scholarly journals The effects of neighbourhood and workplace income comparisons on subjective wellbeing

2021 ◽  
Author(s):  
◽  
Shakked Noy
Keyword(s):  
Income Distribution ◽  
Subjective Wellbeing ◽  
Causal Effect ◽  
Peer Groups ◽  
Sensitivity Analyses ◽  
Group Level ◽  
Mass Layoffs ◽  
Fairness Concerns ◽  
Linear Regressions ◽  
Over Time

<p>We investigate how the incomes of a person’s neighbours and coworkers affect her happiness, using survey data on subjective wellbeing linked to unprecedentedly rich administrative data on the characteristics of survey respondents’ peer groups. Linear regressions of subjective wellbeing on peer income variables establish that people care exclusively about their ordinal rank within their peer income distribution, that workplace rank matters much more than neighbourhood rank, and that workplace comparisons are driven primarily by fairness concerns. We confirm that our results reflect a causal effect of peer income by implementing sensitivity analyses, identifying off changes in peer income over time for immobile people, exploiting plausibly exogenous moves between workplaces triggered by mass layoffs, and testing for the effects of unobservable group-level confounders.</p>

scholarly journals The effects of neighbourhood and workplace income comparisons on subjective wellbeing

2021 ◽  
Author(s):  
◽  
Shakked Noy
Keyword(s):  
Income Distribution ◽  
Subjective Wellbeing ◽  
Causal Effect ◽  
Peer Groups ◽  
Sensitivity Analyses ◽  
Group Level ◽  
Mass Layoffs ◽  
Fairness Concerns ◽  
Linear Regressions ◽  
Over Time

<p>We investigate how the incomes of a person’s neighbours and coworkers affect her happiness, using survey data on subjective wellbeing linked to unprecedentedly rich administrative data on the characteristics of survey respondents’ peer groups. Linear regressions of subjective wellbeing on peer income variables establish that people care exclusively about their ordinal rank within their peer income distribution, that workplace rank matters much more than neighbourhood rank, and that workplace comparisons are driven primarily by fairness concerns. We confirm that our results reflect a causal effect of peer income by implementing sensitivity analyses, identifying off changes in peer income over time for immobile people, exploiting plausibly exogenous moves between workplaces triggered by mass layoffs, and testing for the effects of unobservable group-level confounders.</p>

scholarly journals Bias in two-sample Mendelian randomization when using heritable covariable-adjusted summary associations

2021 ◽  
Author(s):  
Fernando Pires Hartwig ◽  
Kate Tilling ◽  
George Davey Smith ◽  
Deborah A Lawlor ◽  
Maria Carolina Borges
Keyword(s):  
Waist Circumference ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Real Data ◽  
Sensitivity Analyses ◽  
Effect Estimate ◽  
Genome Wide Association Studies ◽  
Residual Confounding

Abstract Background Two-sample Mendelian randomization (MR) allows the use of freely accessible summary association results from genome-wide association studies (GWAS) to estimate causal effects of modifiable exposures on outcomes. Some GWAS adjust for heritable covariables in an attempt to estimate direct effects of genetic variants on the trait of interest. One, both or neither of the exposure GWAS and outcome GWAS may have been adjusted for covariables. Methods We performed a simulation study comprising different scenarios that could motivate covariable adjustment in a GWAS and analysed real data to assess the influence of using covariable-adjusted summary association results in two-sample MR. Results In the absence of residual confounding between exposure and covariable, between exposure and outcome, and between covariable and outcome, using covariable-adjusted summary associations for two-sample MR eliminated bias due to horizontal pleiotropy. However, covariable adjustment led to bias in the presence of residual confounding (especially between the covariable and the outcome), even in the absence of horizontal pleiotropy (when the genetic variants would be valid instruments without covariable adjustment). In an analysis using real data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank, the causal effect estimate of waist circumference on blood pressure changed direction upon adjustment of waist circumference for body mass index. Conclusions Our findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. When that is not possible, careful consideration of the causal relationships underlying the data (including potentially unmeasured confounders) is required to direct sensitivity analyses and interpret results with appropriate caution.

scholarly journals Using genetic variants to evaluate the causal effect of serum vitamin D concentration on COVID-19 susceptibility, severity and hospitalization traits: a Mendelian randomization study

