Nonparametric estimation of the random effects distribution for the risk or rate ratio in rare events meta-analysis with the arm-based and contrast-based approaches.

Rare events are events which occur with low frequencies. These often arise in clinical trials or cohort studies where the data are arranged in binary contingency tables. In this article, we investigate the estimation of effect heterogeneity for the risk-ratio parameter in meta-analysis of rare events studies through two likelihood-based nonparametric mixture approaches: an arm-based and a contrast-based model. Maximum likelihood estimation is achieved using the EM algorithm. Special attention is given to the choice of initial values. Inspired by the classification likelihood, a strategy is implemented which repeatably uses random allocation of the studies to the mixture components as choice of initial values. The likelihoods under the contrast-based and arm-based approaches are compared and differences are highlighted. We use simulations to assess the performance of these two methods. Under the design of sampling studies with nested treatment groups, the results show that the nonparametric mixture model based on the contrast-based approach is more appropriate in terms of model selection criteria such as AIC and BIC. Under the arm-based design the results from the arm-based model performs well although in some cases it is also outperformed by the contrast-based model. Comparisons of the estimators are provided in terms of bias and mean squared error. Also included in the comparison is the mixed Poisson regression model as well as the classical DerSimonian-Laird model (using the Mantel-Haenszel estimator for the common effect). Using simulation, estimating effect heterogeneity in the case of the contrast-based method appears to behave better than the compared methods although differences become negligible for large within-study sample sizes. We illustrate the methodologies using several meta-analytic data sets in medicine.

Rare events meta-analysis using the Bayesian beta-binomial model.

In meta-analyses of rare events, it can be challenging to obtain a reliable estimate of the pooled effect, in particular when the meta-analysis is based on a small number of studies. Recent simulation studies have shown that the beta-binomial model is a promising candidate in this situation, but have thus far only investigated its performance in a frequentist framework. In this study, we aim to make the beta-binomial model for meta-analysis of rare events amenable to Bayesian inference by proposing prior distributions for the effect parameter and investigating the models' robustness to different specifications of priors for the scale parameter. To evaluate the performance of Bayesian beta-binomial models with different priors, we conducted a simulation study with two different data generating models in which we varied the size of the pooled effect, the degree of heterogeneity, the baseline probability, and the sample size. Our results show that while some caution must be exercised when using the Bayesian beta-binomial in meta-analyses with extremely sparse data, the use of a weakly informative prior for the effect parameter is beneficial in terms of mean bias, mean squared error, and coverage. For the scale parameter, half-normal and exponential distributions are identified as candidate priors in meta-analysis of rare events using the Bayesian beta-binomial model.

The Manic Idea Creator? A Review and Meta-Analysis of the Relationship between Bipolar Disorder and Creative Cognitive Potential.

Even though a relationship between psychopathology and creativity has been postulated since the time of ancient Greece, systematic meta-analyses on this topic are still scarce. Thus, the meta-analysis described here can be considered the first to date that specifically focuses on the relationship between creative potential, as measured by divergent thinking, and bipolar disorder, as opposed to psychopathology in general. An extensive literature search of 4670 screened hits identified 13 suitable studies, including a total of 42 effect sizes and 1857 participants. The random-effects model showed an overall significant, positive, yet diminutively small effect (d = 0.11, 95% CI: [0.002, 0.209], p = 0.045) between divergent thinking and bipolar disorder. A handful of moderators were examined, which revealed a significant moderating effect for bipolar status, as either euthymic (d = 0.14, p = 0.043), subclinical (d = 0.17, p = 0.001), manic (d = 0.25, p = 0.097), or depressed (d = -0.51, p < 0.001). However, moderator analyses should be treated with caution because of the observed confounding of moderators. Finally, none of the employed methods for publication-bias detection revealed any evidence for publication bias. We discuss further results, especially regarding the differences between subclinical and clinical samples.

The efficacy and acceptability of psychological interventions for adult PTSD: A network and pairwise meta-analysis of randomized controlled trials.

