Vibration of effects in epidemiologic studies of alcohol consumption and breast cancer risk

Sensitivity analysis with iterative outlier detection for systematic reviews and meta-analyses

Meta-analysis is a widely used tool for synthesizing results from multiple studies. The collected studies are deemed heterogeneous when they do not share a common underlying effect size; thus, the factors attributable to the heterogeneity need to be carefully considered. A critical problem in meta-analyses and systematic reviews is that outlying studies are frequently included, which can lead to invalid conclusions and affect the robustness of decision-making. Outliers may be caused by several factors such as study selection criteria, low study quality, small-study effects, and so on. Although outlier detection is well-studied in the statistical community, limited attention has been paid to meta-analysis. The conventional outlier detection method in meta-analysis is based on a leave-one-study-out procedure. However, when calculating a potentially outlying study's deviation, other outliers could substantially impact its result. This article proposes an iterative method to detect potential outliers, which reduces such an impact that could confound the detection. Furthermore, we adopt bagging to provide valid inference for sensitivity analyses of excluding outliers. Based on simulation studies, the proposed iterative method yields smaller bias and heterogeneity after performing a sensitivity analysis to remove the identified outliers. It also provides higher accuracy on outlier detection. Two case studies are used to illustrate the proposed method's real-world performance.

Grilling the data: application of specification curve analysis to red meat and all-cause mortality

OBJECTIVES

To present an application of specification curve analysis-a novel analytic method that involves defining and implementing all plausible and valid analytic approaches for addressing a research question-to nutritional epidemiology.

STUDY DESIGN AND SETTING

We reviewed all observational studies addressing the effect of red meat on all-cause mortality, sourced from a published systematic review, and documented variations in analytic methods (eg, choice of model, covariates, etc.). We enumerated all defensible combinations of analytic choices to produce a comprehensive list of all the ways in which the data may reasonably be analyzed. We applied specification curve analysis to data from National Health and Nutrition Examination Survey 2007 to 2014 to investigate the effect of unprocessed red meat on all-cause mortality. The specification curve analysis used a random sample of all reasonable analytic specifications we sourced from primary studies.

RESULTS

Among 15 publications reporting on 24 cohorts included in the systematic review on red meat and all-cause mortality, we identified 70 unique analytic methods, each including different analytic models, covariates, and operationalizations of red meat (eg, continuous vs quantiles). We applied specification curve analysis to National Health and Nutrition Examination Survey, including 10,661 participants. Our specification curve analysis included 1208 unique analytic specifications, of which 435 (36.0%) yielded a hazard ratio equal to or more than 1 for the effect of red meat on all-cause mortality and 773 (64.0%) less than 1. The specification curve analysis yielded a median hazard ratio of 0.94 (interquartile range: 0.83-1.05). Forty-eight specifications (3.97%) were statistically significant, 40 of which indicated unprocessed red meat to reduce all-cause mortality and eight of which indicated red meat to increase mortality.

CONCLUSION

We show that the application of specification curve analysis to nutritional epidemiology is feasible and presents an innovative solution to analytic flexibility.

Effect of alcohol consumption on breast cancer: probabilistic bias analysis for adjustment of exposure misclassification bias and confounders

PURPOSE

This study was conducted to evaluate the effect of alcohol consumption on breast cancer, adjusting for alcohol consumption misclassification bias and confounders.

METHODS

This was a case-control study of 932 women with breast cancer and 1000 healthy control. Using probabilistic bias analysis method, the association between alcohol consumption and breast cancer was adjusted for the misclassification bias of alcohol consumption as well as a minimally sufficient set of adjustment of confounders derived from a causal directed acyclic graph. Population attributable fraction was estimated using the Miettinen's Formula.

RESULTS

Based on the conventional logistic regression model, the odds ratio estimate between alcohol consumption and breast cancer was 1.05 (95% CI: 0.57, 1.91). However, the adjusted estimates of odds ratio based on the probabilistic bias analysis ranged from 1.82 to 2.29 for non-differential and from 1.93 to 5.67 for differential misclassification. Population attributable fraction ranged from 1.51 to 2.57% using non-differential bias analysis and 1.54-3.56% based on differential bias analysis.

CONCLUSION

A marked measurement error was in self-reported alcohol consumption so after correcting misclassification bias, no evidence against independence between alcohol consumption and breast cancer changed to a substantial positive association.

