Missingness mechanism messiness in meta-analysis: A response to "The importance of plausible data-generating mechanisms in simulation studies".

The current paper responds to the commentary on the article (doi:10.1002/jrsm.1605).

Comparing methods for handling missing covariates in meta-regression.

Meta-analysts often encounter missing covariate values when estimating meta-regression models. In practice, ad hoc approaches involving data deletion have been widely used. The current study investigates the performance of different methods for handling missing covariates in meta-regression, including complete-case analysis (CCA), shifting-case analysis (SCA), multiple imputation (MI), and full information maximum likelihood (FIML), assuming missing at random mechanism.

Cluster wild bootstrapping to handle dependent effect sizes in meta-analysis with a small number of studies.

The most common and well-known meta-regression models work under the assumption that there is only one effect size estimate per study and that the estimates are independent. However, meta-analytic reviews of social science research often include multiple effect size estimates per primary study, leading to dependence in the estimates. Some meta-analyses also include multiple studies conducted by the same lab or investigator, creating another potential source of dependence.

Multilevel Meta-Analysis of Individual Participant Data of Single-Case Experimental Designs: One-Stage versus Two-Stage Methods.

To conduct a multilevel meta-analysis of multiple single-case experimental design (SCED) studies, the individual participant data (IPD) can be analyzed in one or two stages. In the one-stage approach, a multilevel model is estimated based on the raw data. In the two-stage approach, an effect size is calculated for each participant and these effect sizes and their sampling variances are subsequently combined to estimate a meta-analytic multilevel model.

Estimating outcome-specific effects in meta-analyses of multiple outcomes: A simulation study.

In meta-analysis, primary studies often include multiple, dependent effect sizes. Several methods address this dependency, such as the multivariate approach, three-level models, and the robust variance estimation (RVE) method. As for today, most simulation studies that explore the performance of these methods have focused on the estimation of the overall effect size.

The application of meta-analytic (multi-level) models with multiple random effects: A systematic review.

In meta-analysis, study participants are nested within studies, leading to a multilevel data structure. The traditional random effects model can be considered as a model with a random study effect, but additional random effects can be added in order to account for dependent effects sizes within or across studies. The goal of this systematic review is three-fold.

Multilevel meta-analysis of multiple regression coefficients from single-case experimental studies.

The focus of the current study is on handling the dependence among multiple regression coefficients representing the treatment effects when meta-analyzing data from single-case experimental studies. We compare the results when applying three different multilevel meta-analytic models (i.e.

The multivariate multiple-membership random-effect model: An introduction and evaluation.

In the present study, we focused on models that handle several data structure complexities simultaneously. We introduced and evaluated the multivariate multiple-membership random-effect model (MV-MMREM) for handling multiple-membership data in scenarios with multiple, related outcomes. Although a recent study introduced the idea of the MV-MMREM, no research has directly assessed its estimation nor demonstrated its use with real data.

MultiSCED: A tool for (meta-)analyzing single-case experimental data with multilevel modeling.

The MultiSCED web application has been developed to assist applied researchers in behavioral sciences to apply multilevel modeling to quantitatively summarize single-case experimental design (SCED) studies through a user-friendly point-and-click interface embedded within R. In this paper, we offer a brief introduction to the application, explaining how to define and estimate the relevant multilevel models and how to interpret the results numerically and graphically. The use of the application is illustrated through a re-analysis of an existing meta-analytic dataset.

Quantifying the association between acute leukemia and serum zinc, copper, and selenium: a meta-analysis.

Acute leukemia is a clonal malignant disorder that occurs when immature blast cells accumulate in bone marrow. Zinc (Zn) and copper (Cu) are related to normal lymphocyte maturation and immune function regulation. Selenium (Se) is protective against oxidative damage.

Synthesizing effects for multiple outcomes per study using robust variance estimation versus the three-level model.

Primary studies increasingly report information that can be used to provide multiple effect sizes. Of interest in this study, primary studies might compare a treatment and a control group on multiple related outcomes that result in multiple dependent effect sizes to be synthesized. There are a number of ways to handle the resulting within-study "multiple-outcome" dependency.

Analysis of single-case experimental count data using the linear mixed effects model: A simulation study.

When (meta-)analyzing single-case experimental design (SCED) studies by means of hierarchical or multilevel modeling, applied researchers almost exclusively rely on the linear mixed model (LMM). This type of model assumes that the residuals are normally distributed. However, very often SCED studies consider outcomes of a discrete rather than a continuous nature, like counts, percentages or rates.

Zinc, Copper, and Iron in Oral Submucous Fibrosis: A Meta-Analysis.

Oral submucous fibrosis (OSF) is a potentially malignant disorder which causes fibrosis and inflammation of the oral mucosa. Studies have reported altered levels of trace elements in oral submucous fibrosis subjects, but findings have been inconsistent. The objective of this research is to perform a meta-analysis to summarize studies that report zinc (Zn), copper (Cu), and iron (Fe) in patients, with and without OSF.

Trace element status in type 2 diabetes: A meta-analysis.

Introduction: Type 2 diabetes is a chronic metabolic disorder that has been associated with alterations in the status of trace elements, including zinc, copper, iron and manganese. However, clinical studies reporting statuses of these trace elements in type 2 diabetes patients compared to controls have shown conflicting results.

