Search results for " Twin data"

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A Critical Review of Statistical Methods for Twin Studies Relating Exposure to Early Life Health Conditions

2021

International audience; When investigating disease etiology, twin data provide a unique opportunity to control for confounding and disentangling the role of the human genome and exposome. However, using appropriate statistical methods is fundamental for exploiting such potential. We aimed to critically review the statistical approaches used in twin studies relating exposure to early life health conditions. We searched PubMed, Scopus, Web of Science, and Embase (2011–2021). We identified 32 studies and nine classes of methods. Five were conditional approaches (within-pair analyses): additive-common-erratic (ACE) models (11 studies), generalized linear mixed models (GLMMs, five studies), gene…

ExposomeComputer scienceHealth Toxicology and MutagenesisInferenceMarginal modelReviewexposomeGeneralized linear mixed modeltwin data03 medical and health sciences0302 clinical medicineDiscriminative modelchildren[STAT.AP] Statistics [stat]/Applications [stat.AP]StatisticsHumans030212 general & internal medicineGeneralized estimating equationchildren Exposome Genome Health Statistical methods Twin data Humans Linear Models Models Statisticalgenome030304 developmental biology0303 health sciences[STAT.AP]Statistics [stat]/Applications [stat.AP]Models StatisticalConfoundingPublic Health Environmental and Occupational HealthRhealthTwin studychildren exposome genome health statistical methods twin data[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologieLinear Modelsstatistical methodsMedicine[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie
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The Return-to-Entrepreneurship Puzzle

2013

The returns to entrepreneurship are monetary and non-monetary. We offer new evidence on these returns using a large sample of genetically identical male twins. Our within-twin analysis suggests that OLS estimates are downwards, and traditional first-differenced panel data estimates upwards biased. We find no differences in the earnings of men with either low or high education. Our within-twin analysis of non-monetary returns shows that entrepreneurs with low education work longer hours and have greater responsibilities, but also face a reduced risk of divorce and less monotonous work tasks. The same does not apply to highly educated entrepreneurs.

Organizational Behavior and Human Resource ManagementEconomics and EconometricsLabour economicsEntrepreneurshipta511Earningsmedia_common.quotation_subjectControl (management)05 social sciencesLarge samplejel:J24Work (electrical)jel:L260502 economics and business8. Economic growthEconomics050207 economicsWelfarehealth care economics and organizations050203 business & managemententrepreneurship; earnings; twin data; education; monetary returns; nonmonetary returns; selectionPanel dataTwo-part tariffmedia_commonSSRN Electronic Journal
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