Summary
Meta‐analysis and meta‐regression are statistical methods for synthesizing and modelling the results of different studies, and are critical research synthesis tools in ecology and ...evolutionary biology (E&E). However, many E&E researchers carry out meta‐analyses using software that is limited in its statistical functionality and is not easily updatable. It is likely that these software limitations have slowed the uptake of new methods in E&E and limited the scope and quality of inferences from research syntheses.
We developed OpenMEE: Open Meta‐analyst for Ecology and Evolution to address the need for advanced, easy‐to‐use software for meta‐analysis and meta‐regression. OpenMEE has a cross‐platform, easy‐to‐use graphical user interface (GUI) that gives E&E researchers access to the diverse and advanced statistical functionalities offered in R, without requiring knowledge of R programming.
OpenMEE offers a suite of advanced meta‐analysis and meta‐regression methods for synthesizing continuous and categorical data, including meta‐regression with multiple covariates and their interactions, phylogenetic analyses, and simple missing data imputation. OpenMEE also supports data importing and exporting, exploratory data analysis, graphing of data, and summary table generation.
As intuitive, open‐source, free software for advanced methods in meta‐analysis, OpenMEE meets the current and pressing needs of the E&E community for teaching meta‐analysis and conducting high‐quality syntheses. Because OpenMEE's statistical components are written in R, new methods and packages can be rapidly incorporated into the software. To fully realize the potential of OpenMEE, we encourage community development with an aim to advance the capabilities of meta‐analyses in E&E.
Statistical tests for funnel-plot asymmetry are common in meta-analyses. Inappropriate application can generate misleading inferences about publication bias. We aimed to measure, in a survey of ...meta-analyses, how frequently the application of these tests would be not meaningful or inappropriate.
We evaluated all meta-analyses of binary outcomes with é 3 studies in the Cochrane Database of Systematic Reviews (2003, issue 2). A separate, restricted analysis was confined to the largest meta-analysis in each of the review articles. In each meta-analysis, we assessed whether criteria to apply asymmetry tests were met: no significant heterogeneity, I2 < 50%, é 10 studies (with statistically significant results in at least 1) and ratio of the maximal to minimal variance across studies > 4. We performed a correlation and 2 regression asymmetry tests and evaluated their concordance. Finally, we sampled 60 meta-analyses from print journals in 2005 that cited use of the standard regression test.
A total of 366 of 6873 (5%) and 98 of 846 meta-analyses (12%) in the wider and restricted Cochrane data set, respectively, would have qualified for use of asymmetry tests. Asymmetry test results were significant in 7%-18% of the meta-analyses. Concordance between the 3 tests was modest (estimated k 0.33-0.66). Of the 60 journal meta-analyses, 7 (12%) would qualify for asymmetry tests; all 11 claims for identification of publication bias were made in the face of large and significant heterogeneity.
Statistical conditions for employing asymmetry tests for publication bias are absent from most meta-analyses; yet, in medical journals these tests are performed often and interpreted erroneously.
The R environment provides a natural platform for developing new statistical methods due to the mathematical expressiveness of the language, the large number of existing libraries, and the active ...developer community. One drawback to R, however, is the learning curve; programming is a deterrent to non-technical users, who typically prefer graphical user interfaces (GUIs) to command line environments. Thus, while statisticians develop new methods in R, practitioners are often behind in terms of the statistical techniques they use as they rely on GUI applications. Meta-analysis is an instructive example; cutting-edge meta-analysis methods are often ignored by the overwhelming majority of practitioners, in part because they have no easy way of applying them. This paper proposes a strategy to close the gap between the statistical state-of-the-science and what is applied in practice. We present open-source meta-analysis software that uses R as the underlying statistical engine, and Python for the GUI. We present a framework that allows methodologists to implement new methods in R that are then automatically integrated into the GUI for use by end-users, so long as the programmer conforms to our interface. Such an approach allows an intuitive interface for non-technical users while leveraging the latest advanced statistical methods implemented by methodologists.
