A Partly Parametric Additive Risk Model

1993 ◽  
Author(s):  
Ian W. McKeague ◽  
Peter D. Sasieni
Keyword(s):  
Risk Model ◽  
Additive Risk Model ◽  
Additive Risk

scholarly journals A Likelihood Ratio Test for Gene-Environment Interaction Based on the Trend Effect of Genotype Under an Additive Risk Model Using the Gene-Environment Independence Assumption

2020 ◽  
Vol 190 (1) ◽  
pp. 129-141
Author(s):  
Matthieu de Rochemonteix ◽  
Valerio Napolioni ◽  
Nilotpal Sanyal ◽  
Michaël E Belloy ◽  
Neil E Caporaso ◽  
...  
Keyword(s):  
Apolipoprotein E ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Risk Model ◽  
Ratio Test ◽  
Independence Assumption ◽  
Additive Risk Model ◽  
Gene Environment ◽  
Genome Wide ◽  
Additive Risk

Abstract Several statistical methods have been proposed for testing gene-environment (G-E) interactions under additive risk models using data from genome-wide association studies. However, these approaches have strong assumptions from underlying genetic models, such as dominant or recessive effects that are known to be less robust when the true genetic model is unknown. We aimed to develop a robust trend test employing a likelihood ratio test for detecting G-E interaction under an additive risk model, while incorporating the G-E independence assumption to increase power. We used a constrained likelihood to impose 2 sets of constraints for: 1) the linear trend effect of genotype and 2) the additive joint effects of gene and environment. To incorporate the G-E independence assumption, a retrospective likelihood was used versus a standard prospective likelihood. Numerical investigation suggests that the proposed tests are more powerful than tests assuming dominant, recessive, or general models under various parameter settings and under both likelihoods. Incorporation of the independence assumption enhances efficiency by 2.5-fold. We applied the proposed methods to examine the gene-smoking interaction for lung cancer and gene–apolipoprotein E $\varepsilon$4 interaction for Alzheimer disease, which identified 2 interactions between apolipoprotein E $\varepsilon$4 and loci membrane-spanning 4-domains subfamily A (MS4A) and bridging integrator 1 (BIN1) genes at genome-wide significance that were replicated using independent data.

The Risk of Neck, Shoulder and Upper Limb Musculoskeletal Disorders due to Interactions between Physical and Psychosocial Work Risk Factors

2000 ◽  
Vol 44 (30) ◽  
pp. 5-597-5-597 ◽  
Author(s):  
J. J. Devereux ◽  
P.W. Buckle
Keyword(s):  
Risk Factors ◽  
Upper Limb ◽  
Risk Model ◽  
Psychosocial Risk Factors ◽  
Psychosocial Risk ◽  
Additive Risk Model ◽  
Additive Risk ◽  
Upper Limb Disorders ◽  
Customer Services ◽  
Self Administered Questionnaire

Objectives - To investigate the possible interactions between physical and psychosocial risk factors in the workplace that may be associated with self-reported neck and upper-limb musculoskeletal disorder symptoms. Methods - 891 of 1514 manual handlers, delivery drivers, technicians, customer services computer operators and general office staff reported physical and psychosocial working conditions and neck and upper-limb disorder symptoms using a self-administered questionnaire (59% return rate). Of the 869 valid questionnaire respondents, 564 individual workers were classified in to one of four exposure groups: high physical - high psychosocial, high physical - low psychosocial, low physical - high psychosocial and low physical - low psychosocial. Results - The highest increase in risk was found in the high physical - high psychosocial exposure group for upper limb disorders. In the analyses, a departure from an additive risk model was observed for the upper-limb outcome definitions but not for those of the neck. Conclusions - This study suggests that an interaction effect between physical and psychosocial risk factors in the workplace may exist to increase the risk of self-reported upper-limb disorders.

A partly parametric additive risk model

Biometrika ◽  
1994 ◽  
Vol 81 (3) ◽  
pp. 501-514 ◽  
Author(s):  
IAN W. MCKEAGUE ◽  
PETER D. SASIENI
Keyword(s):  
Risk Model ◽  
Additive Risk Model ◽  
Additive Risk

scholarly journals The partly parametric and partly nonparametric additive risk model

2021 ◽  
Author(s):  
Nils Lid Hjort ◽  
Emil Aas Stoltenberg
Keyword(s):  
Hazard Rate ◽  
Goodness Of Fit ◽  
Risk Model ◽  
Real Data ◽  
Rate Function ◽  
Nonparametric Model ◽  
Additive Risk Model ◽  
Data Application ◽  
Additive Risk ◽  
Hazard Factor

AbstractAalen’s linear hazard rate regression model is a useful and increasingly popular alternative to Cox’ multiplicative hazard rate model. It postulates that an individual has hazard rate function $$h(s)=z_1\alpha _1(s)+\cdots +z_r\alpha _r(s)$$ h ( s ) = z 1 α 1 ( s ) + ⋯ + z r α r ( s ) in terms of his covariate values $$z_1,\ldots ,z_r$$ z 1 , … , z r . These are typically levels of various hazard factors, and may also be time-dependent. The hazard factor functions $$\alpha _j(s)$$ α j ( s ) are the parameters of the model and are estimated from data. This is traditionally accomplished in a fully nonparametric way. This paper develops methodology for estimating the hazard factor functions when some of them are modelled parametrically while the others are left unspecified. Large-sample results are reached inside this partly parametric, partly nonparametric framework, which also enables us to assess the goodness of fit of the model’s parametric components. In addition, these results are used to pinpoint how much precision is gained, using the parametric-nonparametric model, over the standard nonparametric method. A real-data application is included, along with a brief simulation study.

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