STATISTICAL METHODOLOGY FOR VACCINE EVALUATION

疫苗评估的统计方法

• 批准号: 3456293
• 负责人: Lawrence H. Moulton
• 金额: $ 8.98万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-12-01 至 1997-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3456293
• 关键词: antigen antibody reaction; data collection methodology /evaluation; drug design /synthesis /production; drug quality /standard; drug screening /evaluation; field study; health related legal; mathematical model; statistics /biometry; vaccines

Recent biotechnological advances have led to accelerated rates of development and improvement for a wide variety of vaccines. The need to optimize the evaluation of vaccine safety, immunogenicity, and efficacy for specified populations is becoming increasingly important. This study will extend, adapt, and apply modern statistical methods for handling detailed covariate information to the evaluation of vaccines at all phases of development. Special attention will be given to problems of correlation induced by longitudinal measurements or clustered samples. For Phase I and Phase II trials, asymmetric least squares and bootstrap techniques for percentile and nonlinear regression analyses will be investigated and extended for the analysis of repeated measures of immune system responses. Public domain software for these analyses will be developed. For prospective Phase III trials, the implementation of a variety of correlated data techniques when there has been randomization by cluster will be investigated. These methods rely, in general, on large sample asymptotic justifications; this study will develop techniques with improved second-order asymptotic properties. For Phase IV observational and case-referent studies, extensions of generalized estimating approaches to accounting for the correlation in clustered data will be studied. New methods of analyzing case-exposure data will be developed. Extensions of transmission models for handling dependence will be made and compared to current methods. Finally, propensity score methods will be applied to non-randomized vaccine studies and their properties evaluated. The results of this study will help shorten the vaccine development and licensure process by earlier needs identification and problem detection. In addition, the development of improved statistical techniques will yield more efficient study designs and precise quantification of relationships, providing savings in research costs.

最近的生物技术进步导致了加速率 多种疫苗的开发和改进。 需要 优化疫苗安全性，免疫原性和功效的评估 对于指定的人群，越来越重要。 这项研究 将扩展，适应和应用现代统计方法进行处理 详细的协变量信息完全可以评估疫苗 发展阶段。 将特别注意 纵向测量或聚类样品引起的相关性。 对于第一阶段和II期试验，不对称最小二乘和引导程序 百分位和非线性回归分析的技术将是 研究并扩展以分析免疫的重复测量 系统响应。 这些分析的公共领域软件将是 发达。 对于预期的III期试验，实施了各种 当群集随机化时，相关的数据技术 将被调查。 通常，这些方法依赖于大型样本 渐近理由；这项研究将通过 改善了二阶渐近特性。 对于第四阶段观察和病例研究研究， 广义估算方法会计相关性 将研究聚类的数据。 分析病例暴露的新方法 数据将开发。 用于处理的传输模型的扩展 将制定依赖性并将其与当前方法进行比较。 最后， 倾向评分方法将应用于非随机疫苗 研究及其特性。 这项研究的结果将有助于缩短疫苗开发，并 通过早期需要识别和问题检测的许可过程。 此外，改进的统计技术的发展将 产生更有效的研究设计和精确量化 关系，提供研究成本的节省。