OBJECTIVE: A comprehensive quantitative summary of the efficacy and acceptability of psychological interventions (PIs) for adult posttraumatic stress disorder (PTSD) is lacking. METHOD: We conducted a systematic literature search to identify randomized controlled trials (RCTs) examining the efficacy and acceptability (all-cause dropout) of psychological interventions (i.e., trauma-focused cognitive behavior therapy [TF-CBT], eye movement desensitization and reprocessing [EMDR], other trauma-focused interventions and non-trauma-focused interventions). RESULTS: One hundred fifty-seven RCTs were included comprising 11,565 patients. Most research (64% of RCTs) accumulated for TF-CBT. In network meta-analyses, all therapies were effective when compared to control conditions. Interventions did not differ significantly in their efficacy. Yet, TF-CBT yielded higher short- (g = 0.17, 95% CI [0.03-0.31], number of comparisons kes = 190), mid- (i.e., ≤5 months posttreatment, g = 0.23, 95% CI [0.06-0.40], kes = 73) and long-term efficacy (i.e., >5 months posttreatment, g = 0.20, 95% CI [0.04-0.35], kes = 41) than non-trauma-focused interventions. There was some evidence of network inconsistencies, and heterogeneity in outcomes was large. In pairwise meta-analysis, slightly more patients dropped out from TF-CBT than non-trauma-focused interventions (RR = 1.36; 95% CI [1.08-1.70], kes = 22). Other than that, interventions did not differ in their acceptability. CONCLUSIONS: Interventions with and without trauma focus are effective and acceptable in the treatment of PTSD. While TF-CBT yields the highest efficacy, slightly more patients discontinued TF-CBT than non-trauma-focused interventions. Altogether, the present results align with results of most previous quantitative reviews. Yet, results need to be interpreted with caution in light of some network inconsistencies and high heterogeneity in outcomes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Comprehensive ABA-based interventions in the treatment of children with autism spectrum disorder - a meta-analysis.

BACKGROUND: Many studies display promising results for interventions that are based on Applied Behavior Analysis (ABA) in the treatment of autism spectrum disorder (ASD). METHODS: This meta-analysis assessed the effects of such treatments on developmental outcomes in children with ASD and on parental stress based on 11 studies with 632 participants. RESULTS: Compared to treatment as usual, minimal or no treatment, comprehensive ABA-based interventions showed medium effects for intellectual functioning (standardized mean difference SMD = 0.51, 95% CI [0.09; 0.92]) and adaptive behavior (SMD = 0.37, 95% CI [0.03; 0.70]). Language abilities, symptom severity or parental stress did not improve beyond the improvement in control groups. Moderator analyses indicate that language abilities at intake could influence the effect sizes and the influence of treatment intensity might decrease with older age. CONCLUSIONS: Practical implications and limitations are discussed.

Random-effects meta-analysis models for the odds ratio in the case of rare events under different data-generating models: A simulation study.

Meta-analysis of binary data is challenging when the event under investigation is rare, and standard models for random-effects meta-analysis perform poorly in such settings. In this simulation study, we investigate the performance of different random-effects meta-analysis models in terms of point and interval estimation of the pooled log odds ratio in rare events meta-analysis. First and foremost, we evaluate the performance of a hypergeometric-normal model from the family of generalized linear mixed models (GLMMs), which has been recommended, but has not yet been thoroughly investigated for rare events meta-analysis. Performance of this model is compared to performance of the beta-binomial model, which yielded favorable results in previous simulation studies, and to the performance of models that are frequently used in rare events meta-analysis, such as the inverse variance model and the Mantel-Haenszel method. In addition to considering a large number of simulation parameters inspired by real-world data settings, we study the comparative performance of the meta-analytic models under two different data-generating models (DGMs) that have been used in past simulation studies. The results of this study show that the hypergeometric-normal GLMM is useful for meta-analysis of rare events when moderate to large heterogeneity is present. In addition, our study reveals important insights with regard to the performance of the beta-binomial model under different DGMs from the binomial-normal family. In particular, we demonstrate that although misalignment of the beta-binomial model with the DGM affects its performance, it shows more robustness to the DGM than its competitors.

