Permutation inference methods for multivariate meta-analysis

Multivariate meta-analysis is gaining prominence in evidence synthesis research because it enables simultaneous synthesis of multiple correlated outcome data, and random-effects models have generally been used for addressing between-studies heterogeneities. However, coverage probabilities of confidence regions or intervals for standard inference methods for random-effects models (e.g., restricted maximum likelihood estimation) cannot retain their nominal confidence levels in general, especially when the number of synthesized studies is small because their validities depend on large sample approximations. In this article, we provide permutation-based inference methods that enable exact joint inferences for average outcome measures without large sample approximations. We also provide accurate marginal inference methods under general settings of multivariate meta-analyses. We propose effective approaches for permutation inferences using optimal weighting based on the efficient score statistic. The effectiveness of the proposed methods is illustrated via applications to bivariate meta-analyses of diagnostic accuracy studies for airway eosinophilia in asthma and a network meta-analysis for antihypertensive drugs on incident diabetes, as well as through simulation experiments. In numerical evaluations performed via simulations, our methods generally provided accurate confidence regions or intervals under a broad range of settings, whereas the current standard inference methods exhibited serious undercoverage properties.Comment: 20 pages, 2 figures, 2 tabl

Confidence intervals of prediction accuracy measures for multivariable prediction models based on the bootstrap-based optimism correction methods

In assessing prediction accuracy of multivariable prediction models, optimism corrections are essential for preventing biased results. However, in most published papers of clinical prediction models, the point estimates of the prediction accuracy measures are corrected by adequate bootstrap-based correction methods, but their confidence intervals are not corrected, e.g., the DeLong's confidence interval is usually used for assessing the C-statistic. These naive methods do not adjust for the optimism bias and do not account for statistical variability in the estimation of parameters in the prediction models. Therefore, their coverage probabilities of the true value of the prediction accuracy measure can be seriously below the nominal level (e.g., 95%). In this article, we provide two generic bootstrap methods, namely (1) location-shifted bootstrap confidence intervals and (2) two-stage bootstrap confidence intervals, that can be generally applied to the bootstrap-based optimism correction methods, i.e., the Harrell's bias correction, 0.632, and 0.632+ methods. In addition, they can be widely applied to various methods for prediction model development involving modern shrinkage methods such as the ridge and lasso regressions. Through numerical evaluations by simulations, the proposed confidence intervals showed favourable coverage performances. Besides, the current standard practices based on the optimism-uncorrected methods showed serious undercoverage properties. To avoid erroneous results, the optimism-uncorrected confidence intervals should not be used in practice, and the adjusted methods are recommended instead. We also developed the R package predboot for implementing these methods (https://github.com/nomahi/predboot). The effectiveness of the proposed methods are illustrated via applications to the GUSTO-I clinical trial

Errors in Relative Risks Reported in Figure 3 in a Network Meta-analysis of Cognitive Behavior Therapy Delivery Formats in Adults with Depression

To the Editor: The authors regret to report that Figure 3B was not represented correctly in their Original Investigation, “Effectiveness and Acceptability of Cognitive Behavior Therapy Delivery Formats in Adults With Depression: A Network Meta-analysis,”1 published in the July 2019 issue. In reviewing the article for a presentation, a coauthor detected the errors. In the original Figure 3B, we had shown the dropouts of care as usual over each of the treatment formats, instead of dropouts of the formats over care as usual. But Figure 3B supposed care as usual to be the common comparator, so the correct representation should give the dropouts of the various formats over care as usual. We therefore had to reverse the relative risks. Because of this adjustment, we had to change a number in the text and add a phrase for context, but none of these changes affect the interpretations or conclusions of the study. Thus, we have requested that our article be corrected.2 The authors apologize for any inconvenience caused. This article was previously corrected on July 17, 2019, to fix a label error in Figure 3B.

Initial severity of depression and efficacy of cognitive-behavioural therapy: individual-participant data meta-analysis of pill-placebo-controlled trials

BACKGROUND: The influence of baseline severity has been examined for antidepressant medications but has not been studied properly for cognitive-behavioural therapy (CBT) in comparison with pill placebo. AIMS: To synthesise evidence regarding the influence of initial severity on efficacy of CBT from all randomised controlled trials (RCTs) in which CBT, in face-to-face individual or group format, was compared with pill-placebo control in adults with major depression. METHOD: A systematic review and an individual-participant data meta-analysis using mixed models that included trial effects as random effects. We used multiple imputation to handle missing data. RESULTS: We identified five RCTs, and we were given access to individual-level data (n = 509) for all five. The analyses revealed that the difference in changes in Hamilton Rating Scale for Depression between CBT and pill placebo was not influenced by baseline severity (interaction P = 0.43). Removing the non-significant interaction term from the model, the difference between CBT and pill placebo was a standardised mean difference of -0.22 (95% CI -0.42 to -0.02, P = 0.03, I2 = 0%). CONCLUSIONS: Patients suffering from major depression can expect as much benefit from CBT across the wide range of baseline severity. This finding can help inform individualised treatment decisions by patients and their clinicians.R01 MH060998 - NIMH NIH HHS; R34 MH086668 - NIMH NIH HHS; R01 AT007257 - NCCIH NIH HHS; R21 MH101567 - NIMH NIH HHS; K02 MH001697 - NIMH NIH HHS; R01 MH060713 - NIMH NIH HHS; R34 MH099311 - NIMH NIH HHS; R21 MH102646 - NIMH NIH HHS; K23 MH100259 - NIMH NIH HHS; R01 MH099021 - NIMH NIH HH

A mindfulness-based stress management program and treatment with omega-3 fatty acids to maintain a healthy mental state in hospital nurses (Happy Nurse Project): study protocol for a randomized controlled trial

Background: It is reported that nursing is one of the most vulnerable jobs for developing depression. While they may not be clinically diagnosed as depressed, nurses often suffer from depression and anxiety symptoms, which can lead to a low level of patient care. However, there is no rigorous evidence base for determining an effective prevention strategy for these symptoms in nurses. After reviewing previous literature, we chose a strategy of treatment with omega-3 fatty acids and a mindfulness-based stress management program for this purpose. We aim to explore the effectiveness of these intervention options for junior nurses working in hospital wards in Japan. Methods/Design: A factorial-design multi-center randomized trial is currently being conducted. A total of 120 nurses without a managerial position, who work for general hospitals and gave informed consent, have been randomly allocated to a stress management program or psychoeducation using a leaflet, and to omega-3 fatty acids or identical placebo pills. The stress management program has been developed according to mindfulness cognitive therapy and consists of four 30-minute individual sessions conducted using a detailed manual. These sessions are conducted by nurses with a managerial position. Participants allocated to the omega-3 fatty acid groups are provided with 1, 200 mg/day of eicosapentaenoic acid and 600 mg/day of docosahexaenoic acid for 90 days. Discussion: An effective preventive intervention may not only lead to the maintenance of a healthy mental state in nurses, but also to better quality of care for inpatients. This paper outlines the background and methods of a randomized trial that evaluates the possible additive value of omega-3 fatty acids and a mindfulness-based stress management program for reducing depression in nurses