Statistical methods for pediatric research

儿科研究的统计方法

• 批准号: 203137-2007
• 负责人: Platt, Robert
• 金额: $ 1.24万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=476953
• 关键词: Statistical; methods; pediatric; research

My research centres on three areas: statistical methods for meta-analysis, causal inference, and methods for pregnancy outcomes. With Khajak Ishak (PhD graduate 2006) I have done extensive work on meta-analysis of multivariate data. We demonstrated that meta-analysis of multivariate data does not always provide benefit, in particular when correlation between the multivariate outcomes comes from sources other than correlation among treatment effects (eg study heterogeneity). I will continue to work on a new model for multivariate outcomes based on a Bayesian approach to a multinomial distribution, and individual patient data (rather than summary data). Iwill implement our model using frequentist and Bayesian analytic techniques, both in simulation and in real data. As I and others have noted, pregnancy outcomes pose many statistical challenges. In particular, I am interested in the role of gestational age. Gestational age is an important perinatal predictor, but is subject to both systematic and random misclassification. I will extend my work on measurement error in gestational age to multivariate data; when information on birth length, head circumference and other characteristics are available, they should provide added information on the likelihood of errors in gestational age. In addition, I will develop a more general model for this measurement error, of which my original model represents a special case. Typical analyses of gestational age have treated the measurement error and subsequent analyses as separate problems. Bayesian approaches to survival analysis. Finally, I intend to apply the theory of causal inference based on potential outcomes (Rubin 1976, Robins 1987 and subsequent papers) in several settings. I will develop and compare methods for model selection for inverse probability weighted estimation procedures, and develop principal-stratification based methods for randomized trials with noncompliance.

我的研究集中在三个领域：荟萃分析的统计方法，因果推理和怀孕结果的方法。与Khajak Ishak（2006年博士毕业生）一起，我在多变量数据的荟萃分析方面做了大量工作。我们证明了多变量数据的荟萃分析并不总是提供益处，特别是当多变量结局之间的相关性来自治疗效果之间的相关性以外的来源时（例如研究异质性）。我将继续研究一种新的多变量结局模型，该模型基于贝叶斯方法对多项分布和个体患者数据（而不是汇总数据）进行分析。我将使用频率论和贝叶斯分析技术，在模拟和真实的数据中实现我们的模型。正如我和其他人所指出的，怀孕结果带来了许多统计挑战。特别是，我对孕龄的作用感兴趣。孕龄是一个重要的围产期预测因素，但受到系统和随机的错误分类。我将把我在胎龄测量误差方面的工作扩展到多变量数据;当关于出生身长、头围和其他特征的信息可用时，它们应该提供关于胎龄误差可能性的补充信息。 此外，我将为这种测量误差开发一个更通用的模型，其中我的原始模型代表一种特殊情况。 典型的胎龄分析将测量误差和后续分析作为单独的问题处理。生存分析的贝叶斯方法。 最后，我打算在几种情况下应用基于潜在结果的因果推理理论（Rubin 1976，Robins 1987和随后的论文）。我将开发和比较逆概率加权估计程序的模型选择方法，并开发基于主要分层的方法，用于不依从性的随机试验。