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Zhiyong Cui ◽  
Yun Tian
Keyword(s):  
Vitamin D ◽  
Fixed Effects ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Serum Vitamin ◽  
Vitamin D Supplementation ◽  
Sensitivity Analyses ◽  
Global Test ◽  
Serum Vitamin D

Abstract Background The coronavirus disease 2019 (COVID-19) pandemic has struck globally and is exerting a devastating toll on humans. The pandemic has led to calls for widespread vitamin D supplementation in public. However, evidence supporting the role of vitamin D in the COVID-19 pandemic remains controversial. Methods We performed a two-sample Mendelian randomization (MR) analysis to analyze the causal effect of the 25-hydroxyvitamin D [25(OH)D] concentration on COVID-19 susceptibility, severity and hospitalization traits by using summary-level GWAS data. The causal associations were estimated with inverse variance weighted (IVW) with fixed effects (IVW-fixed) and random effects (IVW-random), MR-Egger, weighted edian and MR Robust Adjusted Profile Score (MR.RAPS) methods. We further applied the MR Steiger filtering method, MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) global test and PhenoScanner tool to check and remove single nucleotide polymorphisms (SNPs) that were horizontally pleiotropic. Results We found no evidence to support the causal associations between the serum 25(OH)D concentration and the risk of COVID-19 susceptibility [IVW-fixed: odds ratio (OR) = 0.9049, 95% confidence interval (CI) 0.8197–0.9988, p = 0.0473], severity (IVW-fixed: OR = 1.0298, 95% CI 0.7699–1.3775, p = 0.8432) and hospitalized traits (IVW-fixed: OR = 1.0713, 95% CI 0.8819–1.3013, p = 0.4878) using outlier removed sets at a Bonferroni-corrected p threshold of 0.0167. Sensitivity analyses did not reveal any sign of horizontal pleiotropy. Conclusions Our MR analysis provided precise evidence that genetically lowered serum 25(OH)D concentrations were not causally associated with COVID-19 susceptibility, severity or hospitalized traits. Our study did not provide evidence assessing the role of vitamin D supplementation during the COVID-19 pandemic. High-quality randomized controlled trials are necessary to explore and define the role of vitamin D supplementation in the prevention and treatment of COVID-19.

Toward a Post-Sraffian Theory of Income Distribution

1994 ◽  
Author(s):  
A. Roncaglia
Keyword(s):  
Income Distribution ◽  
Theoretical Framework ◽  
Distribution Theory ◽  
Social Classes ◽  
Modern World ◽  
Sraffian Theory ◽  
Over Time

After recalling the Sraffian critiques to marginalist distribution theory, and hence the need for a different approach, the paper illustrates the classical conceptualization of social classes and its flexibility for the application to the modern world. The relationships among market forms-above all oligopoly, mark-up pricing, and income distribution-are then discussed, in search of a theoretical framework for the analysis of the evolution of distributive variables over time: an approach suggested as superior to the traditional one which aims at determining equilibrium values for the distributive variables at a moment in time.

scholarly journals Noncompliance and Missing Data in Health Economic Evaluation

2019 ◽  
Author(s):  
Karla DiazOrdaz ◽  
Richard Grieve
Keyword(s):  
Health Economic ◽  
Economic Evaluation ◽  
Missing Data ◽  
Causal Effect ◽  
Health Economic Evaluation ◽  
Specific Treatment ◽  
Model Specification ◽  
Sensitivity Analyses ◽  
Economic Evaluations ◽  
Random Assignment

Health economic evaluations face the issues of noncompliance and missing data. Here, noncompliance is defined as non-adherence to a specific treatment, and occurs within randomized controlled trials (RCTs) when participants depart from their random assignment. Missing data arises if, for example, there is loss-to-follow-up, survey non-response, or the information available from routine data sources is incomplete. Appropriate statistical methods for handling noncompliance and missing data have been developed, but they have rarely been applied in health economics studies. Here, we illustrate the issues and outline some of the appropriate methods with which to handle these with application to health economic evaluation that uses data from an RCT. In an RCT the random assignment can be used as an instrument-for-treatment receipt, to obtain consistent estimates of the complier average causal effect, provided the underlying assumptions are met. Instrumental variable methods can accommodate essential features of the health economic context such as the correlation between individuals’ costs and outcomes in cost-effectiveness studies. Methodological guidance for handling missing data encourages approaches such as multiple imputation or inverse probability weighting, which assume the data are Missing At Random, but also sensitivity analyses that recognize the data may be missing according to the true, unobserved values, that is, Missing Not at Random. Future studies should subject the assumptions behind methods for handling noncompliance and missing data to thorough sensitivity analyses. Modern machine-learning methods can help reduce reliance on correct model specification. Further research is required to develop flexible methods for handling more complex forms of noncompliance and missing data.

