SynthesisClinical pharmacokinetics2015
Optimal sampling scheme for estimation of intraocular pressure diurnal curves in glaucoma trials.
Synthesis in Clinical pharmacokinetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- Applications of Model-Based Meta-Analysis in Drug Development.Pharmaceutical research · 2022Pooled it
- Extraocular, periocular, and intraocular routes for sustained drug delivery for glaucoma.Progress in retinal and eye research · 2021Review
- Advances in diagnostic applications for monitoring intraocular pressure in Glaucoma: A review.Journal of optometryReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveEffective control of intraocular pressure (IOP) is essential for the successful management of glaucoma. IOP exhibits diurnal variation, yet continuous monitoring is impractical. To date, no clear evidence exists on the number of sampling timepoints required to characterize diurnal IOP and when those measurements should be collected. The objective of this study was to develop an optimized sampling scheme to estimate diurnal IOP and to provide sampling windows for practicality.
methodsBaseline IOP values for glaucoma patients were collected from the published literature. A population model-based meta-analysis was performed to develop a model for diurnal IOP that accounts for covariates and inter-study variability. Optimization was performed using the D-optimality criteria to determine optimal sampling times. In addition, various reduced sampling designs were tested to investigate the minimum number of sampling timepoints to precisely estimate diurnal IOP. Also, sampling windows were calculated around the final optimal sampling times to allow flexibility in data collection. The final reduced optimized model was validated by simulating and estimating 500 datasets with reduced optimal sampling times.
resultsThe final baseline IOP model included type of glaucoma as a covariate. Bootstrap analysis and visual predictive check plots revealed the adequacy of the model to describe the observed IOP data. Optimization results indicated an increasing trend in bias with decreasing sampling timepoints. A reduced model with four sampling times resulted in acceptable precision (<40 %). Restricting the sampling time between 8 a.m. and 4 p.m. underestimates the fluctuation in diurnal IOP. Sampling windows with ≥95 % efficiency were calculated around the optimized sampling times. Validation results indicated acceptable precision and relative bias for model estimates in the reduced optimized model.
conclusionA physiologically based mechanistic model was developed to describe the diurnal variation in baseline IOP and inter-study variability was estimated on key diurnal model parameters. Optimization of the final covariate model indicated a reduced sampling time of at least four samples should be collected at 5:45 a.m., 2:15 p.m., 8:00 p.m., and 12:00 a.m. for reliable estimation of diurnal IOP variation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.