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Grant: Discovery Grants Program - Individual

Memorial University of Newfoundland

$14,000.00

Bayesian Methods for Imprecise Data

FY 2018-19

Summary
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 average number of cigarettes smoked a day 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
Discovery Grants Program - Individual
Location
Newfoundland and Labrador, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
RGPIN-2017-04256
Name nom
Abarin, Taraneh
Department département
Mathematics and Statistics
Organization id
89
Institution établissement
Memorial University of Newfoundland
Province en
Newfoundland and Labrador
Province fr
Terre-Neuve-et-Labrador
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2018
Competition year année de concours
2017
Award amount
14000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1508
Committee name en
Mathematics and Statistics
Committee name fr
Mathématiques et statistique
Area of application code
1211
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Medical and health sciences
Area of application fr
Sciences médicales et de la santé
Research subject code
3001
Research subject group en
Statistics and probability
Research subject group fr
Statistique et probabilité
Research subject en
Statistical theory
Research subject fr
Théorie de statistique
Partie
2
Nb partie
5
Application title
Bayesian Methods for Imprecise Data
Keyword
NSERC
Application summary
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 average number of cigarettes smoked a day 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.*
Memorial University of Newfoundland - $14,000 Grant (… | Canada Spends