scholarly journals Using a Two-Sample Mendelian Randomization Method in Assessing the Causal Relationships Between Human Blood Metabolites and Heart Failure

2021 ◽  
Vol 8 ◽  
Author(s):  
Zixian Wang ◽  
Shiyu Chen ◽  
Qian Zhu ◽  
Yonglin Wu ◽  
Guifeng Xu ◽  
...  
Keyword(s):  
Heart Failure ◽  
Human Blood ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Meta Analysis ◽  
Sensitivity Analyses ◽  
Blood Metabolites ◽  
Causal Relationships

Background: Heart failure (HF) is the main cause of morbidity and mortality worldwide, and metabolic dysfunction is an important factor related to HF pathogenesis and development. However, the causal effect of blood metabolites on HF remains unclear.Objectives: Our chief aim is to investigate the causal relationships between human blood metabolites and HF risk.Methods: We used an unbiased two-sample Mendelian randomization (MR) approach to assess the causal relationships between 486 human blood metabolites and HF risk. Exposure information was obtained from Sample 1, which is the largest metabolome-based genome-wide association study (mGWAS) data containing 7,824 Europeans. Outcome information was obtained from Sample 2, which is based on the results of a large-scale GWAS meta-analysis of HF and contains 47,309 cases and 930,014 controls of Europeans. The inverse variance weighted (IVW) model was used as the primary two-sample MR analysis method and followed the sensitivity analyses, including heterogeneity test, horizontal pleiotropy test, and leave-one-out analysis.Results: We observed that 11 known metabolites were potentially related to the risk of HF after using the IVW method (P &lt; 0.05). After adding another four MR models and performing sensitivity analyses, we found a 1-SD increase in the xenobiotics 4-vinylphenol sulfate was associated with ~22% higher risk of HF (OR [95%CI], 1.22 [1.07–1.38]).Conclusions: We revealed that the 4-vinylphenol sulfate may nominally increase the risk of HF by 22% after using a two-sample MR approach. Our findings may provide novel insights into the pathogenesis underlying HF and novel strategies for HF prevention.

scholarly journals Using the MR-Base platform to investigate risk factors and drug targets for thousands of phenotypes

2019 ◽  
Vol 4 ◽  
pp. 113 ◽  
Author(s):  
Venexia M Walker ◽  
Neil M Davies ◽  
Gibran Hemani ◽  
Jie Zheng ◽  
Philip C Haycock ◽  
...  
Keyword(s):  
Risk Factors ◽  
Web Application ◽  
Drug Targets ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
R Package ◽  
Sensitivity Analyses ◽  
Link Type ◽  
Study Results

Mendelian randomization (MR) estimates the causal effect of exposures on outcomes by exploiting genetic variation to address confounding and reverse causation. This method has a broad range of applications, including investigating risk factors and appraising potential targets for intervention. MR-Base has become established as a freely accessible, online platform, which combines a database of complete genome-wide association study results with an interface for performing Mendelian randomization and sensitivity analyses. This allows the user to explore millions of potentially causal associations. MR-Base is available as a web application or as an R package. The technical aspects of the tool have previously been documented in the literature. The present article is complementary to this as it focuses on the applied aspects. Specifically, we describe how MR-Base can be used in several ways, including to perform novel causal analyses, replicate results and enable transparency, amongst others. We also present three use cases, which demonstrate important applications of Mendelian randomization and highlight the benefits of using MR-Base for these types of analyses.

scholarly journals Effect of Basal Metabolic Rate on Cancer: A Mendelian Randomization Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Jack C. M. Ng ◽  
C. Mary Schooling
Keyword(s):  
Growth Factor ◽  
Cancer Risk ◽  
Confidence Interval ◽  
Metabolic Rate ◽  
Basal Metabolic Rate ◽  
Odds Ratio ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Sensitivity Analyses ◽  
Insulin Like Growth Factor

