Statistical inference for correlated data in ophthalmologic studies

Man Lai TANG*, Nian Sheng Tang, Bernard Rosner

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)

Abstract

In ophthalmologic studies, each subject usually contributes important information for each of two eyes and the values from the two eyes are generally highly correlated. Previous studies showed that test procedures for binary paired data that ignore the presence of intraclass correlation could lead to inflated significance levels. Furthermore, it is possible that asymptotic versions of these procedures that take the intraclass correlation into account could also produce unacceptably high type I error rates when the sample size is small or the data structure is sparse. We propose two alternatives for these situations, namely the exact unconditional and approximate unconditional procedures. According to our simulation results, the exact procedures usually produce extremely conservative empirical type I error rates. That is, the corresponding type I error rates could greatly underestimate the pre-assigned nominal level (e.g. (empirical type I error rate/nominal type I error rate) <0.8). On the other hand, the approximate unconditional procedures usually yield empirical type I error rates close to the pre-chosen nominal level. We illustrate our methodologies with a data set from a retinal detachment study.

Original languageEnglish
Pages (from-to)2771-2783
Number of pages13
JournalStatistics in Medicine
Volume25
Issue number16
DOIs
Publication statusPublished - 30 Aug 2006

Scopus Subject Areas

  • Epidemiology
  • Statistics and Probability

User-Defined Keywords

  • Approximate unconditional tests
  • Asymptotic tests
  • Exact unconditional tests
  • Intraclass correlation

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