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Ophthalmic Epidemiol. 2018 Feb;25(1):45-54. doi: 10.1080/09286586.2017.1339809. Epub 2017 Sep 11.

Evaluation of Approaches to Analyzing Continuous Correlated Eye Data When Sample Size Is Small.

Author information

1
a Division of Biostatistics , Center for Clinical Epidemiology and Biostatistics.
2
b Center for Preventive Ophthalmology and Biostatistics, Department of Ophthalmology, Perelman School of Medicine , University of Pennsylvania , Philadelphia , Pennsylvania , USA.

Abstract

PURPOSE:

To evaluate the performance of commonly used statistical methods for analyzing continuous correlated eye data when sample size is small.

METHODS:

We simulated correlated continuous data from two designs: (1) two eyes of a subject in two comparison groups; (2) two eyes of a subject in the same comparison group, under various sample size (5-50), inter-eye correlation (0-0.75) and effect size (0-0.8). Simulated data were analyzed using paired t-test, two sample t-test, Wald test and score test using the generalized estimating equations (GEE) and F-test using linear mixed effects model (LMM). We compared type I error rates and statistical powers, and demonstrated analysis approaches through analyzing two real datasets.

RESULTS:

In design 1, paired t-test and LMM perform better than GEE, with nominal type 1 error rate and higher statistical power. In design 2, no test performs uniformly well: two sample t-test (average of two eyes or a random eye) achieves better control of type I error but yields lower statistical power. In both designs, the GEE Wald test inflates type I error rate and GEE score test has lower power.

CONCLUSION:

When sample size is small, some commonly used statistical methods do not perform well. Paired t-test and LMM perform best when two eyes of a subject are in two different comparison groups, and t-test using the average of two eyes performs best when the two eyes are in the same comparison group. When selecting the appropriate analysis approach the study design should be considered.

KEYWORDS:

Correlated eye data; generalized estimating equations; linear mixed effects model; paired t-test; small sample size; two sample t-test

PMID:
28891730
PMCID:
PMC5988240
DOI:
10.1080/09286586.2017.1339809
[Indexed for MEDLINE]
Free PMC Article

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