Statistical methods for comparative treatment effectiveness using claims data

使用索赔数据比较治疗效果的统计方法

• 批准号: 8037531
• 负责人: PINGSHENG WU
• 金额: $ 87.03万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-27 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8037531
• 关键词: Address; Agonist; Antidepressive Agents; Asthma; Benefits and Risks; Characteristics; Child; Clinical; Data; Data Set; Dose; Equilibrium; Event; Face; Investigation; Life; Methods; Modeling; Nature; Observational Study; Outcome; Patients; Performance; Perinatal; Pharmaceutical Preparations; Physicians; Pregnancy; Probability; Public Health; Research; Research Design; Research Personnel; Risk; Safety; Scoring Method; Simulate; Statistical Methods; Statistical Models; Techniques; Time; Treatment Effectiveness; Work; adverse outcome; analytical method; comparative; comparative effectiveness; comparison group; effectiveness research; experience; improved; insight; research study; response; simulation; theories; therapy duration; tool

DESCRIPTION (provided by applicant): Significance. Propensity score methods with a dichotomous treatment (exposed v. unexposed) have been extensively evaluated and proven to be an effective tool for reducing bias and balancing the distribution of confounders between comparison groups in observational studies. Propensity score methods to handle multi- level exposure, with and without ordering, and continuous exposures have been developed. However, there is very limited experience in the application of these methods in either simulated or empirical studies. Important information in establishing both the benefits and risks of medications includes the estimation of a dose effect, duration effect, and multiple medication effect in comparative effectiveness and safety investigations. Real- world medication use involves all three of these issues, and researchers need a thoroughly evaluated analytical method to apply in such scenarios. We will determine the generalizability and practical insight of the performance of propensity scores when considering these three important issues: medication dose, multiple medications, and continuous duration of therapy. This project addresses an important gap in the current analytical approach to evaluating the comparative effectiveness of medications, and will enhance and improve the quality of observational comparative effectiveness research. Specific Aims. We will (1) Evaluate the performance of propensity scores when considering a dose-response relationship of medication exposure and the effects of multiple medication exposures; and (2) Evaluate the performance of propensity scores when considering the effects of continuously varying duration of therapy. Research Design. We will conduct both simulation studies and analyses of real data to answer comparative effectiveness research questions for both specific aims. A propensity score is defined as the conditional probability that a patient receives a particular treatment given all her/his other observed covariates. Such treatment can vary with respect to type, dose, and duration of therapy. Appropriate statistical models will be used to estimate this probability for each subject according to the nature of her/his exposure. Dose and duration effects, and difference in effect among medications will be further estimated. Performance of four common applications of propensity scores and four scenarios for which propensity scores are often applied will be evaluated. Results will be further compared with results from conventional multivariable regression models. Impact. The project will address critically important issues encountered in observational comparative effective research. It will have a wide public health impact by providing researchers with a thoroughly evaluated analytical approach to conduct such studies, and will enhance and improve the quality of observational comparative effectiveness research. . PUBLIC HEALTH RELEVANCE: This project will provide a critically needed and thoroughly evaluated statistical technique to conduct comparative effectiveness research studies using observational data. We will evaluate the application of propensity score methods to common situations when medication dose effect, duration effect, and multiple medication exposures need to be considered. This project addresses an important gap in the current analytical approach to medication comparative effectiveness, and will enhance and improve the quality of observational comparative effectiveness research, and thus have a wide public health impact.

描述（由申请人提供）：意义。已对二分治疗（暴露与未暴露）的倾向评分方法进行了广泛评价，并证明其是减少偏倚和平衡观察性研究中比较组间混杂因素分布的有效工具。倾向分数方法处理多级曝光，有和没有排序，和连续曝光已经发展.然而，在模拟或实证研究中应用这些方法的经验非常有限。确定药物获益和风险的重要信息包括在比较有效性和安全性研究中估计剂量效应、持续时间效应和多种药物效应。真实的世界的药物使用涉及所有这三个问题，研究人员需要一个彻底评估的分析方法，以适用于这种情况。我们将在考虑这三个重要问题时确定倾向评分的普遍性和实际洞察力：药物剂量、多种药物和连续治疗时间。该项目解决了目前评价药物比较有效性的分析方法中的一个重要差距，并将加强和提高观察性比较有效性研究的质量。具体目标。我们将（1）在考虑药物暴露的剂量-反应关系和多次药物暴露的影响时评价倾向评分的性能;（2）在考虑治疗持续时间连续变化的影响时评价倾向评分的性能。研究设计。我们将对真实的数据进行模拟研究和分析，以回答这两个特定目标的比较有效性研究问题。倾向评分定义为患者接受特定治疗的条件概率，考虑到她/他的所有其他观察到的协变量。这种治疗可以根据治疗的类型、剂量和持续时间而变化。将使用适当的统计模型根据每例受试者的暴露性质估计其概率。将进一步估计剂量和持续时间效应以及药物之间的效应差异。将评估倾向评分的四种常见应用和倾向评分经常应用的四种情景的性能。结果将进一步与传统的多变量回归模型的结果进行比较。冲击该项目将解决观察性比较有效研究中遇到的至关重要的问题。它将通过为研究人员提供进行此类研究的全面评估分析方法，产生广泛的公共卫生影响，并将加强和提高观察性比较有效性研究的质量。. 公共卫生相关性：该项目将提供一种迫切需要的、经过全面评估的统计技术，以利用观察数据进行比较有效性研究。我们将评估倾向评分法在需要考虑药物剂量效应、持续时间效应和多种药物暴露的常见情况下的应用。该项目解决了目前药物比较有效性分析方法中的一个重要差距，并将加强和提高观察性比较有效性研究的质量，从而产生广泛的公共卫生影响。