Grant: Discovery Grants Program - Individual
University of Western Ontario
$11,000.00
Statistical Methods for Correlated Data in Health Research
EF 2015-16
Résumé
The general goal of my research program is to develop statistical methods that can have a real impact on statistical practice. Specific objectives for the next five years are as follows: 1) Development of methods for complex inter-observer and reliability studies with binary outcomes. In this part of my research, I will develop methods for concurrent assessment of both inter-observer agreement and intra-observer reliability. The finite sample performance of asymptotic results will be compared to the bootstrap. These results will be used to analyze several ongoing large studies for which I am a co-investigator. 2) Development of methods for community intervention trials with time-to-event outcomes. A defining feature of these trials is that they often involve a small number of large clusters. However, methods that can be safely applied such trials are not well developed. I will propose small sample adjustments to asymptotic methods. I will also develop computationally intensive approaches such as the bootstrap. 3) Development of confidence interval procedures for the intraclass correlation coefficients in cluster randomization trials with binary outcomes. Investigators frequently use point estimates from previous literature without fully appreciating the inherent lack of precision associated with these estimates. A confidence interval procedure that is applicable for trials with small number of large clusters would have high practical value. I will develop new procedures based on my previous results developed for large number of small clusters. Confidence interval procedure for new dependence parameters for binary outcomes arising from cluster randomization trials will also be developed. 4) Development of methods for causal mediation analysis of cluster randomization trials with binary outcomes. Although mediation analysis is a natural tool for illuminating how an intervention works, virtually no research work has been done in the context of cluster randomization trials. I will develop relevant statistical methods using the counterfactual framework.
Programme
Discovery Grants Program - Individual
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
260930-2013
Name nom
Zou, Guangyong
Department département
Epidemiology and Biostatistics
Organization id
36
Institution établissement
University of Western Ontario
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2015
Competition year année de concours
2013
Award amount
11000
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
3005
Research subject group en
Statistics and probability
Research subject group fr
Statistique et probabilité
Research subject en
Biostatistics
Research subject fr
Biostatistiques
Partie
3
Nb partie
5
Application title
Statistical Methods for Correlated Data in Health Research
Keyword
NSERC
Application summary
The general goal of my research program is to develop statistical methods that can have a real impact on statistical practice. Specific objectives for the next five years are as follows: 1) Development of methods for complex inter-observer and reliability studies with binary outcomes. In this part of my research, I will develop methods for concurrent assessment of both inter-observer agreement and intra-observer reliability. The finite sample performance of asymptotic results will be compared to the bootstrap. These results will be used to analyze several ongoing large studies for which I am a co-investigator. 2) Development of methods for community intervention trials with time-to-event outcomes. A defining feature of these trials is that they often involve a small number of large clusters. However, methods that can be safely applied such trials are not well developed. I will propose small sample adjustments to asymptotic methods. I will also develop computationally intensive approaches such as the bootstrap. 3) Development of confidence interval procedures for the intraclass correlation coefficients in cluster randomization trials with binary outcomes. Investigators frequently use point estimates from previous literature without fully appreciating the inherent lack of precision associated with these estimates. A confidence interval procedure that is applicable for trials with small number of large clusters would have high practical value. I will develop new procedures based on my previous results developed for large number of small clusters. Confidence interval procedure for new dependence parameters for binary outcomes arising from cluster randomization trials will also be developed. 4) Development of methods for causal mediation analysis of cluster randomization trials with binary outcomes. Although mediation analysis is a natural tool for illuminating how an intervention works, virtually no research work has been done in the context of cluster randomization trials. I will develop relevant statistical methods using the counterfactual framework.