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Heterogeneity is commonplace in meta-analysis. When heterogeneity is found, researchers often aim to identify predictors that account for at least part of such heterogeneity by using mixed-effects meta-regression models. Another potentially relevant goal is to focus on the amount of heterogeneity as a function of one or more predictors, but this cannot be examined with standard random- and mixed-effects models, which assume a constant (i.e., homoscedastic) value for the heterogeneity variance component across studies. In this paper, we describe a location-scale model for meta-analysis as an extension of the standard random- and mixed-effects models that not only allows an examination of whether predictors are related to the size of the outcomes (i.e., their location), but also the amount of heterogeneity (i.e., their scale). We present estimation methods for such a location-scale model through maximum and restricted maximum likelihood approaches, as well as methods for inference and suggestions for visualization. We also provide an implementation via the "metafor" package for R that makes this model readily available to researchers. Location-scale models can provide a useful tool to researchers interested in heterogeneity in meta-analysis, with the potential to enhance the scope of research questions in the field of evidence synthesis.

Standard meta-analysis methods are vulnerable to bias from incomplete reporting of results (both publication and outcome reporting bias) and poor study quality. Several alternative methods have been proposed as being less vulnerable to such biases. To evaluate these claims independently we simulated study results under a broad range of conditions first with no bias, then introducing simulated publication bias, outcome reporting bias, and bias from poor study quality. We then implemented common and the proposed bias robust meta-analysis methods and compared the mean bias and mean squared error (MSE) for four estimates of effect and the coverage probability of seven confidence intervals. We found that no methods perform well in the presence of any substantial bias. A regression based extension to Egger's test gave an estimate of effect with lower mean bias than standard methods in the presence of publication bias or poor study quality, but had a substantially worse MSE except in very specific conditions. Coverage of all 95% confidence intervals was very poor with increasing numbers of studies in biased conditions, often falling below 50%. The Knapp-Hartung interval performed closest to nominal coverage with fewer than 10 studies in most conditions, and the Henmi-Copas interval generally performed best with more than 10 studies. There was no evidence that a multiplicative term for heterogeneity improved coverage. Multiple forms of bias remain problematic for all meta-analysis methods, with very poor performance under conceivable conditions.

Mixed-effects models can be used to examine the association between a categorical moderator and the magnitude of the effect size. Two approaches are available to estimate the residual between-studies variance, t[superscript 2][subscript res] --namely, separate estimation within each category of the moderator versus pooled estimation across all categories. We examine, by means of a Monte Carlo simulation study, both approaches for t[superscript 2][subscript res] estimation in combination with two methods, the Wald-type X[superscript 2] and "F" tests, to test the statistical significance of the moderator. Results suggest that the "F" test using a pooled estimate of t[superscript 2][subscript res] across categories is the best option in most conditions, although the "F" test using separate estimates of t[superscript 2][subscript res] is preferable if the residual heterogeneity variances are heteroscedastic.

This paper considers the problem in aggregate data meta-analysis of studies reporting multiple competing binary outcomes and of studies using different summary formats for those outcomes. For example, some may report numbers of patients with at least one of each outcome while others may report the total number of such outcomes. We develop a shared parameter model on hazard ratio scale accounting for different data summaries and competing risks. We adapt theoretical arguments from the literature to demonstrate that the models are equivalent if events are rare. We use constructed data examples and a simulation study to find an event rate threshold of approximately 0.2 above which competing risks and different data summaries may bias results if no adjustments are made. Below this threshold, simpler models may be sufficient. We recommend analysts to consider the absolute event rates and only use a simple model ignoring data types and competing risks if all of underlying events are rare (below our threshold of approximately 0.2). If one or more of the absolute event rates approaches or exceeds our informal threshold, it may be necessary to account for data types and competing risks through a shared parameter model in order to avoid biased estimates.

Background: The increase in soft tissue (ST) around implants can benefit peri-implant health and aesthetic results. The objective was to compare the gingival and esthetic health benefits of immediate implant placement (IIP) with simultaneous or delayed connective tissue graft (CTG) compared to IIP without CTG. Methods: A systematic review was carried out by two reviewers in Medline-Pubmed, Scopus, and Cochrane. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) were considered. Randomized Clinical Trials (RCTs) that were published between April 2017 and February 2024 were used. Studies that analyzed the performance of a simultaneous or deferred CTG after the placement of an implant in the aesthetic zone, with or without immediate provisionalization, without previous regeneration, with a follow-up of 6 months, and that were performed in humans were included. Results: Quantitative analysis was performed using data provided by the RCTs. The five RCTs that were selected analyzed a total "n" of 245 subjects who met the inclusion criteria and focused on the subject of the study. In the quantitative analysis, four RCTs were included. The studies evaluated buccal gingiva levels when placing the IIP with and without CTG, obtaining a mean buccal gingiva level difference of 0.09 mm (95% CI: −0.54 to 0.72, p = 0.05), statistically not significant, but with a favorable trend. Conclusions: The use of CTG associated with the II can maintain the gum level but not increase the volume. CTG is favorable for achieving successful esthetic results when immediate placement of an implant with a provisional prosthesis is planned. [ABSTRACT FROM AUTHOR]

Non-specific low back pain (NSLBP) in children and adolescents has increased in recent years, and the evidence of the physiotherapy interventions in back care needs to be updated. Our main goal was to quantify the effects of preventive physiotherapy interventions on improving behavior and knowledge related to back care and prevention of NSLBP in children and adolescents. Based on two previous meta-analyses, Cochrane Library, MEDLINE, PEDro, Web of Science, LILACS, IBECS, PsycINFO, and IME databases and several journals were searched. Two researchers independently extracted data and assessed the risk of bias in the studies using the RoB2 tool. Data were described according to PRISMA guidelines. A total of 24 studies (28 reports) were included. In the posttest, the behavior variable obtained an overall effect size of d+ = 1.48 (95%CI: 0.40 to 2.56), and the knowledge variable obtained an effect size of d+ = 1.41 (95%CI: 1.05 to 1.76). Physiotherapy has demonstrated beneficial impacts on behavior and knowledge concerning back care and to prevent NSLBP in children and adolescents. Interventions focusing on postural hygiene and exercise should be preferred, especially those that are shorter in number of weeks, more intense, and incorporate as many intervention hours as possible. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this investigation was to analyze the prevalence of apical periodontitis (AP) and periodontal disease (periodontitis) (PD) in Chronic kidney disease (CKD) patients in relation to their treatment phase. Subjects and Methods: In this cross‐sectional study, 188 patients with CKD were divided into two groups: patients without dialysis (WD group, n = 53) and patients on dialysis (DP group, n = 135). Panoramic radiographs were used to diagnose AP. The presence of periodontal disease was evaluated radiographically assessing alveolar bone loss. Student's t‐test, chi‐squared test, and logistic regression analysis were used to determine the significance of differences between groups. Results: In the WD group, 55% of patients had at least one tooth with AP, whereas in the DP group 67% had at least one tooth with AP (OR = 2.11; 95% CI = 1.09–4.08; p < 0.05). PD was more prevalent in the DP group (78%) than in the WD group (36%) (OR = 6.26; CI 95% = 3.13–12.52; p < 0.01). Conclusions: Oral infections are more prevalent in the advanced stages of CKD. The treatment of PD and AP should be incorporated in the treatment planning of patients with CKD. [ABSTRACT FROM AUTHOR]