Objective: This meta-analysis aimed to summarize the existing literature on the statuses of zinc, copper, iron, and manganese in Type 2 diabetes mellitus patients.

A demonstration and evaluation of the use of cross-classified random-effects models for meta-analysis.

It is common for the primary studies in meta-analyses to report multiple effect sizes, generating dependence among them. Hierarchical three-level models have been proposed as a means to deal with this dependency. Sometimes, however, dependency may be due to multiple random factors, and random factors are not necessarily nested, but rather may be crossed.

Estimation of a Latent Variable Regression Growth Curve Model for Individuals Cross-Classified by Clusters.

The cross-classified multiple membership latent variable regression (CCMM-LVR) model is a recent extension to the three-level latent variable regression (HM3-LVR) model which can be utilized for longitudinal data that contains individuals who changed clusters over time (for instance, student mobility across schools). The HM3-LVR model can include the initial status on growth effect as varying across those clusters and allows testing of more flexible hypotheses about the influence of initial status on growth and of factors that might impact that relationship, but only in the presence of pure clustering of participants within higher-level units. This Monte Carlo study was conducted to evaluate model estimation under a variety of conditions and to measure the impact of ignoring cross-classified data when estimating the incorrectly specified HM3-LVR model in a scenario in which true values for parameters are known.

Methodological quality of meta-analyses of single-case experimental studies.

Background: Methodological rigor is a fundamental factor in the validity and credibility of the results of a meta-analysis.

Aim: Following an increasing interest in single-case experimental design (SCED) meta-analyses, the current study investigates the methodological quality of SCED meta-analyses.

Methods And Procedures: We assessed the methodological quality of 178 SCED meta-analyses published between 1985 and 2015 through the modified Revised-Assessment of Multiple Systematic Reviews (R-AMSTAR) checklist.

The impact of response-guided baseline phase extensions on treatment effect estimates.

Background: When developmental disabilities researchers use multiple-baseline designs they are encouraged to delay the start of an intervention until the baseline stabilizes or until preceding cases have responded to intervention. Using ongoing visual analyses to guide the timing of the start of the intervention can help to resolve potential ambiguities in the graphical display; however, these forms of response-guided experimentation have been criticized as a potential source of bias in treatment effect estimation and inference.

Aims And Methods: Monte Carlo simulations were used to examine the bias and precision of average treatment effect estimates obtained from multilevel models of four-case multiple-baseline studies with series lengths that varied from 19 to 49 observations per case.

A Biomarker Combining Imaging and Neuropsychological Assessment for Tracking Early Alzheimer's Disease in Clinical Trials.

Background: Combining optimized cognitive (Alzheimer's Disease Assessment Scale- Cognitive subscale, ADAS-Cog) and atrophy markers of Alzheimer's disease for tracking progression in clinical trials may provide greater sensitivity than currently used methods, which have yielded negative results in multiple recent trials. Furthermore, it is critical to clarify the relationship among the subcomponents yielded by cognitive and imaging testing, to address the symptomatic and anatomical variability of Alzheimer's disease.

Method: Using latent variable analysis, we thoroughly investigated the relationship between cognitive impairment, as assessed on the ADAS-Cog, and cerebral atrophy.

AHRQ series on complex intervention systematic reviews-paper 5: advanced analytic methods.

Background And Objective: Advanced analytic methods for synthesizing evidence about complex interventions continue to be developed. In this paper, we emphasize that the specific research question posed in the review should be used as a guide for choosing the appropriate analytic method.

Methods: We present advanced analytic approaches that address four common questions that guide reviews of complex interventions: (1) How effective is the intervention? (2) For whom does the intervention work and in what contexts? (3) What happens when the intervention is implemented? and (4) What decisions are possible given the results of the synthesis?

Conclusion: The analytic approaches presented in this paper are particularly useful when each primary study differs in components, mechanisms of action, context, implementation, timing, and many other domains.

Medication Adherence and Persistence in Patients with Severe Major Depressive Disorder with Psychotic Features: Antidepressant and Second-Generation Antipsychotic Therapy Versus Antidepressant Monotherapy.

Background: Major depressive disorder with psychotic features, or psychotic depression, is a severe mental health disorder often associated with a worse depression-related symptom profile when compared with major depressive disorder without psychotic features. While combination pharmacotherapy with an antidepressant and an antipsychotic is recommended as first-line therapy, antidepressant monotherapy has been found to be useful and efficacious in psychotic depression.

Objective: To assess the rates of antidepressant adherence and antidepressant persistence in Texas Medicaid patients with psychotic depression who used antidepressant plus second-generation antipsychotic (AD/SGA) therapy or antidepressant (AD) monotherapy.

New scoring methodology improves the sensitivity of the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) in clinical trials.

Introduction: As currently used, the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) has low sensitivity for measuring Alzheimer's disease progression in clinical trials. A major reason behind the low sensitivity is its sub-optimal scoring methodology, which can be improved to obtain better sensitivity.

Methods: Using item response theory, we developed a new scoring methodology (ADAS-CogIRT) for the ADAS-Cog, which addresses several major limitations of the current scoring methodology.