Empirical Studies of the Development and Diffusion of Medical Technologies

医疗技术发展和传播的实证研究

• 批准号: 8627379
• 负责人: HEIDI Lie WILLIAMS
• 金额: $ 28.42万
• 依托单位: NATIONAL BUREAU OF ECONOMIC RESEARCH
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-30 至 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8627379
• 关键词: Affect; Antineoplastic Agents; Award; Basic Science; Cancer Patient; Cause of Death; Classification; Clinical Trials; Code; Cost-Benefit Analysis; Costs and Benefits; Data; Data Set; Development; Diagnosis; Diagnostic; Diffusion; Disease; Economics; Enrollment; Genes; Genotype; Goals; Government; Grant; Healthcare Market; Hereditary Disease; Human; Human Genome; Incentives; Investments; Legal patent; Length; Licensing; Light; Link; Litigation; Malignant Neoplasms; Marketing; MeSH Thesaurus; Measures; Medical; Medical Technology; Methods; National Cancer Institute; Oncogenes; Organ; Paper; Patients; Pharmacologic Substance; Phenotype; Physician Data Query; Policies; Prostate; PubMed; Public Policy; Randomized; Registries; Research; Rights; Scientific Advances and Accomplishments; Shapes; Source; Staging; System; Technology; Text; Theoretical model; Trademark; Work; base; cancer therapy; cost; design; drug development; innovation; new technology; public health relevance; response

OTHER PROJECT INFORMATION - Project Summary/Abstract Empirical Studies of the Development and Diffusion of Medical Technologies Technological change is a central feature of health care markets, and over the past few decades has revolutionized the treatment of many medical conditions. Although breakthrough scientific advances are an important driver of the pace of medical innovation, market incentives - such as those provided by the patent system - are often a critical determinant of which potential technologies successfully make the transition 'from the lab to the market.' The key contribution of this project is to develop new data (Aims 1 and 3) and new empirical methods (Aims 2 and 4) to investigate how patents shape the development and diffusion of medical innovations. Specifically, we apply these new data and new methods to estimate two relevant parameters: the extent to which patents provide incentives for the development of new technologies, and the extent to which patents on existing technologies hinder subsequent innovation. The more effective patents are in inducing research investments, the stronger the case for longer or broader patents. On the other hand, the larger the costs of patents in terms of hindering subsequent innovation, the weaker is this case. Evaluating these costs and benefits of patents is a key input into optimal policy design. To estimate the first parameter - how the prospect of stronger patent protection affects research investments - we develop a new empirical method (Aim 2) based on the following idea: because pharmaceutical firms file patents prior to starting clinical trials, shorter clinical trials grant firms longer effective patent terms. Hence, if longer patent terms encourage more research investments, we should see higher levels of research investments on treatments for patient groups that require shorter clinical trials. We apply this method to a newly developed data set measuring research investments on treatments for cancer patients (developed in Aim 1). To estimate the second parameter - how patents on existing technologies affect subsequent research investments - we develop a new empirical method (Aim 4). The key idea behind our approach is to take advantage of two facts: first, patent applications are quasi- randomly assigned to patent examiners at the US Patent and Trademark Office; and second, patent examiners differ in their likelihood of awarding a patent to any given application. We apply this method to a newly- developed data set measuring research investments related to the human genome (developed in Aim 3) and quantify the extent to which patents on human genes have hindered subsequent medical innovation. The new empirical methods (Aims 2 and 4) will be applied to two particular classes of medical technologies - cancer treatments and gene-based diagnostics and treatments - but each method can be applied in other contexts. In addition, the new data construction methods (Aims 1 and 3) - text-based matching with clinical trial enrollment lists, and MeSH-ICD matching of research investments to the relevant groups of patients - can also be applied in other contexts.

其他项目信息-项目概要/摘要 医疗技术发展与扩散的实证研究 技术变革是医疗保健市场的核心特征，在过去几十年中， 彻底改变了许多医疗条件的治疗。虽然突破性的科学进步是一个 医疗创新步伐的重要驱动力，市场激励-如专利提供的激励 系统-往往是一个关键的决定因素，潜在的技术成功地过渡到“从 从实验室到市场“该项目的主要贡献是开发新的数据（目标1和3）和新的 实证方法（目标2和4），以调查专利如何塑造医疗发展和扩散 创新。具体来说，我们应用这些新数据和新方法来估计两个相关参数： 专利在多大程度上激励了新技术的开发， 现有技术的专利会阻碍后续的创新。更有效的专利是诱导 研究投资，更长或更广泛的专利的理由越强。另一方面， 专利成本在阻碍后续创新方面，这种情况较弱。评估这些成本 专利的利益是最佳政策设计的关键投入。要估计第一个参数-如何 更强的专利保护的前景影响研究投资-我们开发了一种新的实证方法（目的 2)基于以下想法：由于制药公司在开始临床试验之前提交专利， 临床试验给予公司更长的有效专利期限。因此，如果更长的专利期限鼓励更多的研究， 投资，我们应该看到更高水平的研究投资对患者群体的治疗， 更短的临床试验我们将这种方法应用于一个新开发的测量研究投资的数据集， 癌症患者的治疗（在目标1中开发）。为了估计第二个参数-专利如何 现有技术影响后续研究投资-我们开发了一种新的实证方法（目标4）。 我们的方法背后的关键思想是利用两个事实：第一，专利申请是准， 随机分配给美国专利商标局的专利审查员;第二，专利审查员 不同的可能性授予专利给任何特定的应用程序。我们将这种方法应用于一个新的- 开发了衡量与人类基因组有关的研究投资的数据集（在目标3中开发）， 量化人类基因专利在多大程度上阻碍了随后的医学创新。新 经验方法（目标2和4）将应用于两类特殊的医疗技术--癌症 治疗和基于基因的诊断和治疗-但每种方法都可以应用于其他情况。在 此外，新的数据构建方法（目标1和3）-基于文本的匹配与临床试验入组 列表，以及MeSH-ICD研究投资与相关患者群体的匹配-也可以应用 in other contexts上下文.