Estimating risk and rate ratio in rare events meta-analysis with the Mantel-Haenszel estimator and assessing heterogeneity.

Meta-analysis of binary outcome data faces often a situation where studies with a rare event are part of the set of studies to be considered. These studies have low occurrence of event counts to the extreme that no events occur in one or both groups to be compared. This raises issues how to estimate validly the summary risk or rate ratio across studies. A preferred choice is the Mantel-Haenszel estimator, which is still defined in the situation of zero studies unless all studies have zeros in one of the groups to be compared. For this situation, a modified Mantel-Haenszel estimator is suggested and shown to perform well by means of simulation work. Also, confidence interval estimation is discussed and evaluated in a simulation study. In a second part, heterogeneity of relative risk across studies is investigated with a new chi-square type statistic which is based on a conditional binomial distribution where the conditioning is on the event margin for each study. This is necessary as the conventional Q-statistic is undefined in the occurrence of zero studies. The null-distribution of the proposed Q-statistic is obtained by means of a parametric bootstrap as a chi-square approximation is not valid for rare events meta-analysis, as bootstrapping of the null-distribution shows. In addition, for the effect heterogeneity situation, confidence interval estimation is considered using a nonparametric bootstrap procedure. The proposed techniques are illustrated at hand of three meta-analytic data sets.

The relationship of trauma-related guilt with PTSD symptoms in adult trauma survivors: a meta-analysis.

Posttraumatic stress disorder (PTSD) is a severe condition that is associated with trauma-related guilt. We aimed at providing a comprehensive quantitative systematic review on the relationship between trauma-related guilt and adult PTSD. Database searches in Medline, PsycINFO, PTSDpubs and Web of Knowledge resulted in the inclusion of 163 eligible studies with a total of 35 020 trauma survivors. The studies reported on 157 cross-sectional and 19 longitudinal data points. Overall, we included 135 studies not included in previous meta-analyses. Random-effect models yielded a moderate cross-sectional correlation (r = 0.38, 95% CI 0.35-0.42, p < 0.001, I2 = 90.3%) and a small to moderate predictive correlation (r = 0.21, 95% CI 0.13-0.29, p < 0.001, I2 = 66.7%). The association appeared to be stable over time and was robust to sensitivity analyses. All symptom clusters significantly correlated with guilt. No effects were found for military v. civilian populations or clinical v. non-clinical samples. Effects were smaller for high-quality studies and larger for instruments based on DSM-5. Further significant moderators were type of guilt measure and trauma type. The largest association was found among participants reporting war-related trauma (r = 0.44, 95% CI 0.36-0.51) and the smallest among survivors of motor-vehicle accidents (r = 0.18, 95% CI 0.02-0.33). The results underpin the role of trauma-related guilt in the onset and maintenance of PTSD symptoms, which have important clinical implications. Future studies should further explore the change interactions of guilt and PTSD symptoms.

Persistent neuropsychiatric symptoms after COVID-19: a systematic review and meta-analysis.

