scholarly journals Response to letter: Sibling models—An underused tool with limitations

2021 ◽  
Author(s):  
Øystein Kravdal
Keyword(s):  
Sibling Models

Sibling Models, Categorical Outcomes, and the Intra-Class Correlation

2021 ◽  
Author(s):  
Richard Breen ◽  
John Ermisch
Keyword(s):  
Random Effects ◽  
Probability Model ◽  
Intraclass Correlation ◽  
Linear Probability Model ◽  
Intra Class Correlation ◽  
Sibling Differences ◽  
Sibling Models ◽  
Alternative Measures ◽  
Linear Probability ◽  
Parametric Estimate

Abstract In sibling models with categorical outcomes the question arises of how best to calculate the intraclass correlation, ICC. We show that, for this purpose, the random effects linear probability model is preferable to a random effects non-linear probability model, such as a logit or probit. This is because, for a binary outcome, the ICC derived from a random effects linear probability model is a non-parametric estimate of the ICC, equivalent to a statistic called Cohen’s κ. Furthermore, because κ can be calculated when the outcome has more than two categories, we can use the random effects linear probability model to compute a single ICC in cases with more than two outcome categories. Lastly, ICCs are often compared between groups to show the degree to which sibling differences vary between groups: we show that when the outcome is categorical these comparisons are invalid. We suggest alternative measures for this purpose.

Detection and prevention of neurologic injury in the intensive care unit

2005 ◽  
Vol 15 (S1) ◽  
pp. 149-153 ◽  
Author(s):  
George M. Hoffman
Keyword(s):  
Perioperative Management ◽  
Resonance Imaging ◽  
Genetic Determinants ◽  
Neurologic Injury ◽  
Functional Deficits ◽  
Complex Interactions ◽  
Postoperative Changes ◽  
Increased Risk ◽  
Sibling Models ◽  
Physical Limitations

Survivors of repairs of complex congenital cardiac malformations in infancy have an increased risk of permanent abnormalities in motor, cognitive, expressive, and behavioral functioning. These functional deficits are expressions of complex interactions of environment, including prolonged hospitalization and conditioned child–parental behaviours, alterations of social environment, the effects of physical limitations, biological influences including genetic determinants, prenatal injury, and acquired reversible and irreversible neuronal injury.1,2 The magnitude of the problem is large, with incidence dependent upon the measures used for assessment. Overt postoperative neurologic signs have been recorded in up to one-tenth of postoperative infants and children, with double that rate found in those with abnormalities of the aortic arch.3 A decreased potential for development, based upon parent-sibling models, has been estimated to occur in one-third of survivors.4,5 Evidence of injury is provided by magnetic resonance imaging in up to one-third of patients preoperatively, and between half and nine-tenths postoperatively, although most of these early postoperative changes will disappear.5 Although recent changes in perioperative management are likely to reduce such neurologic injury, their significance remains high.

scholarly journals Mediation Analysis in Sibling Designs: An Application to the Effect of Prenatal Antidepressant Exposure on Toddler Depression Mediated by Gestational Age at Birth

2021 ◽  
Author(s):  
Mollie Wood ◽  
Espen Eilertsen ◽  
Eivind Ystrom ◽  
Hedvig Nordeng ◽  
Sonia Hernandez-Diaz
Keyword(s):  
Gestational Age ◽  
Mediation Analysis ◽  
Full Cohort ◽  
Point Estimates ◽  
Mother And Child ◽  
Sibling Models ◽  
Z Scores ◽  
Gestational Age At Birth ◽  
Anxiety Depression ◽  
Age At Birth

Abstract Background: Mediation analysis requires strong assumptions of no unmeasured confounding. Sibling designs offer a method for controlling confounding shared within families, but no previous research has done mediation analysis using sibling models. Methods: We demonstrate the validity of the sibling mediation approach using simulation, and show its application using the example of prenatal antidepressant exposure and toddler anxiety and depression, with gestational age at birth as a mediator. We used data from the Norwegian Mother and Child Cohort Study, a cohort comprising 41% of births in Norway between 1999 and 2008 to identify 91,333 pregnancies, of which 25,776 were part of sibling groups. Results: In simulations, sibling models were less biased than cohort models in cases where non-shared confounding was weaker than shared confounding, and when stronger non-shared confounding was controlled, but more biased otherwise. In the full cohort, the estimated mean difference in depression/anxiety scale z-scores for natural direct effects (NDE) were 0.31 (95% confidence interval 0.23 to 0.39) and 0.14 (95% CI 0.03 to 0.24), without and with adjustment for non-shared confounders, respectively. The natural indirect effect was 0.01 (95% CI 0.00 to 0.02) after adjustment. Adjustment for shared and non-shared confounding showed similar point estimates with wider confidence intervals (NDE 0.18, 95% CI -0.21 to 0.47; NIE -0.01, 95% CI -0.06 to 0.06).Conclusions: Findings suggest that the modest association between prenatal antidepressant exposure and anxiety/depression is not mediated by gestational age and is likely explained by both shared confounders and non-shared confounders, and chance.

scholarly journals Attrition in Models of Intergenerational Links Using the PSID with Extensions to Health and to Sibling Models

