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Statistical methods for community-based cancer interventions and health disparities research.

机译：基于社区的癌症干预措施和健康差异研究的统计方法。

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摘要

Health disparities associated with race, ethnicity, and socioeconomic status are consistently observed in mortality, morbidity, and other indicators of health. The slow progress in eliminating disparities in health demand that researchers consider new methods for studying these problems. Community based research has emerged as a promising new approach in the evolving field of public health as the missing link in the progression from research to practice. As such, community based interventions have the potential to be powerful tools in reducing health disparities.;Stepping outside the realm of the traditional scientific research model, and developing models that consider cultural and social factors to learn what works to prevent illness, raises a whole new set of statistical questions. These questions are often complex and diverse in their statistical methodological areas. In this dissertation, we propose new methods to answer statistical questions arising from the analysis of data from community based interventions and health disparities research.;The purpose of Chapters 1 and 2 is to develop methodologies that facilitate evaluation of multiple risk factor cancer inventions. In Chapter 1, we use conditional logistic regression to identify an optimal linear combination of multiple behavioral risk factors for cancer in a working class multi-ethnic population. In Chapter 2, we test the linearity assumption and propose methods to test the functional form of the covariates in the conditional logistic regression model, these methods are based on non-parametric smoothers. The methodology developed in Chapter 3 estimates the trend of the hazard function and when the changes in trend occur. Methods, like the ones proposed in this chapter, that estimate the overall trend of the hazard function for an entire population allow researchers and clinicians a better understanding of how changing medical practice affects the survival experience for a patient population. In Chapter 4, we propose a mixture model for ordinal outcome data with a longitudinal covariate that is subject to missingness. The proposed methodology is developed to examine the effectiveness of a tailored, telephone delivered, smoking cessation intervention for construction laborers.
机译：在死亡率，发病率和其他健康指标中始终观察到与种族，族裔和社会经济地位相关的健康差异。在消除健康差异方面进展缓慢，研究人员正在考虑研究这些问题的新方法。基于社区的研究已成为公共卫生不断发展的领域中一种有希望的新方法，因为它已成为从研究到实践发展过程中缺失的环节。因此，基于社区的干预措施有可能成为减少健康差异的有力工具。超越传统科学研究模式的范围，开发考虑文化和社会因素的模型以了解预防疾病的有效方法，这将为人们带来一个整体新的统计问题集。这些问题在其统计方法论领域通常是复杂而多样的。在本文中，我们提出了一种新的方法来回答基于社区干预和健康差异研究的数据分析所产生的统计问题。第一章和第二章的目的是开发有助于评估多种危险因素癌症发明的方法。在第一章中，我们使用条件逻辑回归来确定工人阶级多族裔人群中多种癌症行为危险因素的最佳线性组合。在第二章中，我们测试了线性假设，并提出了在条件逻辑回归模型中测试协变量功能形式的方法，这些方法基于非参数平滑器。第3章中开发的方法估计了危险函数的趋势以及趋势何时发生变化。像本章中提出的方法一样，估计整个人群的危害功能的总体趋势的方法使研究人员和临床医生可以更好地了解不断变化的医学实践如何影响患者人群的生存经历。在第4章中，我们提出了带有纵向协变量的序数结果数据的混合模型，该协变量容易丢失。开发拟议的方法是为了检验针对建筑工人的量身定制，电话传递，戒烟干预措施的有效性。

著录项

  • 作者

    Goodman, Melody Serene.;

  • 作者单位

    Harvard University.;

  • 授予单位 Harvard University.;
  • 学科 Public health.;Biostatistics.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 103 p.
  • 总页数 103
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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