Approximate Bayesian Model Selection with the Deviance Statistic

Bayesian model selection poses two main challenges: the specification of parameter priors for all models, and the computation of the resulting Bayes factors between models. There is now a large literature on automatic and objective parameter priors in the linear model. One important class are $g$-priors, which were recently extended from linear to generalized linear models (GLMs). We show that the resulting Bayes factors can be approximated by test-based Bayes factors (Johnson [Scand. J. Stat. 35 (2008) 354-368]) using the deviance statistics of the models. To estimate the hyperparameter $g$, we propose empirical and fully Bayes approaches and link the former to minimum Bayes factors and shrinkage estimates from the literature. Furthermore, we describe how to approximate the corresponding posterior distribution of the regression coefficients based on the standard GLM output. We illustrate the approach with the development of a clinical prediction model for 30-day survival in the GUSTO-I trial using logistic regression.Comment: Published at http://dx.doi.org/10.1214/14-STS510 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Optimizing the design and analysis of clinical trials for antibacterials against multidrug-resistant organisms:a white paper from COMBACTE's STAT-Net

Innovations are urgently required for clinical development of antibacterials against multidrug-resistant organisms. Therefore, a European, public-private working group (STAT-Net; part of Combatting Bacterial Resistance in Europe [COMBACTE]), has reviewed and tested several innovative trials designs and analytical methods for randomized clinical trials, which has resulted in 8 recommendations. The first 3 focus on pharmacokinetic and pharmacodynamic modeling, emphasizing the pertinence of population-based pharmacokinetic models, regulatory procedures for the reassessment of old antibiotics, and rigorous quality improvement. Recommendations 4 and 5 address the need for more sensitive primary end points through the use of rank-based or time-dependent composite end points. Recommendation 6 relates to the applicability of hierarchical nested-trial designs, and the last 2 recommendations propose the incorporation of historical or concomitant trial data through Bayesian methods and/or platform trials. Although not all of these recommendations are directly applicable, they provide a solid, evidence-based approach to develop new, and established, antibacterials and address this public health challenge

Serious adverse events in patients with target-oriented blood pressure management: a systematic review

: On the basis of the benefits of antihypertensive treatment, progressively intensive treatment is advocated. However, it remains controversial whether intensive blood pressure control might increase the frequency of serious adverse events (SAEs) compared with moderate control. This review assessed the occurrence of SAEs in blood pressure treatment with predefined blood pressure targets. Seven original studies and eight post hoc analyses (derived from two original studies) met the inclusion criteria. Compared with moderate blood pressure treatment, intensive treatment was associated with a significant increase in treatment-related SAEs (Sign-test: P = 0.0002, Wilcoxon signed-rank test: P = 0.001). However, comparability between studies was limited, due to unclear determinations about the treatment-relatedness of adverse events, missing definitions of SAEs and variations in recording methods. Thus, a meta-analysis was not justified. The definitions of serious adverse events and methods of recording and reporting need to be improved and standardized to facilitate the comparison of results

Objective Bayesian model selection for Cox regression

There is now a large literature on objective Bayesian model selection in the linear model based on the g-prior. The methodology has been recently extended to generalized linear models using test-based Bayes factors. In this paper, we show that test-based Bayes factors can also be applied to the Cox proportional hazards model. If the goal is to select a single model, then both the maximum a posteriori and the median probability model can be calculated. For clinical prediction of survival, we shrink the model-specific log hazard ratio estimates with subsequent calculation of the Breslow estimate of the cumulative baseline hazard function. A Bayesian model average can also be employed. We illustrate the proposed methodology with the analysis of survival data on primary biliary cirrhosis patients and the development of a clinical prediction model for future cardiovascular events based on data from the Second Manifestations of ARTerial disease (SMART) cohort study. Cross-validation is applied to compare the predictive performance with alternative model selection approaches based on Harrell's c-Index, the calibration slope and the integrated Brier score. Finally, a novel application of Bayesian variable selection to optimal conditional prediction via landmarking is described. Copyright © 2016 John Wiley & Sons, Ltd

Effects of Vitamin B12 Supplementation on Cognitive Function, Depressive Symptoms, and Fatigue: A Systematic Review, Meta-Analysis, and Meta-Regression

