A weighted logistic regression model for estimation of recurrence of adenomas

Chiu Hsieh Hsu, Sylvan B. Green, Yulei He

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

In a colorectal polyp prevention trial, some participants might have their follow-up colonoscopy conducted before the scheduled time (i.e. at the end of the trial). This results in variable follow-up lengths for participants and the data of recurrence status at the end of the trial can be considered as current status data. In this paper, we use a weighted logistic regression model to estimate recurrence rate of adenoma data at the end of the trial. The weights are used to adjust for variable follow-up. We show that logistic regression tends to underestimate recurrence rate. In a simulation study, we show that Kaplan-Meier estimator derived from the right endpoint of the current status data tends to overestimate recurrence rate in contrast to logistic regression and the weighted logistic regression method can produce reasonable estimates of recurrence rate even under a high non-compliance rate compared to conventional logistic regression and Kaplan-Meier estimator. The method described here is illustrated with an example from a colon cancer study.

Original languageEnglish (US)
Pages (from-to)1567-1578
Number of pages12
JournalStatistics in Medicine
Volume26
Issue number7
DOIs
StatePublished - Mar 30 2007

Keywords

  • Colon Cancer
  • Current status data
  • Logistic regression
  • Non-parametric maximum likelihood estimator
  • Weight function

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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