The nature and extent of persistent neuropsychiatric symptoms after COVID-19 are not established. To help inform mental health service planning in the pandemic recovery phase, we systematically determined the prevalence of neuropsychiatric symptoms in survivors of COVID-19. For this pre-registered systematic review and meta-analysis (PROSPERO ID CRD42021239750), we searched MEDLINE, EMBASE, CINAHL and PsycINFO to 20 February 2021, plus our own curated database. We included peer-reviewed studies reporting neuropsychiatric symptoms at post-acute or later time-points after COVID-19 infection and in control groups where available. For each study, a minimum of two authors extracted summary data. For each symptom, we calculated a pooled prevalence using generalized linear mixed models. Heterogeneity was measured with I 2. Subgroup analyses were conducted for COVID-19 hospitalization, severity and duration of follow-up. From 2844 unique titles, we included 51 studies (n = 18 917 patients). The mean duration of follow-up after COVID-19 was 77 days (range 14-182 days). Study quality was most commonly moderate. The most prevalent neuropsychiatric symptom was sleep disturbance [pooled prevalence = 27.4% (95% confidence interval 21.4-34.4%)], followed by fatigue [24.4% (17.5-32.9%)], objective cognitive impairment [20.2% (10.3-35.7%)], anxiety [19.1% (13.3-26.8%)] and post-traumatic stress [15.7% (9.9-24.1%)]. Only two studies reported symptoms in control groups, both reporting higher frequencies in COVID-19 survivors versus controls. Between-study heterogeneity was high (I 2 = 79.6-98.6%). There was little or no evidence of differential symptom prevalence based on hospitalization status, severity or follow-up duration. Neuropsychiatric symptoms are common and persistent after recovery from COVID-19. The literature on longer-term consequences is still maturing but indicates a particularly high prevalence of insomnia, fatigue, cognitive impairment and anxiety disorders in the first 6 months after infection.

Estimation of Effect Heterogeneity in Rare Events Meta-Analysis.

The paper outlines several approaches for dealing with meta-analyses of count outcome data. These counts are the accumulation of occurred events, and these events might be rare, so a special feature of the meta-analysis is dealing with low counts including zero-count studies. Emphasis is put on approaches which are state of the art for count data modelling including mixed log-linear (Poisson) and mixed logistic (binomial) regression as well as nonparametric mixture models for count data of Poisson and binomial type. A simulation study investigates the performance and capability of discrete mixture models in estimating effect heterogeneity. The approaches are exemplified on a meta-analytic case study investigating the acceptance of bibliotherapy.

ICAOD: An R Package for Finding Optimal designs for Nonlinear Statistical Models by Imperialist Competitive Algorithm.

Optimal design ideas are increasingly used in different disciplines to rein in experimental costs. Given a nonlinear statistical model and a design criterion, optimal designs determine the number of experimental points to observe the responses, the design points and the number of replications at each design point. Currently, there are very few free and effective computing tools for finding different types of optimal designs for a general nonlinear model, especially when the criterion is not differentiable. We introduce an R package ICAOD to find various types of optimal designs and they include locally, minimax and Bayesian optimal designs for different nonlinear statistical models. Our main computational tool is a novel metaheuristic algorithm called imperialist competitive algorithm (ICA) and inspired by socio-political behavior of humans and colonialism. We demonstrate its capability and effectiveness using several applications. The package also includes several theory-based tools to assess optimality of a generated design when the criterion is a convex function of the design.

Neurology and neuropsychiatry of COVID-19: a systematic review and meta-analysis of the early literature reveals frequent CNS manifestations and key emerging narratives.

There is accumulating evidence of the neurological and neuropsychiatric features of infection with SARS-CoV-2. In this systematic review and meta-analysis, we aimed to describe the characteristics of the early literature and estimate point prevalences for neurological and neuropsychiatric manifestations.We searched MEDLINE, Embase, PsycINFO and CINAHL up to 18 July 2020 for randomised controlled trials, cohort studies, case-control studies, cross-sectional studies and case series. Studies reporting prevalences of neurological or neuropsychiatric symptoms were synthesised into meta-analyses to estimate pooled prevalence.13 292 records were screened by at least two authors to identify 215 included studies, of which there were 37 cohort studies, 15 case-control studies, 80 cross-sectional studies and 83 case series from 30 countries. 147 studies were included in the meta-analysis. The symptoms with the highest prevalence were anosmia (43.1% (95% CI 35.2% to 51.3%), n=15 975, 63 studies), weakness (40.0% (95% CI 27.9% to 53.5%), n=221, 3 studies), fatigue (37.8% (95% CI 31.6% to 44.4%), n=21 101, 67 studies), dysgeusia (37.2% (95% CI 29.8% to 45.3%), n=13 686, 52 studies), myalgia (25.1% (95% CI 19.8% to 31.3%), n=66 268, 76 studies), depression (23.0% (95% CI 11.8% to 40.2%), n=43 128, 10 studies), headache (20.7% (95% CI 16.1% to 26.1%), n=64 613, 84 studies), anxiety (15.9% (5.6% to 37.7%), n=42 566, 9 studies) and altered mental status (8.2% (95% CI 4.4% to 14.8%), n=49 326, 19 studies). Heterogeneity for most clinical manifestations was high.Neurological and neuropsychiatric symptoms of COVID-19 in the pandemic's early phase are varied and common. The neurological and psychiatric academic communities should develop systems to facilitate high-quality methodologies, including more rapid examination of the longitudinal course of neuropsychiatric complications of newly emerging diseases and their relationship to neuroimaging and inflammatory biomarkers.