A comprehensive evaluation of the longitudinal association between alcohol consumption and a measure of inflammation: Multiverse and vibration of effects analyses

BACKGROUND

Moderate alcohol consumption appears to be associated with reduced inflammation. Determining whether this association is robust to common variations in research parameters has wide-reaching implications for our understanding of disease aetiology and public health policy. We aimed to conduct comprehensive multiverse and vibration of effects analyses evaluating the associations between alcohol consumption and a measure of inflammation.

METHODS

A secondary analysis of the 1970 British Birth Cohort Study was performed, using data from 1970 through 2016. Measurements of alcohol consumption were taken in early/mid-adulthood (ages 34 and 42), and level of inflammation marker high-sensitivity C-reactive protein (hsCRP) at age 46. Multiverse analyses were applied to comparisons of low-to-moderate consumption and consumption above various international drinking guidelines with an 'abstinent' reference. Research parameters of interest related to: definitions of drinking and reference groups; alcohol consumption measurement year; outcome variable transformation; and breadth of covariate adjustment. After identifying various analytic options within these parameters and running the analysis over each unique option combination, specification curve plots, volcano plots, effect ranges, and variance decomposition metrics were used to assess consistency of results.

RESULTS

A total of 3101 individuals were included in the final analyses, with primary analyses limited to those where occasional consumers served as reference. All combinations of research specifications resulted in lower levels of inflammation amongst low-to-moderate consumers compared to occasional consumers (1st percentile effect: -0.21; 99th percentile effect: -0.04). Estimates comparing above-guidelines drinking with occasional consumers were less definitive (1st percentile effect: -0.26; 99th percentile effect: 0.43).

CONCLUSIONS

The association between low-to-moderate drinking and lower hsCRP levels is largely robust to common variations in researcher-defined parameters, warranting further research to establish whether this relationship is causal. The association between above-guidelines drinking and hsCRP levels is less definitive.

Systematically assessing microbiome–disease associations identifies drivers of inconsistency in metagenomic research

Evaluating the relationship between the human gut microbiome and disease requires computing reliable statistical associations. Here, using millions of different association modeling strategies, we evaluated the consistency—or robustness—of microbiome-based disease indicators for 6 prevalent and well-studied phenotypes (across 15 public cohorts and 2,343 individuals). We were able to discriminate between analytically robust versus nonrobust results. In many cases, different models yielded contradictory associations for the same taxon–disease pairing, some showing positive correlations and others negative. When querying a subset of 581 microbe–disease associations that have been previously reported in the literature, 1 out of 3 taxa demonstrated substantial inconsistency in association sign. Notably, >90% of published findings for type 1 diabetes (T1D) and type 2 diabetes (T2D) were particularly nonrobust in this regard. We additionally quantified how potential confounders—sequencing depth, glucose levels, cholesterol, and body mass index, for example—influenced associations, analyzing how these variables affect the ostensible correlation between Faecalibacterium prausnitzii abundance and a healthy gut. Overall, we propose our approach as a method to maximize confidence when prioritizing findings that emerge from microbiome association studies.

The human microbiome has been associated with many aspects of our health, but how many of these associations are truly reproducible? This study attempts to address this question by systematically testing the robustness of 581 microbial features that have been reported as being disease-associated.

Are there non-linear relationships between alcohol consumption and long-term health?: a systematic review of observational studies employing approaches to improve causal inference

Background

Research has long found ‘J-shaped’ relationships between alcohol consumption and certain health outcomes, indicating a protective effect of moderate consumption. However, methodological limitations in most studies hinder causal inference. This review aimed to identify all observational studies employing improved approaches to mitigate confounding in characterizing alcohol–long-term health relationships, and to qualitatively synthesize their findings.

Methods

Eligible studies met the above description, were longitudinal (with pre-defined exceptions), discretized alcohol consumption, and were conducted with human populations. MEDLINE, PsycINFO, Embase and SCOPUS were searched in May 2020, yielding 16 published manuscripts reporting on cancer, diabetes, dementia, mental health, cardiovascular health, mortality, HIV seroconversion, and musculoskeletal health. Risk of bias of cohort studies was evaluated using the Newcastle-Ottawa Scale, and a recently developed tool was used for Mendelian Randomization studies.