Background: Basal metabolic rate is associated with cancer, but these observations are open to confounding. Limited evidence from Mendelian randomization studies exists, with inconclusive results. Moreover, whether basal metabolic rate has a similar role in cancer for men and women independent of insulin-like growth factor 1 increasing cancer risk has not been investigated.Methods: We conducted a two-sample Mendelian randomization study using summary data from the UK Biobank to estimate the causal effect of basal metabolic rate on cancer. Overall and sex-specific analysis and multiple sensitivity analyses were performed including multivariable Mendelian randomization to control for insulin-like growth factor 1.Results: We obtained 782 genetic variants strongly (p-value &lt; 5 × 10–8) and independently (r2 &lt; 0.01) predicting basal metabolic rate. Genetically predicted higher basal metabolic rate was associated with an increase in cancer risk overall (odds ratio, 1.06; 95% confidence interval, 1.02–1.10) with similar estimates by sex (odds ratio for men, 1.07; 95% confidence interval, 1.002–1.14; odds ratio for women, 1.06; 95% confidence interval, 0.995–1.12). Sensitivity analyses including adjustment for insulin-like growth factor 1 showed directionally consistent results.Conclusion: Higher basal metabolic rate might increase cancer risk. Basal metabolic rate as a potential modifiable target of cancer prevention warrants further study.

scholarly journals Sensitivity analysis of point and parametric pedotransfer functions for estimating water retention of soils in Algeria

SOIL ◽  
2016 ◽  
Vol 2 (4) ◽  
pp. 647-657 ◽  
Author(s):  
Sami Touil ◽  
Aurore Degre ◽  
Mohamed Nacer Chabaca
Keyword(s):  
Water Retention ◽  
Sensitivity Analyses ◽  
Pedotransfer Functions ◽  
Sand Fraction ◽  
Significant Difference ◽  
Point Pressure ◽  
Input Variables ◽  
Clay Percentage ◽  
Different Sources ◽  
Linear Regressions

Abstract. Improving the accuracy of pedotransfer functions (PTFs) requires studying how prediction uncertainty can be apportioned to different sources of uncertainty in inputs. In this study, the question addressed was as follows: which variable input is the main or best complementary predictor of water retention, and at which water potential? Two approaches were adopted to generate PTFs: multiple linear regressions (MLRs) for point PTFs and multiple nonlinear regressions (MNLRs) for parametric PTFs. Reliability tests showed that point PTFs provided better estimates than parametric PTFs (root mean square error, RMSE: 0.0414 and 0.0444 cm3 cm−3, and 0.0613 and 0.0605 cm3 cm−3 at −33 and −1500 kPa, respectively). The local parametric PTFs provided better estimates than Rosetta PTFs at −33 kPa. No significant difference in accuracy, however, was found between the parametric PTFs and Rosetta H2 at −1500 kPa with RMSE values of 0.0605 cm3 cm−3 and 0.0636 cm3 cm−3, respectively. The results of global sensitivity analyses (GSAs) showed that the mathematical formalism of PTFs and their input variables reacted differently in terms of point pressure and texture. The point and parametric PTFs were sensitive mainly to the sand fraction in the fine- and medium-textural classes. The use of clay percentage (C %) and bulk density (BD) as inputs in the medium-textural class improved the estimation of PTFs at −33 kPa.

Endogenous democracy: causal evidence from the potato productivity shock in the old world

2020 ◽  
pp. 1-8
Author(s):  
Joan Barceló ◽  
Guillermo Rosas
Keyword(s):  
Economic Development ◽  
Causal Effect ◽  
Transition To Democracy ◽  
Sensitivity Analyses ◽  
Old World ◽  
Omitted Variable Bias ◽  
Productivity Shock ◽  
Variable Bias ◽  
The Impact ◽  
New Evidence

Abstract Despite a high cross-country correlation between development and democracy, it is difficult to gauge the impact of economic development on the probability that autocracies will transition to democracy because of endogeneity, especially due to reverse causation and omitted variable bias. Hence, whether development causes democracy remains a contested issue. We exploit exogeneity in the regional variation of potato cultivation along with the timing of the introduction of potatoes to the Old World (i.e., a potato productivity shock) to identify a causal effect of urbanization, a proxy for economic development, on democratization. Our results, which hold under sensitivity analyses that question the validity of the exclusion restriction, present new evidence of the existence of a causal effect of economic development on democracy.

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