Advances in meta-analysis: examples from internal medicine to neurology

Frequency of severe reactions following penicillin drug provocation tests: A Bayesian meta-analysis

BACKGROUND

Patients with a penicillin allergy label tend to have worse clinical outcomes and increased healthcare use. Drug provocation tests (DPT) are the gold-standard in the diagnostic workup of penicillin allergy, but safety concerns may hinder their performance. We aimed to assess the frequency of severe reactions following a DPT in patients with reported allergy to penicillins or other β-lactams.

METHODS

We performed a systematic review, searching MEDLINE, Scopus, and Web of Science. We included primary studies assessing participants with a penicillin allergy label who underwent a DPT. We performed a Bayesian meta-analysis to estimate the pooled frequency of severe reactions to penicillin DPTs. Sources of heterogeneity were explored by subgroup and metaregression analyses.

RESULTS

We included 112 primary studies which included a total of 26,595 participants. The pooled frequency of severe reactions was estimated at 0.06% (95% credible interval [95% CrI] = 0.01%-0.13%; I2  = 57.9%). Most severe reactions (80/93; 86.0%) consisted of anaphylaxis. Compared to studies where the index reaction was immediate, we observed a lower frequency of severe reactions for studies assessing non-immediate index reactions (OR = 0.05; 95% CrI = 0-0.31). Patients reporting anaphylaxis as their index reaction were found to be at increased risk of developing severe reactions (OR = 13.5; 95% CrI = 7.7-21.5; I2  = 0.3%). Performance of direct DPTs in low-risk patients or testing with the suspected culprit drug were not associated with clinically relevant increased risk of severe reactions.

CONCLUSIONS

In patients with a penicillin allergy label, severe reactions resulting from DPTs are rare. Therefore, except for patients with potentially life-threatening index reactions or patients with positive skin tests-who were mostly not assessed in this analysis -, the safety of DPTs supports their performance in the diagnostic assessment of penicillin allergy.

Bronchoscopic lung volume reduction procedures for emphysema: A network meta-analysis

Bronchoscopic lung volume reduction (BLVR) offers alternative novel treatments for patients with emphysema. Comprehensive evidence for comparing different BLVR remains unclear. To estimate the effects of different BLVR on patients with emphysema. PubMed, EMBASE, Cochrane Library, and Web of Science databases from January 2001 to August 2017 were searched. Randomized clinical trials evaluated effects of BLVR on patients with emphysema. The relevant information was extracted from the published reports with a predefined data extraction sheet, and the risk of bias was assessed with the Cochrane risk of bias tools. Pair-wise metaanalyses were made using the random-effects model. A random-effects network meta-analysis was applied within a Bayesian framework. The quality of evidence contributing to primary outcomes was assessed using the GRADE framework. 13 trials were deemed eligible, including 1993 participants. The quality of evidence was rated as moderate in most comparisons. Medical care (MC)was associated with the lowest adverse events compared with intrabronchial valve (IBV)(-2.5,[-4.70 to -0.29]), endobronchial valve (EBV) (-1.73, [-2.37 to -1.09]), lung volume reduction coils (LVRC) (-0.76, [-1.24 to -0.28]), emphysematous lung sealant (ELS) (-1.53, [-2.66 to -0.39]), and airway bypass(-1.57, [-3.74 to 0.61]). Adverse events in LVRC were lower compared with ELS (-0.77,[-2.00 to 0.47]). Bronchoscopic thermal vapor ablation (BTVA) showed significant improvement in FEV1 compared with MC (0.99, [0.37 to 1.62]), IBV (1.25, [0.25 to 2.25]), and LVRC (0.72, [0.03 to 1.40] ). Six  minute walking distance (6 MWD) in ELS was significantly improved compared with other four BLVR, sham control, and MC (-1.96 to 1.99). Interestingly, MC showed less improvement in FEV1 and 6MWDcompared with EBV (-0.45, [-0.69 to -0.20] and -0.39, [-0.71 to -0.07], respectively). The mortality in MC and EBV was lower compared with LVRC alone (-0.38, [-1.16 to 0.41] and -0.50, [-1.68 to 0.68], respectively). BTVA and EBV led to significant changes in St George's respiratory questionnaire (SGRQ) compared with MC alone (-0.74, [-1.43 to -0.05] and 0.44, [0.11 to 0.78], respectively). BLVR offered a clear advantage for patients with emphysema. EBV had noticeable beneficial effects on the improvement of forced expiratory volume 1, 6MWD and SGRQ, and was associated with lower mortality compared with MC in different strategies of BLVR.