The Relationship between Intelligence and Divergent Thinking-A Meta-Analytic Update.

This paper provides a meta-analytic update on the relationship between intelligence and divergent thinking (DT), as research on this topic has increased, and methods have diversified since Kim's meta-analysis in 2005. A three-level meta-analysis was used to analyze 849 correlation coefficients from 112 studies with an overall N = 34,610. The overall effect showed a significant positive correlation of r = .25. This increase of the correlation as compared to Kim's prior meta-analytic findings could be attributed to the correction of attenuation because a difference between effect sizes prior-Kim vs. post-Kim was non-significant. Different moderators such as scoring methods, instructional settings, intelligence facets, and task modality were tested together with theoretically relevant interactions between some of these factors. These moderation analyses showed that the intelligence-DT relationship can be higher (up to r = .31-.37) when employing test-like assessments coupled with be-creative instructions, and considering DT originality scores. The facet of intelligence (g vs. gf vs. gc) did not affect the correlation between intelligence and DT. Furthermore, we found two significant sample characteristics: (a) average sample age was positively associated with the intelligence-DT correlation, and (b) the intelligence-DT correlation decreased for samples with increasing percentages of females in the samples. Finally, inter-moderator correlations were checked to take potential confounding into account, and also publication bias was assessed. This meta-analysis provides a comprehensive picture of current research and possible research gaps. Theoretical implications, as well as recommendations for future research, are discussed.

An Experimental Approach to Investigate the Involvement of Cognitive Load in Divergent Thinking.

Up to now, support for the idea that a controlled component exists in creative thought has mainly been supported by correlational studies; to further shed light on this issue, we employed an experimental approach. We used four alternate uses tasks that differed in instruction type ("be fluent" vs. "be creative") and concurrent secondary workload (load vs. no load). A total of 51 participants (39 female) went through all tasks and generated ideas for a total of 16 different objects; their responses were scored in terms of fluency (number of responses generated), creative quality, and flexibility. We did find, as expected, that the be-creative instruction resulted in fewer and more creative ideas, as well as more flexible idea sets, but neither of the expected interaction effects became significant. Specifically, fluency was not affected more strongly by secondary workload in the be-fluent instruction condition than in the be-creative instruction condition. Further, the performance drop evoked by the secondary workload was not stronger in the be-creative instruction condition compared to the be-fluent instruction condition when creative quality or flexibility were examined as dependent variable. Altogether, our results do not confirm that be-creative instructions involve more cognitive load than be-fluent instructions. Nevertheless, the analysis of the serial order effect and additional correlational examinations revealed some promising results. Methodological limitations which may have influenced the results are discussed in light of the inherent suspense between internal and external validity (i.e., most likely the applied self-paced dual-task approach increased external validity, but undermined internal validity) and potentially guide future research.

Can High-Dimensional Questionnaires Resolve the Ipsativity Issue of Forced-Choice Response Formats?