Results

A variety of functional forms were found, including reverse J/J-shaped relationships for prostate cancer and related mortality, dementia risk, mental health, and certain lipids. However, most outcomes were only evaluated by a single study, and few studies provided information on the role of alcohol consumption pattern.

Conclusions

More research employing enhanced causal inference methods is urgently required to accurately characterize alcohol–long-term health relationships. Those studies that have been conducted find a variety of linear and non-linear functional forms, with results tending to be discrepant even within specific health outcomes.

Trial registration

PROSPERO registration number CRD42020185861.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01486-5.

[Benefit assessment of digital health applications-challenges and opportunities]

Digital health applications promise to improve patient health and medical care. This analysis provides a brief overview of evidence-based benefit assessment and the challenges to the underlying evidence as prerequisites for optimal patient-oriented decision making. Classical concepts in study design, recent developments, and innovative approaches are described with the aim of highlighting future areas of development in innovative study designs and strategic evaluation concepts for digital health applications. A special focus is on pragmatic study designs.Evidence-based benefit assessment has fundamental requirements and criteria regardless of the type of treatments evaluated. Reliable evidence is essential. Fast, efficient, reliable, and practice-relevant evaluation of digital health applications is not achieved by turning to nonrandomized trials, but rather by better pragmatic randomized trials. They are feasible and combine the characteristics of digital health applications, classical methodological concepts, and new approaches to study conduct. Routinely collected data, low-contact study conduct (remote trials, virtual trials), and digital biomarkers promote useful randomized real-world evidence as solid evidence base for digital health applications. Continuous learning evaluation with randomized designs embedded in routine care is key to sustainable and efficient benefit assessment of digital health applications and may be crucial for strategic improvement of healthcare.

Alcohol induced impairment/abnormalities in brain: Role of MicroRNAs

Alcohol is a highly toxic substance and has teratogenic properties that can lead to a wide range of developmental disorders. Excessive use of alcohol can change the structural and functional aspects of developed brain and other organs. Which can further lead to significant health, social and economic implications in many countries of the world. Convincing evidence support the involvement of microRNAs (miRNAs) as important post-transcriptional regulators of gene expression in neurodevelopment and maintenance. They also show differential expression following an injury. MiRNAs are the special class of small non coding RNAs that can modify the gene by targeting the mRNA and fine tune the development of cells to organs. Numerous pieces of evidences have shown the relationship between miRNA, alcohol and brain damage. These studies also show how miRNA controls different cellular mechanisms involved in the development of alcohol use disorder. With the increasing number of research studies, the roles of miRNAs following alcohol-induced injury could help researchers to recognize alternative therapeutic methods to treat/cure alcohol-induced brain damage. The present review summarizes the available data and brings together the important miRNAs, that play a crucial role in alcohol-induced brain damage, which will help in better understanding complex mechanisms. Identifying these miRNAs will not only expand the current knowledge but can lead to the identification of better targets for the development of novel therapeutic interventions.

Mini review: The FDA-approved prescription drugs that target the MAPK signaling pathway in women with breast cancer

Breast cancer (BC) is the most common cancer and the prevalent type of malignancy among women. Multiple risk factors, including genetic changes, biological age, dense breast tissue, and obesity are associated with BC. The mitogen-activated protein kinases (MAPK) signaling pathway has a pivotal role in regulating biological functions such as cell proliferation, differentiation, apoptosis, and survival. It has become evident that the MAPK pathway is associated with tumorigenesis and may promote breast cancer development. The MAPK/RAS/RAF cascade is closely associated with breast cancer. RAS signaling can enhance BC cell growth and progression. B-Raf is an important kinase and a potent RAF isoform involved in breast tumor initiation and differentiation. Depending on the reasons for cancer, there are different strategies for treatment of women with BC. Till now, several FDA-approved treatments have been investigated that inhibit the MAPK pathway and reduce metastatic progression in breast cancer. The most common breast cancer drugs that regulate or inhibit the MAPK pathway may include Farnesyltransferase inhibitors (FTIs), Sorafenib, Vemurafenib, PLX8394, Dabrafenib, Ulixertinib, Simvastatin, Alisertib, and Teriflunomide. In this review, we will discuss the roles of the MAPK/RAS/RAF/MEK/ERK pathway in BC and summarize the FDA-approved prescription drugs that target the MAPK signaling pathway in women with BC.