Vitamin B12 is often used to improve cognitive function, depressive symptoms, and fatigue. In most cases, such complaints are not associated with overt vitamin B12 deficiency or advanced neurological disorders and the effectiveness of vitamin B12 supplementation in such cases is uncertain. The aim of this systematic review and meta-analysis of randomized controlled trials (RCTs) is to assess the effects of vitamin B12 alone (B12 alone), in addition to vitamin B12 and folic acid with or without vitamin B6 (B complex) on cognitive function, depressive symptoms, and idiopathic fatigue in patients without advanced neurological disorders or overt vitamin B12 deficiency. Medline, Embase, PsycInfo, Cochrane Library, and Scopus were searched. A total of 16 RCTs with 6276 participants were included. Regarding cognitive function outcomes, we found no evidence for an effect of B12 alone or B complex supplementation on any subdomain of cognitive function outcomes. Further, meta-regression showed no significant associations of treatment effects with any of the potential predictors. We also found no overall effect of vitamin supplementation on measures of depression. Further, only one study reported effects on idiopathic fatigue, and therefore, no analysis was possible. Vitamin B12 supplementation is likely ineffective for improving cognitive function and depressive symptoms in patients without advanced neurological disorders

Data from: Objective Bayesian model selection for Cox regression

There is now a large literature on objective Bayesian model selection in the linear model based on the $g$-prior. The methodology has been recently extended to generalized linear models using test-based Bayes factors (TBFs). In this paper we show that TBFs can also be applied to the Cox proportional hazards model. If the goal is to select a single model, then both the maximum {\em a posteriori} and the median probability model can be calculated. For clinical prediction of survival, we shrink the model-specific log hazard ratio estimates with subsequent calculation of the Breslow estimate of the cumulative baseline hazard function. A Bayesian model average can also be employed. We illustrate the proposed methodology with the analysis of survival data on primary biliary cirrhosis (PBC) patients and the development of a clinical prediction model for future cardiovascular events based on data from the SMART cohort study. Cross-validation is applied to compare the predictive performance with alternative model selection approaches based on Harrell's c-Index, the calibration slope and the integrated Brier score. Finally a novel application of Bayesian variable selection to optimal conditional prediction via landmarking is described. <br

Dataset for: Objective Bayesian model selection for Cox regression

There is now a large literature on objective Bayesian model selection in the linear model based on the $g$-prior. The methodology has been recently extended to generalized linear models using test-based Bayes factors (TBFs). In this paper we show that TBFs can also be applied to the Cox proportional hazards model. If the goal is to select a single model, then both the maximum {\em a posteriori} and the median probability model can be calculated. For clinical prediction of survival, we shrink the model-specific log hazard ratio estimates with subsequent calculation of the Breslow estimate of the cumulative baseline hazard function. A Bayesian model average can also be employed. We illustrate the proposed methodology with the analysis of survival data on primary biliary cirrhosis (PBC) patients and the development of a clinical prediction model for future cardiovascular events based on data from the SMART cohort study. Cross-validation is applied to compare the predictive performance with alternative model selection approaches based on Harrell's c-Index, the calibration slope and the integrated Brier score. Finally a novel application of Bayesian variable selection to optimal conditional prediction via landmarking is described

Benefits of Mobile Apps in Pain Management: Systematic Review

BACKGROUND Pain is a common condition with a significant physical, psychosocial, and economic impact. Due to enormous progress in mobile device technology as well as the increase in smartphone ownership in the general population, mobile apps can be used to monitor patients with pain and support them in pain management. OBJECTIVE The aim of this review was to assess the efficacy of smartphone or computer tablet apps in the management of patients with pain. METHODS In December 2017, a literature search was performed in the following databases: MEDLINE, EMBASE, CINAHL, Cochrane, and PsycINFO. In addition, a bibliography search was conducted. We included studies with at least 20 participants per arm that evaluated the effects of apps on smartphones or computer tablets on improvement in pain. RESULTS A total of 15 studies with 1962 patients met the inclusion criteria. Of these, 4 studies examined the effect of mobile apps on pain management in an in-clinic setting and 11 in an out-clinic setting. The majority of the original studies reported beneficial effects of the use of a pain app. Severity of pain decreased in most studies where patients were using an app compared with patients not using an app. Other outcomes, such as worst pain or quality of life showed improvements in patients using an app. Due to heterogeneity between the original studies-patient characteristics, app content, and study setting-a synthesis of the results by statistical methods was not performed. CONCLUSIONS Apps for pain management may be beneficial for patients, particularly in an out-clinic setting. Studies have shown that pain apps are workable and well liked by patients and health care professionals. There is no doubt that in the near future, mobile technologies will develop further. Medicine could profit from this development as indicated by our results, but there is a need for more scientific inputs. It is desirable to know which elements of apps or additional devices and tools may improve usability and help patients in pain management