scholarly journals Bayesian Specification Curve Analysis

2020 ◽  
Author(s):  
Christoph Semken ◽  
David Rossell
Keyword(s):  
Technology Use ◽  
Treatment Effects ◽  
Bayesian Model Averaging ◽  
Control Variable ◽  
Model Averaging ◽  
Well Being ◽  
Curve Analysis ◽  
Selective Reporting ◽  
Heterogeneous Effects ◽  
Missing Feature

Science suffers from a reproducibility crisis. Specification Curve Analysis (SCA) helps address this crisis by preventing the selective reporting of results and arbitrary data analysis choices. SCA plots the variability (or heterogeneity) of treatment effects against all ‘reasonable specifications’ (ways to conduct analysis). However, SCA has also been used for formal statistical inference on a type of global average (median) treatment effect (ATE), leading a study by Orben & Przybylski to conclude that ‘the association of [adolescent mental] well-being with regularly eating potatoes was nearly as negative as the association with technology use.’ In contrast, we find relevant associations between certain technologies and well-being, and sharp discrepancies between parent and teenager assessments. These heterogeneous effects are masked by taking medians. In layman’s terms, an ATE may appear practically irrelevant due to averaging over apples and oranges. In addition, the SCA median can have large bias and variance, due to over-weighting statistically implausible control variable specifications. With the Bayesian Specification Curve Analysis (BSCA) we extend SCA to estimate both individual and, if desired, average treatment effects, with controls weighted via Bayesian Model Averaging. The strategy allows to test individual effects, a missing feature in SCA, while improving statistical properties and protecting against false positives. We provide R code that implements BSCA and reproduces our analyses.

scholarly journals Monopsony in Labor Markets: A Meta-Analysis

ILR Review ◽  
2020 ◽  
Vol 74 (1) ◽  
pp. 27-55 ◽  
Author(s):  
Anna Sokolova ◽  
Todd Sorensen
Keyword(s):  
Labor Supply ◽  
Bayesian Model Averaging ◽  
Meta Analysis ◽  
Model Averaging ◽  
Labor Market Institutions ◽  
Selective Reporting ◽  
Wage Setting ◽  
The Mean ◽  
Monopsony Power ◽  
Identification Strategy

When jobs offered by different employers are not perfect substitutes, employers gain wage-setting power; the extent of this power can be captured by the elasticity of labor supply to the firm. The authors collect 1,320 estimates of this parameter from 53 studies. Findings show a prominent discrepancy between estimates of direct elasticity of labor supply to changes in wage (smaller) and the estimates converted from inverse elasticities (larger), suggesting that labor market institutions may rein in a substantial amount of firm wage-setting power. This gap remains after they control for 22 additional variables and use Bayesian Model Averaging and LASSO to address model uncertainty; however, it is less pronounced for studies employing an identification strategy. Furthermore, the authors find strong evidence that implies the literature on direct estimates is prone to selective reporting: Negative estimates of the elasticity of labor supply to the firm tend to be discarded, leading to upward bias in the mean reported estimate. Additionally, they point out several socioeconomic factors that seem to affect the degree of monopsony power.

Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

2012 ◽  
Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bridge Models

scholarly journals Older Adults and Technology Use: A Systematic Literature Review

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 823-823
Author(s):  
Hyung Wook Choi ◽  
Rose Ann DiMaria-Ghalili ◽  
Mat Kelly ◽  
Alexander Poole ◽  
Erjia Yan ◽  
...  
Keyword(s):  
Older Adults ◽  
Technology Use ◽  
Age Groups ◽  
Well Being ◽  
Sample Population ◽  
Age Cohorts ◽  
Fine Grained ◽  
Nuanced Understanding ◽  
Smart Technologies ◽  
Age Range

Abstract Researchers are increasingly interested in leveraging technology to support the physical and mental well-being of older adults. We systematically reviewed previous scholars’ criteria for sampling older adult populations, focusing on age cohorts (namely adults over 65) and their use of internet and smart technologies. We iteratively developed keyword combinations that represent older adults and technology from the retrieved literature. Between 2011 and 2020, 70 systematic reviews were identified, 26 of which met our inclusion criteria for full review. Most important, not one of the 26 papers used a sample population classification more fine-grained than “65 and older.” A knowledge gap thus exists; researchers lack a nuanced understanding of differences within this extraordinarily broad age-range. Demographics that we propose to analyze empirically include not only finer measures of age (e.g., 65-70 or 71-75, as opposed to “65 and older”), but also those age groups’ attitudes toward and capacity for technology use.

scholarly journals KBoost: a new method to infer gene regulatory networks from gene expression data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Luis F. Iglesias-Martinez ◽  
Barbara De Kegel ◽  
Walter Kolch
Keyword(s):  
Breast Cancer ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
R Package ◽  
Breast Cancer Patients ◽  
Sources Of Information ◽  
Cancer Subtypes ◽  
Gene Regulatory

AbstractReconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, many of the existing methods predict numerous false positives and have limited capabilities to integrate other sources of information, such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. We have benchmarked KBoost against other high performing algorithms using three different datasets. The results show that our method compares favorably to other methods across datasets. We have also applied KBoost to a large cohort of close to 2000 breast cancer patients and 24,000 genes in less than 2 h on standard hardware. Our results show that molecularly defined breast cancer subtypes also feature differences in their GRNs. An implementation of KBoost in the form of an R package is available at: https://github.com/Luisiglm/KBoost and as a Bioconductor software package.

scholarly journals Identification of Mortality Risks in the Advancement of Old Age: Application of Proportional Hazard Models Based on the Stepwise Variable Selection and the Bayesian Model Averaging Approach

Nutrients ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 1098
Author(s):  
Ewelina Łukaszyk ◽  
Katarzyna Bień-Barkowska ◽  
Barbara Bień
Keyword(s):  
Vitamin D ◽  
Mortality Rate ◽  
Plasma Level ◽  
Variable Selection ◽  
Lymphocyte Count ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Multivariable Analysis ◽  
Model Averaging ◽  
Nutritional Risk Index

Identifying factors that affect mortality requires a robust statistical approach. This study’s objective is to assess an optimal set of variables that are independently associated with the mortality risk of 433 older comorbid adults that have been discharged from the geriatric ward. We used both the stepwise backward variable selection and the iterative Bayesian model averaging (BMA) approaches to the Cox proportional hazards models. Potential predictors of the mortality rate were based on a broad range of clinical data; functional and laboratory tests, including geriatric nutritional risk index (GNRI); lymphocyte count; vitamin D, and the age-weighted Charlson comorbidity index. The results of the multivariable analysis identified seven explanatory variables that are independently associated with the length of survival. The mortality rate was higher in males than in females; it increased with the comorbidity level and C-reactive proteins plasma level but was negatively affected by a person’s mobility, GNRI and lymphocyte count, as well as the vitamin D plasma level.

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