Forced-choice questionnaires can prevent faking and other response biases typically associated with rating scales. However, the derived trait scores are often unreliable and ipsative, making interindividual comparisons in high-stakes situations impossible. Several studies suggest that these problems vanish if the number of measured traits is high. To determine the necessary number of traits under varying sample sizes, factor loadings, and intertrait correlations, simulations were performed for the two most widely used scoring methods, namely the classical (ipsative) approach and Thurstonian item response theory (IRT) models. Results demonstrate that while especially Thurstonian IRT models perform well under ideal conditions, both methods yield insufficient reliabilities in most conditions resembling applied contexts. Moreover, not only the classical estimates but also the Thurstonian IRT estimates for questionnaires with equally keyed items remain (partially) ipsative, even when the number of traits is very high (i.e., 30). This result not only questions earlier assumptions regarding the use of classical scores in high-dimensional questionnaires, but it also raises doubts about many validation studies on Thurstonian IRT models because correlations of (partially) ipsative scores with external criteria cannot be interpreted in a usual way.

Blood-Based ATN Biomarkers of Alzheimer's Disease: A Meta-Analysis.

BACKGROUND: The Amyloid Tau Neurodegeneration (ATN) framework was proposed to define the biological state underpinning Alzheimer's disease (AD). Blood-based biomarkers offer a scalable alternative to the costly and invasive currently available biomarkers. OBJECTIVE: In this meta-analysis we sought to assess the diagnostic performance of plasma amyloid (Aß40, Aß42, Aß42/40 ratio), tangle (p-tau181), and neurodegeneration (total tau [t-tau], neurofilament light [NfL]) biomarkers. METHODS: Electronic databases were screened for studies reporting biomarker concentrations for AD and control cohorts. Biomarker performance was examined by random-effect meta-analyses based on the ratio between biomarker concentrations in patients and controls. RESULTS: 83 studies published between 1996 and 2020 were included in the analyses. Aß42/40 ratio as well as Aß42 discriminated AD patients from controls when using novel platforms such as immunomagnetic reduction (IMR). We found significant differences in ptau-181 concentration for studies based on single molecule array (Simoa), but not for studies based on IMR or ELISA. T-tau was significantly different between AD patients and control in IMR and Simoa but not in ELISA-based studies. In contrast, NfL differentiated between groups across platforms. Exosome studies showed strong separation between patients and controls for Aß42, t-tau, and p-tau181. CONCLUSION: Currently available assays for sampling plasma ATN biomarkers appear to differentiate between AD patients and controls. Novel assay methodologies have given the field a significant boost for testing these biomarkers, such as IMR for Aß, Simoa for p-tau181. Enriching samples through extracellular vesicles shows promise but requires further validation.

Introducing a Computerized Figural Memory Test Based on Automatic Item Generation: An Analysis With the Rasch Poisson Counts Model.

An automatic item generator for figural memory test items called figumem was developed. It is available in R. A cognitive model allowed the generation of hypothetically parallel items within three difficulty levels determined by visual information load. In a pilot study, participants solved three items for each level of visual load. Within an item response theory approach, the Rasch Poisson counts model and modifications of it were fitted to the data. Results showed overall satisfying fit. Visual information load explained most of the variance in item difficulty. Differences in difficulty between items of the same family were comparatively low, displaying the utility of the item generator for the creation of parallel test forms. Implications, limitations, and suggestions for the use and extensions of figumem are discussed.

Comparison of random-effects meta-analysis models for the relative risk in the case of rare events: A simulation study.