Online physician reviews are a massive and potentially rich source of information capturing patient sentiment regarding healthcare. We analyze a corpus comprising nearly 60,000 such reviews with a ...state-of-the-art probabilistic model of text. We describe a probabilistic generative model that captures latent sentiment across aspects of care (eg, interpersonal manner). We target specific aspects by leveraging a small set of manually annotated reviews. We perform regression analysis to assess whether model output improves correlation with state-level measures of healthcare. We report both qualitative and quantitative results. Model output correlates with state-level measures of quality healthcare, including patient likelihood of visiting their primary care physician within 14 days of discharge (p=0.03), and using the proposed model better predicts this outcome (p=0.10). We find similar results for healthcare expenditure. Generative models of text can recover important information from online physician reviews, facilitating large-scale analyses of such reviews.
Purpose:
Bayesian calibration is generally superior to standard direct-search algorithms in that it estimates the full joint posterior distribution of the calibrated parameters. However, there are ...many barriers to using Bayesian calibration in health decision sciences stemming from the need to program complex models in probabilistic programming languages and the associated computational burden of applying Bayesian calibration. In this paper, we propose to use artificial neural networks (ANN) as one practical solution to these challenges.
Methods:
Bayesian Calibration using Artificial Neural Networks (BayCANN) involves (1) training an ANN metamodel on a sample of model inputs and outputs, and (2) then calibrating the trained ANN metamodel instead of the full model in a probabilistic programming language to obtain the posterior joint distribution of the calibrated parameters. We illustrate BayCANN using a colorectal cancer natural history model. We conduct a confirmatory simulation analysis by first obtaining parameter estimates from the literature and then using them to generate adenoma prevalence and cancer incidence targets. We compare the performance of BayCANN in recovering these “true” parameter values against performing a Bayesian calibration directly on the simulation model using an incremental mixture importance sampling (IMIS) algorithm.
Results:
We were able to apply BayCANN using only a dataset of the model inputs and outputs and minor modification of BayCANN's code. In this example, BayCANN was slightly more accurate in recovering the true posterior parameter estimates compared to IMIS. Obtaining the dataset of samples, and running BayCANN took 15 min compared to the IMIS which took 80 min. In applications involving computationally more expensive simulations (e.g., microsimulations), BayCANN may offer higher relative speed gains.
Conclusions:
BayCANN only uses a dataset of model inputs and outputs to obtain the calibrated joint parameter distributions. Thus, it can be adapted to models of various levels of complexity with minor or no change to its structure. In addition, BayCANN's efficiency can be especially useful in computationally expensive models. To facilitate BayCANN's wider adoption, we provide BayCANN's open-source implementation in R and Stan.
IMPORTANCE: Computed tomographic (CT) scanning is the standard for the rapid diagnosis of intracranial injury, but it is costly and exposes patients to ionizing radiation. The Pediatric Emergency ...Care Applied Research Network (PECARN) rules for identifying children with minor head trauma who are at very low risk of clinically important traumatic brain injury (ciTBI) are widely used to triage CT imaging. OBJECTIVE: To examine whether optimal classification trees (OCTs), which are novel machine-learning classifiers, improve on PECARN rules’ predictive accuracy. DESIGN, SETTING, AND PARTICIPANTS: A secondary analysis of prospective, publicly available data on emergency department visits for head trauma used by the PECARN group to develop their tool was conducted to derive OCT-based prediction rules for ciTBI in a development cohort and compare their predictive performance vs the PECARN rules in a validation cohort among children who were younger than 2 years and 2 years or older. Data on 42 412 children with head trauma and without severely altered mental status who were examined between June 1, 2004, and September 30, 2006, were gathered from 25 emergency departments in North America participating in PECARN. Data