Examining the robustness of observational associations to model, measurement and sampling uncertainty with the vibration of effects framework

BACKGROUND

The results of studies on observational associations may vary depending on the study design and analysis choices as well as due to measurement error. It is important to understand the relative contribution of different factors towards generating variable results, including low sample sizes, researchers' flexibility in model choices, and measurement error in variables of interest and adjustment variables.

METHODS

We define sampling, model and measurement uncertainty, and extend the concept of vibration of effects in order to study these three types of uncertainty in a common framework. In a practical application, we examine these types of uncertainty in a Cox model using data from the National Health and Nutrition Examination Survey. In addition, we analyse the behaviour of sampling, model and measurement uncertainty for varying sample sizes in a simulation study.

RESULTS

All types of uncertainty are associated with a potentially large variability in effect estimates. Measurement error in the variable of interest attenuates the true effect in most cases, but can occasionally lead to overestimation. When we consider measurement error in both the variable of interest and adjustment variables, the vibration of effects are even less predictable as both systematic under- and over-estimation of the true effect can be observed. The results on simulated data show that measurement and model vibration remain non-negligible even for large sample sizes.

CONCLUSION

Sampling, model and measurement uncertainty can have important consequences for the stability of observational associations. We recommend systematically studying and reporting these types of uncertainty, and comparing them in a common framework.

Are there non-linear relationships between alcohol consumption and long-term health? Protocol for a systematic review of observational studies employing approaches to improve causal inference

INTRODUCTION

There is a substantial literature finding that moderate alcohol consumption is protective against certain health conditions. However, more recent research has highlighted the possibility that these findings are methodological artefacts, caused by confounding and other biases. While modern analytical and study design approaches can mitigate confounding and thus enhance causal inference in observational studies, they are not routinely applied in research assessing the relationship between alcohol use and long-term health outcomes. The purpose of this systematic review is to identify observational studies that employ these analytical/design-based approaches in assessing whether relationships between alcohol consumption and health outcomes are non-linear. This review seeks to evaluate, on a per-outcome basis, what these studies find the strength and form of the relationship between alcohol consumption and health to be.

METHODS AND ANALYSIS

Electronic databases (MEDLINE, PsycINFO, Embase and SCOPUS) were searched in May 2020. Study selection will comply with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Articles will be screened against eligibility criteria intended to capture studies using observational data to assess the relationship between varying levels of alcohol exposure and any long-term health outcome (actual or surrogate), and that have employed at least one of the prespecified approaches to enhancing causal inference. Risk of bias of included articles will be assessed using study design-specific tools. A narrative synthesis of the results is planned.

ETHICS AND DISSEMINATION

Formal ethics approval is not required given there will be no primary data collection. The results of the study will be disseminated through published manuscripts, conferences and seminar presentations.

PROSPERO REGISTRATION NUMBER

CRD42020185861.

Consideration of confounding in epidemiologic studies assessing alcohol consumption on the risk of breast cancer: A brief report

BACKGROUND

Individual observational studies have suggested null, weak, linear, and J-shaped associations between alcohol consumption and breast cancer risk. However, observational studies are susceptible to confounders, which can obscure the true impact of an exposure on an outcome. Given the uncertainty regarding the association between alcohol consumption and breast cancer, and the challenges of identifying, measuring, and accounting for all potential confounders, we assessed whether and how authors of observational studies evaluating the impact of alcohol consumption on the risk of breast cancer considered bias when interpreting their main study findings.

METHODS

We identified all observational studies included in a recent alcohol-breast cancer meta-analysis. The Abstract and/or Discussion sections were reviewed to determine whether authors considered confounding.

RESULTS

Among 101 eligible studies, 73 (72.3%) mentioned confounding explicitly in the Abstract and Discussion sections. There were 33 (32.7%) studies that included statements regarding specific confounders that were not adjusted for in the analyses and 60 (59.4%) studies without any statements about the impact of residual confounding on their main findings. Although none of the studies outlined that their main findings were "likely" to be affected by residual confounding, 25 (24.8%) mentioned a "possible" impact and 16 (15.8%) claimed an "unlikely" impact. Only one (1.0%) article explicitly stated that caution was needed when interpreting their findings due to confounding.

CONCLUSION

These results highlight the need for more adequate consideration of the potential impact of residual confounding in observational studies evaluating the impact of alcohol consumption on the risk of breast cancer.