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The effect of changes in intraocular pressure on the risk of primary open-angle glaucoma in patients with ocular hypertension: an application of latent class analysis

Feng Gao1*, J Philip Miller1, Stefano Miglior3, Julia A Beiser2, Valter Torri4, Michael A Kass2 and Mae O Gordon12

Author affiliations

1 Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, 63110, USA

2 Department of Ophthalmology & Visual Sciences, Washington University School of Medicine, St. Louis, MO, 63110, USA

3 Department of Ophthalmology, The Policlinico di Monza, University Bicocca of Milan, Milan, Italy

4 Laboratory of New Drugs Development Strategies, Mario Negri Institute, Milan, Italy

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Citation and License

BMC Medical Research Methodology 2012, 12:151  doi:10.1186/1471-2288-12-151

Published: 4 October 2012



Primary open-angle glaucoma (POAG) is one of the leading causes of blindness in the United States and worldwide. While lowering intraocular pressure (IOP) has been proven to be effective in delaying or preventing the onset of POAG in many large-scale prospective studies, one of the recent hot topics in glaucoma research is the effect of IOP fluctuation (IOP lability) on the risk of developing POAG in treated and untreated subjects.


In this paper, we analyzed data from the Ocular Hypertension Treatment Study (OHTS) and the European Glaucoma Prevention Study (EGPS) for subjects who had at least 2 IOP measurements after randomization prior to POAG diagnosis. We assessed the interrelationships among the baseline covariates, the changes of post-randomization IOP over time, and the risk of developing POAG, using a latent class analysis (LCA) which allows us to identify distinct patterns (latent classes) of IOP trajectories.


The IOP change in OHTS was best described by 6 latent classes differentiated primarily by the mean IOP levels during follow-up. Subjects with high post-randomization mean IOP level and/or large variability were more likely to develop POAG. Five baseline factors were found to be significantly predictive of the IOP classification in OHTS: treatment assignment, baseline IOP, gender, race, and history of hypertension. In separate analyses of EGPS, LCA identified different patterns of IOP change from those in OHTS, but confirmed that subjects with high mean level and large variability were at high risk to develop POAG.


LCA provides a useful tool to assess the impact of post-randomization IOP level and fluctuation on the risk of developing POAG in patients with ocular hypertension. The incorporation of post-randomization IOP can improve the overall predictive ability of the original model that included only baseline risk factors.

Latent class analysis; Longitudinal data; Time-dependent covariate; Prediction model; Survival data; Primary open-angle glaucoma; Intraocular pressure fluctuation