analysis was conducted from September 15, 2016, to December 18, 2018. MAIN OUTCOMES AND MEASURES: The outcome was ciTBI, with predictive performance measured by estimating the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio for the OCT and the PECARN rules. The OCT and PECARN rules’ performance was compared by estimating ratios for each measure. RESULTS: Of the 42 412 children (15 996 37.7% girls) included in the analysis, 10 718 were younger than 2 years (25.3%; mean SD age, 11.6 0.6 months) and 31 694 were 2 years or older (74.7%; age, 9.1 4.9 years). Compared with PECARN rules, OCTs misclassified 0 vs 1 child with ciTBI in the younger and 10 vs 9 children with ciTBI in the older cohort, and correctly identified more children with very low risk of ciTBI in the younger (7605 vs 5701) and older (20 594 vs 18 134) cohorts. In the validation cohorts, compared with the PECARN rules, the OCTs had statistically significantly better specificity (in the younger cohort: 69.3%; 95% CI, 67.4%-71.2% vs 52.8%; 95% CI, 50.8%-54.9%; in the older cohort: 65.6%; 95% CI, 64.5%-66.8% vs 57.6%; 95% CI, 56.4%-58.8%), positive predictive value (odds ratios, 1.54; 95% CI, 1.36-1.74 and 1.23; 95% CI, 1.17-1.30, in younger and older children, respectively), and positive likelihood ratio (risk ratios, 1.54; 95% CI, 1.36-1.74 and 1.23; 95% CI, 1.17-1.30, in younger and older children, respectively). There were no statistically significant differences in the sensitivity, negative predictive value, and negative likelihood ratio between the 2 sets of rules. CONCLUSIONS AND RELEVANCE: If implemented, OCTs may help reduce the number of unnecessary CT scans, without missing more patients with ciTBI than the PECARN rules.
Oral mechanical bowel preparation is often used before elective colorectal surgery to reduce postoperative complications.
The purpose of this study was to synthesize the evidence on the comparative ...effectiveness and safety of oral mechanical bowel preparation versus no preparation or enema.
We searched MEDLINE, the Cochrane Central Register of Controlled Trials, Embase, and CINAHL without any language restrictions (last search on September 6, 2013). We also searched the US Food and Drug Administration Web site and ClinicalTrials.gov and supplemented our searches by asking technical experts and perusing reference lists.
We included English-language, full-text reports of randomized clinical trials and nonrandomized comparative studies of patients undergoing elective colon or rectal surgery. For adverse events we also included single-group cohort studies of at least 200 participants.
Interventions included oral mechanical bowel preparation, oral mechanical bowel preparation plus enema, enema only, and no oral mechanical bowel preparation or enema.
Anastomotic leakage, all-cause mortality, wound infection, peritonitis/intra-abdominal abscess, reoperation, surgical site infection, quality of life, length of stay, and adverse events were measured. We synthesized results across studies qualitatively and with Bayesian random-effects meta-analyses.
A total of 18 randomized clinical trials, 7 nonrandomized comparative studies, and 6 single-group cohorts were included. In meta-analyses of randomized clinical trials, the credibility intervals of the summary OR included the null value of 1.0 for comparisons of oral mechanical bowel preparation and either no oral preparation or enema for overall mortality, anastomotic leakage, wound infection, peritonitis, surgical site infection, and reoperation. These results were robust to extensive sensitivity analyses. Evidence on adverse events was sparse.
The study was limited by weaknesses in the underlying evidence, such as incomplete reporting of relevant information, exclusion of non-English and relevant unpublished studies, and possible missed indexing of nonrandomized studies.
Our results could not exclude modest beneficial or harmful effects of oral mechanical bowel preparation compared with no preparation or enema.
The Strategic Timing of AntiRetroviral Treatment (START) trial found that in patients with HIV and CD4
cell counts above 500 cells/mm
who had not previously received treatment, immediate initiation ...of antiretroviral therapy reduced the risk of serious adverse outcomes compared with delaying treatment initiation only when the CD4
cell count fell below 350 cells/mm
.
After the trial's completion, people with HIV not receiving therapy were offered the opportunity to start it without regard to their CD4
cell count. In this issue of
the START group reports data from an additional 5 years of follow-up.
.