2011 ◽  
Vol 11 (3) ◽  
Author(s):  
John M Fitzgerald
Keyword(s):  
National Health Interview Survey ◽  
Economic Status ◽  
Adult Health ◽  
Attrition Bias ◽  
Sibling Correlations ◽  
Sibling Differences ◽  
Intergenerational Links ◽  
Sibling Models ◽  
Interview Survey ◽  
Key Dimensions

Abstract Selective attrition potentially biases estimation of intergenerational links in health and economic status. This paper documents attrition in the PSID through 2007 for a cohort of children, and investigates attrition bias in intergenerational models predicting adult health, education and earnings, including models based on sibling differences. Although attrition affects unconditional means, the weighted PSID generally maintains its representativeness along key dimensions in comparison to the National Health Interview Survey. Using PSID, sibling correlations in outcomes and father-son correlations in earnings are not significantly affected by attrition. Models of intergenerational links with covariates yield more mixed results with females showing few robust impacts of attrition and males showing potential attrition bias for education and earnings outcomes. For adult health outcomes conditional on child background, neither gender shows significant impacts of attrition for the age ranges and models considered here. Sibling models do not produce robustly higher attrition impacts than individual models.

scholarly journals Interpreting Mendelian randomization estimates of the effects of categorical exposures such as disease status and educational attainment

2020 ◽  
Author(s):  
Laurence J Howe ◽  
Matthew Tudball ◽  
George Davey Smith ◽  
Neil M Davies
Keyword(s):  
Educational Attainment ◽  
Mendelian Randomization ◽  
Disease Status ◽  
Smoking Initiation ◽  
Continuous Variable ◽  
Full Time ◽  
Uk Biobank ◽  
Exposure Category ◽  
Sibling Models

AbstractMendelian randomization has been previously used to estimate the effects of binary and ordinal categorical exposures - e.g. type 2 diabetes or educational attainment defined by qualification - on outcomes. Binary and categorical phenotypes can be modelled in terms of liability, an underlying latent continuous variable with liability thresholds separating individuals into categories. Genetic variants typically influence an individual’s categorical exposure via their effects on liability, thus Mendelian randomization analyses with categorical exposures will capture effects of liability which act independent of exposure category.We discuss how groups where the categorical exposure is invariant can be used to detect liability effects acting independently of exposure category. For example, associations between an adult educational attainment polygenic score (PGS) and BMI measured before the minimum school leaving age (e.g. age 10), cannot indicate the effects of years in full-time education on this outcome. Using UK Biobank data, we show that a higher education PGS is strongly associated with lower smoking initiation and higher glasses use at age 15. These associations were replicated in sibling models. An orthogonal approach using the raising of the school leaving age (ROSLA) policy change found that individuals who chose to remain in education to age 16 before the reform likely had higher liability to educational attainment than those who were compelled to remain in education to 16 after the reform, and had higher income, decreased cigarette smoking, higher glasses use and lower deprivation in adulthood. These results suggest that liability to educational attainment associates with health and social outcomes independent of years in full-time education.Mendelian randomization studies with non-continuous exposures should be interpreted in terms of liability, which may affect the outcome via changes in exposure category and/or independently.

Long-term trends in age-specific recruitment of sockeye salmon (Oncorhynchus nerka) in a changing environment

2004 ◽  
Vol 61 (12) ◽  
pp. 2455-2470 ◽  
Author(s):  
Carrie A Holt ◽  
Randall M Peterman
Keyword(s):  
Kalman Filter ◽  
Sockeye Salmon ◽  
Large Scale ◽  
Oncorhynchus Nerka ◽  
Age At Maturity ◽  
Age Class ◽  
Sibling Models ◽  
Long Term Trends ◽  
Forecasting Errors

Sibling – age-class (sibling) models, which relate abundance of one age-class of adult sockeye salmon (Oncorhynchus nerka) to abundance of the previous age-class in the previous year, are commonly used to forecast abundance 1 year ahead. Standard sibling models assume constant parameters over time. However, many sockeye salmon populations have shown temporal changes in age-at-maturity. We therefore developed a new Kalman filter sibling model that allowed for time-varying parameters. We found considerable evidence for long-term trends in parameters of sibling models for 24 sockeye salmon stocks in British Columbia and Alaska; most trends reflected increasing age-at-maturity. In a retrospective analysis, the Kalman filter forecasting models reduced mean-squared forecasting errors compared with standard sibling models in 29%–39% of the stocks depending on the age-class. The Kalman filter models also had mean percent biases closer to zero than the standard models for 54%–94% of the stocks. Parameters of these sibling models are positively correlated among stocks from different regions, suggesting that large-scale factors (e.g., competition among stocks for limited marine prey) may be important drivers of long-term changes in age-at-maturity schedules in sockeye salmon.

scholarly journals Sibling Models of Socioeconomic Effects on the Timing of First Premarital Birth

Demography ◽  
1997 ◽  
Vol 34 (4) ◽  
pp. 493 ◽  
Author(s):  
Daniel A. Powers ◽  
James Cherng-tay Hsueh
Keyword(s):  
Sibling Models ◽  
Premarital Birth
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