Light therapies for acne: abridged Cochrane systematic review including GRADE assessments.

We undertook a Cochrane review of randomized controlled trials (RCTs) evaluating the effects of light-based interventions for acne vulgaris. We searched the Cochrane Skin Specialised Register, CENTRAL, MEDLINE, Embase, LILACS, ISI Web of Science and grey literature sources (September 2015). We used the Grading of Recommendations Assessment, Development and Evaluation Working Group approach to assess the quality of evidence (QoE). We included 71 RCTs (4211 participants, median sample size 31). Results from a single study (n = 266, low QoE) showed little or no difference in effectiveness on participants' assessment of improvement between 20% aminolaevulinic acid (ALA) photodynamic therapy (PDT), activated by blue light, vs. vehicle plus blue light, whereas another study (n = 180) comparing ALA-PDT (red light) concentrations showed that 20% ALA-PDT was no more effective than 15% ALA-PDT but better than 10% and 5% ALA-PDT. Pooled data from three studies (n = 360, moderate QoE) showed that methyl aminolaevulinate PDT, activated by red light, had a similar effect on changes in lesion counts vs. placebo cream with red light. Several studies compared yellow light with placebo or no treatment, infrared light with no treatment, gold microparticle suspension with vehicle and clindamycin/benzoyl peroxide (C/BPO) combined with pulsed dye laser with C/BPO alone. None of these showed any clinically significant effects. Most studies reported adverse effects, but inadequately, with scarring reported as absent, and blistering only in studies on intense pulsed light, infrared light and PDT (very low QoE). Carefully planned studies, using standardized outcome measures and common acne treatments as comparators, are needed.

Bayesian modeling of multiple lesion onset and growth from interval-censored data.

In studying rates of occurrence and progression of lesions (or tumors), it is typically not possible to obtain exact onset times for each lesion. Instead, data consist of the number of lesions that reach a detectable size between screening examinations, along with measures of the size/severity of individual lesions at each exam time. This interval-censored data structure makes it difficult to properly adjust for the onset time distribution in assessing covariate effects on rates of lesion progression. This article proposes a joint model for the multiple lesion onset and progression process, motivated by cross-sectional data from a study of uterine leiomyoma tumors. By using a joint model, one can potentially obtain more precise inferences on rates of onset, while also performing onset time-adjusted inferences on lesion severity. Following a Bayesian approach, we propose a data augmentation Markov chain Monte Carlo algorithm for posterior computation.