移性疾病）、卒中（轻度和重度）和功能丧失（依赖于转移的单一日常生活活动（ADL）和依赖于所有6种标准ADL），每种合并症都将被添加到常见的前列腺癌结局阳痿中。它们将为我们的模型的普遍性提供一个重要的测试。我们提出了一个重要的测试扩展我们的预测模型建立更好的生活质量测量CEA的男性前列腺癌的风险。如果这个模型被证明更普遍，那么接下来可以在癌症部位和其他流行的非癌症合并症的更广泛背景下进行测试。如果它是更有限的，它仍然将指导我们关于合并症纳入CEA的流行病。无论哪种方式，我们将更能够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的损害，治疗副作用以及在老年男性中普遍存在的相关或不相关的合并症的影响。如何衡量同时发生的因素对生活质量的影响，以便纳入成本效益分析，目前尚不清楚。我们已经开发出一种直接的预测模型，用于评估患有和治疗前列腺癌和各种合并症的各种状态的男性的生活质量。本研究旨在测试这种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.