Evaluation of commonly used analysis strategies for epigenome- and transcriptome-wide association studies through replication of large-scale population studies

BACKGROUND

A large number of analysis strategies are available for DNA methylation (DNAm) array and RNA-seq datasets, but it is unclear which strategies are best to use. We compare commonly used strategies and report how they influence results in large cohort studies.

RESULTS

We tested the associations of DNAm and RNA expression with age, BMI, and smoking in four different cohorts (n = ~ 2900). By comparing strategies against the base model on the number and percentage of replicated CpGs for DNAm analyses or genes for RNA-seq analyses in a leave-one-out cohort replication approach, we find the choice of the normalization method and statistical test does not strongly influence the results for DNAm array data. However, adjusting for cell counts or hidden confounders substantially decreases the number of replicated CpGs for age and increases the number of replicated CpGs for BMI and smoking. For RNA-seq data, the choice of the normalization method, gene expression inclusion threshold, and statistical test does not strongly influence the results. Including five principal components or excluding correction of technical covariates or cell counts decreases the number of replicated genes.

CONCLUSIONS

Results were not influenced by the normalization method or statistical test. However, the correction method for cell counts, technical covariates, principal components, and/or hidden confounders does influence the results.

Statistical primer: methodology and reporting of meta-analyses

In modern medicine, the results of a comprehensive and methodologically sound meta-analysis bring the most robust, high-quality information to support evidence-based decision-making. With recent developments in newer meta-analytic approaches, iteration of statistical paradigms and software implementations, network and patient-level meta-analyses have recently gained popularity alongside conventional pairwise study-level meta-analyses. However, pitfalls are common in this challenging and rapidly evolving field of statistics. In this regard, guidelines have been introduced to standardize, strengthen and homogenize different aspects of conducting and reporting the results of a meta-analysis. Current recommendations advise a careful selection of the individual studies to be pooled, mainly based on the methodological quality and homogeneity in study designs. Indeed, even if a reasonable degree of variability across study results (namely, heterogeneity) can be accounted for with proper statistics (i.e. random-effect models), no adjustment can be performed in meta-analyses violating the issue of clinical validity and similarity across the included studies. In this context, this statistical primer aims at providing a conceptual framework, complemented by a practical example, for conducting, interpreting and critically evaluating meta-analyses.

Bayesian methodology for the design and interpretation of clinical trials in critical care medicine: a primer for clinicians

OBJECTIVES

To review Bayesian methodology and its utility to clinical decision making and research in the critical care field.

DATA SOURCE AND STUDY SELECTION

Clinical, epidemiological, and biostatistical studies on Bayesian methods in PubMed and Embase from their inception to December 2013.

DATA SYNTHESIS

Bayesian methods have been extensively used by a wide range of scientific fields, including astronomy, engineering, chemistry, genetics, physics, geology, paleontology, climatology, cryptography, linguistics, ecology, and computational sciences. The application of medical knowledge in clinical research is analogous to the application of medical knowledge in clinical practice. Bedside physicians have to make most diagnostic and treatment decisions on critically ill patients every day without clear-cut evidence-based medicine (more subjective than objective evidence). Similarly, clinical researchers have to make most decisions about trial design with limited available data. Bayesian methodology allows both subjective and objective aspects of knowledge to be formally measured and transparently incorporated into the design, execution, and interpretation of clinical trials. In addition, various degrees of knowledge and several hypotheses can be tested at the same time in a single clinical trial without the risk of multiplicity. Notably, the Bayesian technology is naturally suited for the interpretation of clinical trial findings for the individualized care of critically ill patients and for the optimization of public health policies.

CONCLUSIONS

We propose that the application of the versatile Bayesian methodology in conjunction with the conventional statistical methods is not only ripe for actual use in critical care clinical research but it is also a necessary step to maximize the performance of clinical trials and its translation to the practice of critical care medicine.