ECONOMICS OF CANCER

描述：拟议研究的目的是：1）评估 索赔数据对估计发病率、治疗、支出的有效性 老年人肺癌、结肠癌和前列腺癌的预后; 2）分析 筛查、初始治疗、随访监测和治疗的趋势 这些癌症的复发率; 3）衡量政策因素的影响， 包括癌症设施的类型、州法律和竞争压力; 4)估计这些替代治疗模式对成本的影响， 护理的结果;和5）开发全面的模拟模型， 评估政策和治疗后果的实证研究结果 影响美国癌症管理的变化 第一个目标是国家癌症研究所的一个项目， 公告，“癌症监测使用健康索赔为基础的数据系统”。 这些研究问题将使用一个大型的国家数据库进行检查 和工具变量（IV）技术，以控制遗漏的变量 bias. 分析分为三个部分。 第一部分着重于 流行病学和经济学研究中医疗保险索赔数据的可靠性 关于癌症 第二项分析侧重于医疗方面的趋势 模式、支出和成果以及政策和人口因素 影响这些趋势。 第三部分分析工具性 变量（即，与治疗选择相关的变量，但 与影响健康结果的未观察到的因素无关，例如 患者健康状况），以确定治疗对 结果。 研究人员将研究的癌症数量从六种减少到了 到三种：肺癌，前列腺癌和结肠癌。 诊断时的癌症阶段是 分为两个基本类别：局部的和转移的。 癌 治疗分为四个阶段：检测、初始治疗、随访 对局部癌症患者的监测和后期治疗可能 与复发有关。 治疗仅限于：侵入性筛查 手术（前列腺癌和结肠癌），侵袭性手术程度 管理和使用更广泛的积极措施（手术， 化疗和/或放疗）。 主要成果指标将 生存时间和无治疗生存月数（无治疗生存月数） 住院或癌症治疗）。 支出措施将包括 保险报销总额（以及患者自付额和自付额），以及 估计费用。 （第42页表8总结了 分析中的变量）。 拟议的分析方法建立在调查人员的方法， 心导管检查的有效性分析。 因为患者 并不是随机分配到治疗组， 忽略了一些变量，比如病人健康状况中未被观察到的因素， 可能会影响治疗选择和患者结局。 他们的方法 是确定一个变量，例如获得提供 特别是治疗技术，作为一种“工具”， 分析中的治疗。 如果获得治疗与选择相关 治疗，但不与未观察到的措施，病人的健康，那么 比较不同通路患者的治疗结果 可以提供不同治疗对以下疾病的影响的无偏估计： 结果。

DESCRIPTION: The purpose of the proposed study is to: 1) Assess the validity of claims data for estimating incidence, treatment, expenditures and outcomes for lung, colon and prostate cancer in the elderly; 2) Analyze trends in screening, initial therapy, followup monitoring and treatment of recurrences for these cancers; 3) Measure the effects of policy factors, including types of cancer facilities, state laws and competitive pressures; 4) Estimate the effects of these alternative treatment patterns on costs and outcomes of care; and 5) Develop comprehensive simulation models from the empirical findings to estimate the consequences of policy and treatment changes affecting the management of cancer in the U.S. The first objective addresses a National Cancer Institute Program Announcement, "Cancer Surveillance Using Health Claims-Based Data Systems". These research questions will be examined using a large, national data base and instrumental variable (IV) techniques to control for omitted variables bias. The analysis is in three parts. The first part focuses on the reliability of Medicare claims data for epidemiologic and economic research on cancer. The second analysis focuses on trends in medical treatment patterns, expenditures and outcomes, and the policy and demographic factors influencing those trends. The third part of the analysis uses instrumental variables (i.e., variables that are correlated with choice of treatment, but uncorrelated with unobserved factors affecting health outcomes, such as patient health status) to identify the marginal effect of treatments on outcomes. The investigators have reduced the number of cancers to be studied from six to three: lung, prostate and colon cancer. Stage of cancer at diagnosis is divided into two basic categories: localized and metastatic. Cancer treatment is divided into four phases: detection, initial therapy, followup surveillance for patients with localized cancers and later treatment likely associated with recurrence. Treatments are limited to: invasive screening procedures (for prostate and colon cancer), degree of aggressive surgical management and the use of a broader range of aggressive measures (surgery, chemotherapy and/or radiation therapy). The principal outcome measures will be survival time and treatment-free survival months (months without a hospitalization or a cancer treatment). Expenditure measures will include total insured reimbursement (and patient deductibles and copayments) and estimated costs. (Table 8 on page 42 summarizes the operational definition of variables in the analysis.) The proposed analytic methods build on the investigators' approach to analysis of the effectiveness of cardiac catheterization. Because patients are not assigned randomly to treatments, the investigators are concerned that omitted variables, such as unobserved dimensions of patient health, might influence both treatment choice and patient outcomes. Their approach is to identify a variable, such as access to facilities that offer particular treatment technologies, to stand in as an "instrument" for actual treatment in the analysis. If access to treatment is correlated with choice of treatment, but not with unobserved measures of patient health, then comparing treatment outcomes for patients with different levels of access can provide an unbiased estimate of the effect of different treatments on outcomes.