Pooling the relative risk (RR) across studies investigating rare events, for example, adverse events, via meta-analytical methods still presents a challenge to researchers. The main reason for this is the high probability of observing no events in treatment or control group or both, resulting in an undefined log RR (the basis of standard meta-analysis). Other technical challenges ensue, for example, the violation of normality assumptions, or bias due to exclusion of studies and application of continuity corrections, leading to poor performance of standard approaches. In the present simulation study, we compared three recently proposed alternative models (random-effects [RE] Poisson regression, RE zero-inflated Poisson [ZIP] regression, binomial regression) to the standard methods in conjunction with different continuity corrections and to different versions of beta-binomial regression. Based on our investigation of the models' performance in 162 different simulation settings informed by meta-analyses from the Cochrane database and distinguished by different underlying true effects, degrees of between-study heterogeneity, numbers of primary studies, group size ratios, and baseline risks, we recommend the use of the RE Poisson regression model. The beta-binomial model recommended by Kuss (2015) also performed well. Decent performance was also exhibited by the ZIP models, but they also had considerable convergence issues. We stress that these recommendations are only valid for meta-analyses with larger numbers of primary studies. All models are applied to data from two Cochrane reviews to illustrate differences between and issues of the models. Limitations as well as practical implications and recommendations are discussed; a flowchart summarizing recommendations is provided.

Estimating the undetected infections in the Covid-19 outbreak by harnessing capture-recapture methods.

OBJECTIVES: A major open question, affecting the decisions of policy makers, is the estimation of the true number of Covid-19 infections. Most of them are undetected, because of a large number of asymptomatic cases. We provide an efficient, easy to compute and robust lower bound estimator for the number of undetected cases. METHODS: A modified version of the Chao estimator is proposed, based on the cumulative time-series distributions of cases and deaths. Heterogeneity has been addressed by assuming a geometrical distribution underlying the data generation process. An (approximated) analytical variance of the estimator has been derived to compute reliable confidence intervals at 95% level. RESULTS: A motivating application to the Austrian situation is provided and compared with an independent and representative study on prevalence of Covid-19 infection. Our estimates match well with the results from the independent prevalence study, but the capture-recapture estimate has less uncertainty involved as it is based on a larger sample size. Results from other European countries are mentioned in the discussion. The estimated ratio of the total estimated cases to the observed cases is around the value of 2.3 for all the analyzed countries. CONCLUSIONS: The proposed method answers to a fundamental open question: "How many undetected cases are going around?". CR methods provide a straightforward solution to shed light on undetected cases, incorporating heterogeneity that may arise in the probability of being detected.

Disease-modifying treatments and cognition in relapsing-remitting multiple sclerosis: A meta-analysis.

OBJECTIVE: Disease-modifying treatments (DMTs) are the gold standard for slowing disability progression in multiple sclerosis (MS), but their effects on cognitive impairment, a key symptom of the disease, are mostly unknown. We conducted a systematic review and meta-analysis to evaluate the differential effects of DMTs on cognitive test performance in relapsing-remitting MS (RRMS). METHODS: PubMed, Scopus, and Cochrane Library were searched for studies reporting longitudinal cognitive performance data related to all major DMTs. The standardized mean difference (Hedges g) between baseline and follow-up cognitive assessment was used as the main effect size measure. RESULTS: Forty-four studies, including 55 distinct MS patient samples, were found eligible for the systematic review. Twenty-five studies were related to platform therapies (mainly ß-interferon [n = 17] and glatiramer acetate [n = 4]), whereas 22 studies were related to escalation therapies (mainly natalizumab [n = 14] and fingolimod [n = 6]). Reported data were mostly confined to the cognitive domain processing speed. A meta-analysis including 41 studies and 7,131 patients revealed a small to moderate positive effect on cognitive test performance of DMTs in general (g = 0.27, 95% confidence interval [CI] = [0.21-0.33]), but no statistically significant differences between platform (g = 0.27, 95% CI = [0.18-0.35]) and escalation therapies (g = 0.28, 95% CI = [0.19-0.37]) or between any single DMT and ß-interferon. CONCLUSIONS: DMTs are effective in improving cognitive test performance in RRMS, but a treatment escalation mainly to amend cognition is not supported by the current evidence. Given the multitude of DMTs and their widespread use, the available data regarding differential treatment effects on cognitive impairment are remarkably scant. Clinical drug trials that use more extensive cognitive outcome measures are urgently needed.