Outline

  • Abstract
  • 1. Introduction
  • 2. Approaches to Statistical Analysis
  • 2.1. Notation and Likelihood Construction
  • 2.2. Nonparametric Estimation of the Survival Function Estimation
  • 3. Error in the Reported Onset Time
  • 3.1. Introduction
  • 3.2. the Classical Measurement Error Model
  • 3.3. Empirical Study of Measurement Error
  • 4. the Corrected Likelihood
  • 4.1. Corrected Parametric Conditional Likelihood
  • 4.2. Empirical Study of Corrected Likelihood
  • 5. Discussion
  • References

رئوس مطالب

  • چکیده
  • 1. مقدمه
  • 2. روش ها برای تجزیه و تحلیل آماری
  • 2.1. نشانه گذاری و ساخت احتمال
  • 2.2. برآورد ناپارامتری از برآورد تابع بقا
  • 3. خطا در زمان شروع گزارش شده
  • 3.1. مقدمه
  • 3.2. مدل خطای اندازه گیری کلاسیک
  • 3.3. مطالعه تجربی از خطای اندازه گیری
  • 4. احتمال تصحیح
  • 4.1. احتمال شرطی پارامتری اصلاح
  • 4.2. احتمال تصحیح مطالعه تجربی
  • 5. بحث و گفتگو

Abstract

Prevalent cohort studies involve screening a sample of individuals from a population for disease, recruiting affected individuals, and prospectively following the cohort of individuals to record the occurrence of disease-related complications or death. This design features a response-biased sampling scheme since individuals living a long time with the disease are preferentially sampled, so naive analysis of the time from disease onset to death will over-estimate survival probabilities. Unconditional and conditional analyses of the resulting data can yield consistent estimates of the survival distribution subject to the validity of their respective model assumptions. The time of disease onset is retrospectively reported by sampled individuals, however, this is often associated with measurement error. In this article we present a framework for studying the effect of measurement error in disease onset times in prevalent cohort studies, report on empirical studies of the effect in each framework of analysis, and describe likelihood-based methods to address such a measurement error.

Keywords: - - - -

5. Discussion

Statistical models and methods for the analysis of prevalent cohort data have been reviewed here from both the conditional and unconditional frameworks. It is well known that naive analyses which ignore the selection bias lead to overestimation of the survivor probabilities. The conditional likelihood based on the density for lefttruncated event times can be used to correct for this selection bias. The unconditional likelihood approach is based on the joint density of the backwards and forward recurrence times yield more efficient estimators by incorporating the information contained in the onset times. The typical assumption required to formulate the associated model is of a stationary disease incidence process. Since both approaches make use of the onset time information to correct for selection effects, misspecification of the retrospectively reported disease onset time can have serious implications on the estimation. We investigate the impact of measurement error in disease onset time for prevalent cohort sample and propose “correct” conditional and unconditional likelihoods to account for the measurement error.The methods we proposed to correct for measurement error in this paper are based on the parametric model.

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