Markov Chain Monte Carlo and Exact Logistic Regression

马尔可夫链蒙特卡罗和精确逻辑回归

• 批准号: 6587476
• 负责人: CYRUS R MEHTA
• 金额: $ 40.01万
• 依托单位: CYTEL, INC
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-07-20 至 2005-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6587476
• 关键词: artificial intelligence; clinical research; computer data analysis; computer program /software; computer simulation; computer system design /evaluation; human data; mathematical model; mathematics; statistics /biometry

DESCRIPTION (provided by applicant): Today, software for fitting logistic regression models to binary data belongs in the toolkit of every professional biostatistician, epidemiologist, and social scientist. A natural follow-up to this development is the adoption of exact logistic regression by mainstream biostatisticians and data analysts for any setting in which the accuracy of a statistical analysis based on large-sample maximum likelihood theory is in doubt. Cutting-edge researchers in biometry and numerous other fields have already recognized that it is necessary to supplement inference based on large-sample methods with exact inference for small, sparse and unbalanced data. The LogXact software package developed by Cytel Software Corporation fills this need. It has been used since its inception in 1993 to produce exact inferences for data generated from a wide range fields including clinical trials, epidemiology, disease surveillance, insurance, criminology, finance, accounting, sociology and ecology. In all these applications exact logistic regression was adopted because the limitations of the corresponding asymptotic procedures were clearly recognized in advance by the investigators and the exact inference was computationally feasible. But most of the time it will not be obvious whether asymptotic or exact methods are applicable. Ideally one would prefer to run both types of analyses if there is any doubt about the appropriateness of the asymptotic inference. However, because of the computational limits of the exact algorithms, investigators are currently inhibited from attempting the exact analysis. There is uncertainty about the how long the computations will take and even whether they will produce any results at all before the computer runs out of memory. The current project eliminates this uncertainty by introducing a new generation of numerical algorithms that utilize network based Monte Carlo rejection sampling. The Phase 1 progress report has demonstrated that these new algorithms can speed up the computations by factors of 50 to 1000 relative to what is currently available in LogXact. More importantly they can predict how long a job will take so that the user may decide whether to proceed at once or at a better time. The Phase 2 effort aims to incorporate this new generation of computing algorithms into future versions of LogXact.

描述（由申请人提供）：今天，适合二进制数据的逻辑回归模型的软件属于每个专业生物统计学家，流行病学家和社会科学家的工具包。这一发展的自然后续行动是主流生物统计学家和数据分析师采用精确的逻辑回归，以疑问基于大样本最大似然理论的统计分析的准确性。生物特征和许多其他领域的尖端研究人员已经认识到，有必要根据大样本方法进行补充推断，并确切推断小，稀疏和不平衡的数据。 Cytel Software Corporation开发的LogXact软件包填补了这一需求。自1993年成立以来，它已被用来从广泛领域产生的数据提供确切的推断，包括临床试验，流行病学，疾病监测，保险，犯罪学，财务，会计，社会学和生态学。在所有这些应用中，都采用了精确的逻辑回归，因为研究人员在相应的渐近程序的局限性清楚地确定了，并且确切的推断在计算上是可行的。但是在大多数情况下，是否适用渐近方法或确切方法是不明显的。理想情况下，如果对渐近推理的适当性有任何疑问，则希望进行两种类型的分析。但是，由于确切算法的计算限制，目前抑制了研究人员尝试确切分析。对于计算的时间以及它们是否会在计算机耗尽内存之前是否会产生任何结果，存在不确定性。当前的项目通过引入新一代的数值算法来消除这种不确定性，该算法利用基于网络的蒙特卡洛排斥采样。第1阶段进度报告表明，相对于当前在logXact中可用的算法，这些新算法可以通过50至1000的因素加快计算的速度。更重要的是，他们可以预测工作需要多长时间，以便用户可以决定一次还是更好的时间进行。第2阶段的努力旨在将这种新一代计算算法纳入未来版本的logxact。