Reference number : GC-2017-Q1-01862, amendment number : , agreement type : G, recipient legal name : Abarin, Taraneh (Memorial University of Newfoundland), recipient country : CA, recipient province : nan, recipient city : Newfoundland and Labrador|Terre-Neuve-et-Labrador, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : In statistical association studies, outcomes are determined by combinations of error-prone and accurately measured variables that interact with each other. Association models with interaction terms are widely applied in many areas, such as genetics, engineering, economics, education and epidemiology. For example, an interaction model is considered to assess whether or not the magnitude of an error-prone variable (such as self-reported average number of cigarettes smoked a day) on an outcome (e.g. risk of heart attack) is modified by accurately measured variables (e.g. age, gender, presence or absence of family history of a heart attack). As individuals in the study may have imprecise recall, the variable &ldquo;average number of cigarettes smoked a day&rdquo; is considered imprecise or measured with error. In order to improve the accuracy in the assessment of the effects of these variables on the outcome, one needs to take into account these errors. As techniques dealing with interaction terms when error-prone variables are involved, are quite challenging, in practice, we often see the use of additive models that ignore the interaction effects. However, erroneously omitting interactions in those models significantly reduces efficiency of these studies. 
There are only a few studies that recently implemented Bayesian (an advanced statistical method based on pre-experimental knowledge about unknown parameters) techniques into interaction models with measurement error. However, these studies are still at the early stage of development with many gaps to be filled. For example, majority of them consider models that do not include multiple accurately measured variables, which is more realistic in application. The proposed research is going to fill some of these gaps. It focuses on general association studies with the primary focus on interaction terms subject to error, within the Bayesian paradigm. The findings of this program will shed some light on some of the complex challenges in association studies., program purpose french : In statistical association studies, outcomes are determined by combinations of error-prone and accurately measured variables that interact with each other. Association models with interaction terms are widely applied in many areas, such as genetics, engineering, economics, education and epidemiology. For example, an interaction model is considered to assess whether or not the magnitude of an error-prone variable (such as self-reported average number of cigarettes smoked a day) on an outcome (e.g. risk of heart attack) is modified by accurately measured variables (e.g. age, gender, presence or absence of family history of a heart attack). As individuals in the study may have imprecise recall, the variable &ldquo;average number of cigarettes smoked a day&rdquo; is considered imprecise or measured with error. In order to improve the accuracy in the assessment of the effects of these variables on the outcome, one needs to take into account these errors. As techniques dealing with interaction terms when error-prone variables are involved, are quite challenging, in practice, we often see the use of additive models that ignore the interaction effects. However, erroneously omitting interactions in those models significantly reduces efficiency of these studies. 
There are only a few studies that recently implemented Bayesian (an advanced statistical method based on pre-experimental knowledge about unknown parameters) techniques into interaction models with measurement error. However, these studies are still at the early stage of development with many gaps to be filled. For example, majority of them consider models that do not include multiple accurately measured variables, which is more realistic in application. The proposed research is going to fill some of these gaps. It focuses on general association studies with the primary focus on interaction terms subject to error, within the Bayesian paradigm. The findings of this program will shed some light on some of the complex challenges in association studies., agreement title english : Bayesian Methods for Imprecise Data, agreement title french : Bayesian Methods for Imprecise Data, agreement number : RGPIN, agreement value : 70000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada