Mixed treatment comparison and meta-regression of the efficacy and safety of prostaglandin analogues and comparators for primary open-angle glaucoma and ocular hypertension.
ABSTRACT Primary open-angle glaucoma (POAG) is a chronic condition characterised by optic neuropathy and vision loss. Elevated intraocular pressure (IOP) can damage the optic nerve and is a risk factor for glaucoma, thus treatment usually comprises topical hypotensives. This analysis aims to address methodological issues associated with the synthesis of glaucoma clinical trial data, given variations in study methodology and IOP measurement.
Meta-regression was used to estimate how IOP varies over time for patients receiving treatment. Relative treatment effects were assessed using a random-effects mixed treatment comparison (MTC) in order to preserve randomisation and avoid selection bias. To produce clinically meaningful outputs, these analyses were combined to obtain the mean on-treatment IOP and the proportion of patients achieving different IOP targets at different time points. A further MTC estimated the probability of hyperaemia events.
The analysis showed that after 3 months' treatment, between 58 and 83% of patients will have a > or =20% reduction in IOP and 70-93% of patients will have an absolute IOP <20 mmHg. Latanoprost and bimatoprost were found to produce significantly lower on-treatment IOP compared with timolol (p < 0.05); the difference between latanoprost and bimatoprost was not significant. Travoprost produced a lower mean IOP compared with timolol (not significant). Latanoprost-timolol was found to produce significantly lower IOP than latanoprost alone or beta-blockers. The probability of hyperaemia-type events varied between treatments from 14.8 to 63.03%. Latanoprost had significantly lower odds of hyperaemia than travoprost, bimatoprost, travoprost-timolol, or bimatoprost-timolol.
This analysis suggests that latanoprost and bimatoprost produce a statistically significant reduction in IOP compared with timolol, but are associated with a higher risk of hyperaemia. Out of all the prostaglandins, latanoprost may achieve a good balance between tolerability and IOP efficacy. As with all forms of meta-analysis, the results are based on the assumption that the studies and intervention groupings are sufficiently similar to be compared.
- SourceAvailable from: Jacopo ButiEuropean Journal of Oral Implantology 01/2011; 4(1):55-62. · 2.02 Impact Factor
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ABSTRACT: Mixed treatment comparison (MTC) meta-analyses estimate relative treatment effects from networks of evidence while preserving randomisation. We extend the MTC framework to allow for repeated measurements of a continuous endpoint that varies over time. We used, as a case study, a systematic review and meta-analysis of intraocular pressure (IOP) measurements from randomised controlled trials evaluating topical ocular hypotensives in primary open-angle glaucoma or ocular hypertension because IOP varies over the day and over the treatment course, and repeated measurements are frequently reported. We adopted models for conducting MTC in WinBUGS (The BUGS Project, Cambridge, UK) to allow for repeated IOP measurements and to impute missing standard deviations of the raw data using the predictive distribution from observations with standard deviations. A flexible model with an unconstrained baseline for IOP variations over time and time-invariant random treatment effects fitted the data well. We also adopted repeated measures models to allow for class effects; assuming treatment effects to be exchangeable within classes slightly improved model fit but could bias estimated treatment effects if exchangeability assumptions were not valid. We enabled all timepoints to be included in the analysis, allowing for repeated measures to increase precision around treatment effects and avoid bias associated with selecting timepoints for meta-analysis.The methods we developed for modelling repeated measures and allowing for missing data may be adapted for use in other MTC meta-analyses. Copyright © 2011 John Wiley & Sons, Ltd.Statistics in Medicine 09/2011; 30(20). DOI:10.1002/sim.4284 · 2.04 Impact Factor
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ABSTRACT: To identify published closed-loop Bayesian mixed treatment comparisons (MTCs) and to summarise characteristics regarding their conduct and reporting. Systematic review. We searched multiple bibliographic databases (January 2006-31 July 2011) for full-text, English language publications of Bayesian MTCs comparing the effectiveness or safety of ≥3 interventions based on randomised controlled trials and having at least one closed loop. Methodological and reporting characteristics of MTCs were extracted in duplicate and summarised descriptively. We identified 34 Bayesian MTCs spanning 13 clinical areas. Publication of MTCs increased over the 5-year period; with 76.5% published during or after 2009. MTCs included a mean (±SD) of 35.9±30.1 trials (n=33 459±71 233 participants) and 8.5±4.3 interventions (85.7% pharmacological). Non-informative and informative prior distributions were reported to be used in 44.1% and 8.8% of MTCs, respectively, with the remainder failing to specify the prior used. A random-effects model was used to analyse the networks of trials in 58.5% of MTCs, all using WinBUGS; however, code was infrequently provided (20.6%). More than two-thirds of MTCs (76.5%) also conducted traditional meta-analysis. Methods used to evaluate convergence, heterogeneity and inconsistency were infrequently reported, but from those providing detail, methods appeared varied. MTCs most often used a binary effect measure (85.3%) and ranking of interventions based on probability was common (61.8%), although rarely displayed in a figure (8.8% of MTCs). MTCs were published in 24 different journals with a mean impact factor of 9.20±8.71. While 70.8% of journals imposed limits on word counts and 45.8% limits on the number of tables/figures, online supplements/appendices were allowed in 79.2% of journals. Publication of closed-loop Bayesian MTCs is increasing in frequency, but details regarding their methodology are often poorly described. Efforts in clarifying the appropriate methods and reporting of Bayesian MTCs should be of priority.BMJ Open 07/2013; 3(7). DOI:10.1136/bmjopen-2013-003111 · 2.06 Impact Factor