ABSTRACT
BACKGROUND
In 2012, the United States Food and Drug Administration (FDA) issued a warning regarding potential adverse effects of HMG-CoA reductase inhibitors (statins) on cognition, based on ...the Adverse Events Reporting System and a review of the medical literature. We aimed to synthesize randomized clinical trial (RCTs) evidence on the association between statin therapy and cognitive outcomes.
METHODS
We searched MEDLINE, EMBASE, and Cochrane CENTRAL through December 2012, and reviewed published systematic reviews of statin treatment. We sought RCTs that compared statin treatment versus placebo or standard care, and reported at least one cognitive outcome (frequency of adverse cognitive events or measurements using standard neuropsychological cognitive test scores). Studies reporting sufficient information to calculate effect sizes were included in meta-analyses. Standardized and unstandardized mean differences were calculated for continuous outcomes for global cognition and for pre-specified cognitive domains. The main outcome was change in cognition measured by neuropsychological tests; an outcome of secondary interest was the frequency of adverse cognitive events observed during follow-up.
RESULTS
We identified 25 RCTs (all placebo-controlled) reporting cognitive outcomes in 46,836 subjects, of which 23 RCTs reported cognitive test results in 29,012 participants. Adverse cognitive outcomes attributable to statins were rarely reported in trials involving cognitively normal or impaired subjects. Furthermore, meta-analysis of cognitive test data (14 studies; 27,643 participants) failed to show significant adverse effects of statins on all tests of cognition in either cognitively normal subjects (standardized mean difference 0.01, 95 % confidence interval, CI, −0.01 to 0.03,
p
= 0.42) or Alzheimer’s disease subjects (standardized mean difference −0.05, 95 % CI −0.19 to 0.10,
p
= 0.38).
CONCLUSIONS
Statin therapy was not associated with cognitive impairment in RCTs. These results raise questions regarding the continued merit of the FDA warning about potential adverse effects of statins on cognition.
This article summarizes the Agency for Healthcare Research and Quality's evidence report on the effects of breastfeeding on term infant and maternal health outcomes in developed countries.
Medline, ...CINAHL, Cochrane Library, bibliographies of selected reviews, and suggestions from domain experts were surveyed. Searches were limited to English-language publications.
Eligible comparisons examined the association between differential exposure to breastfeeding and health outcomes. We assessed 15 infant and six maternal outcomes. For four outcomes, we also updated previously published systematic reviews. For the rest of the outcomes, we either summarized previous systematic reviews or conducted new systematic reviews; randomized and non-randomized comparative trials, prospective cohorts, and case-control studies were included. Adjusted estimates were extracted from non-experimental designs. The studies were graded for methodological quality. We did not draw conclusions from poor quality studies.
We screened over 9,000 abstracts. Thirty-two primary studies on term infant health outcomes, 43 primary studies on maternal health outcomes, and 28 systematic reviews or meta-analyses that covered approximately 400 individual studies were included in this review. A history of breastfeeding was associated with a reduction in the risk of acute otitis media, nonspecific gastroenteritis, severe lower respiratory tract infections, atopic dermatitis, asthma (young children), obesity, type 1 and 2 diabetes, childhood leukemia, and sudden infant death syndrome. There was no relationship between breastfeeding in term infants and cognitive performance. There were insufficient good quality data to address the relationship between breastfeeding and cardiovascular diseases and infant mortality. For maternal outcomes, a history of lactation was associated with a reduced risk of type 2 diabetes, breast, and ovarian cancer. Early cessation of breastfeeding or no breastfeeding was associated with an increased risk of maternal postpartum depression. There was no relationship between a history of lactation and the risk of osteoporosis. The effect of breastfeeding in mothers on return-to-prepregnancy weight was negligible, and the effect of breastfeeding on postpartum weight loss was unclear.
A history of breastfeeding is associated with a reduced risk of many diseases in infants and mothers. Future research would benefit from clearer selection criteria, definitions of breastfeeding exposure, and adjustment for potential confounders. Matched designs such as sibling analysis may provide a method to control for hereditary and household factors that are important in certain outcomes.
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