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- Articles and reports: 11-522-X200600110399Description:
In health studies, it is quite common to collect binary or count repeated responses along with a set of multi-dimensional covariates over a small period of time from a large number of independent families, where the families are selected from a finite population by using certain complex sampling designs. It is of interest to examine the effects of the covariates on the familial longitudinal responses after taking the variation in the family effects as well as the longitudinal correlations of the repeated responses into account. In this paper, I review the advantages and drawbacks of the existing methodologies for the estimation of the regression effects, the variance of the family effects and the longitudinal correlations. We then outline the advantages of a new unified generalized quasilikelihood approach in analyzing the complex design based familial longitudinal data. Some existing numerical studies are discussed as illustrations of the methodologies considered in the paper.
Release date: 2008-03-17 - Articles and reports: 11-522-X200600110415Description:
We discuss methods for the analysis of case-control studies in which the controls are drawn using a complex sample survey. The most straightforward method is the standard survey approach based on weighted versions of population estimating equations. We also look at more efficient methods and compare their robustness to model mis-specification in simple cases. Case-control family studies, where the within-cluster structure is of interest in its own right, are also discussed briefly.
Release date: 2008-03-17 - 3. Geovisualization of health and social capital data derived from Statistics Canada surveys ArchivedArticles and reports: 11-522-X200600110447Description:
The classification and identification of locations where persons report to be more or less healthy or have more or less social capital, within a specific area such as a health region, is tremendously helpful for understanding place and health associations. The objective of the proposed study is to classify and map areas within the Zone 6 Health Region (Figure 1) of Nova Scotia (Halifax Regional Municipality and Annapolis Valley regions) according to health status (Dimension 1) and social capital (Dimension 2). We abstracted responses to questions about self-reported health status, mental health, and social capital from the master files of the Canadian Community Health Survey (Cycles 1.1, 1.2 and 2.1), National Population Health Survey (Cycle 5), and the General Social Survey (Cycles 13, 14, 17, and 18). Responses were geocoded using the Statistics Canada Postal Code Conversion File (PCCF+) and imported into a geographical information system (GIS) so that the postal code associated with the response will be assigned to a latitude and longitude within the Nova Scotia Zone 6 health region. Kernel density estimators and additional spatial interpolators were used to develop statistically-smoothed surfaces of the distribution of respondent values for each question. The smoothing process eliminates the possibility of revealing individual respondent location and confidential Statistics Canada sampling frame information. Using responses from similar questions across multiple surveys improves the likelihood of detecting heterogeneity among the responses within the health region area, as well as the accuracy of the smoothed map classification.
Release date: 2008-03-17
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Articles and reports (3)
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- Articles and reports: 11-522-X200600110399Description:
In health studies, it is quite common to collect binary or count repeated responses along with a set of multi-dimensional covariates over a small period of time from a large number of independent families, where the families are selected from a finite population by using certain complex sampling designs. It is of interest to examine the effects of the covariates on the familial longitudinal responses after taking the variation in the family effects as well as the longitudinal correlations of the repeated responses into account. In this paper, I review the advantages and drawbacks of the existing methodologies for the estimation of the regression effects, the variance of the family effects and the longitudinal correlations. We then outline the advantages of a new unified generalized quasilikelihood approach in analyzing the complex design based familial longitudinal data. Some existing numerical studies are discussed as illustrations of the methodologies considered in the paper.
Release date: 2008-03-17 - Articles and reports: 11-522-X200600110415Description:
We discuss methods for the analysis of case-control studies in which the controls are drawn using a complex sample survey. The most straightforward method is the standard survey approach based on weighted versions of population estimating equations. We also look at more efficient methods and compare their robustness to model mis-specification in simple cases. Case-control family studies, where the within-cluster structure is of interest in its own right, are also discussed briefly.
Release date: 2008-03-17 - 3. Geovisualization of health and social capital data derived from Statistics Canada surveys ArchivedArticles and reports: 11-522-X200600110447Description:
The classification and identification of locations where persons report to be more or less healthy or have more or less social capital, within a specific area such as a health region, is tremendously helpful for understanding place and health associations. The objective of the proposed study is to classify and map areas within the Zone 6 Health Region (Figure 1) of Nova Scotia (Halifax Regional Municipality and Annapolis Valley regions) according to health status (Dimension 1) and social capital (Dimension 2). We abstracted responses to questions about self-reported health status, mental health, and social capital from the master files of the Canadian Community Health Survey (Cycles 1.1, 1.2 and 2.1), National Population Health Survey (Cycle 5), and the General Social Survey (Cycles 13, 14, 17, and 18). Responses were geocoded using the Statistics Canada Postal Code Conversion File (PCCF+) and imported into a geographical information system (GIS) so that the postal code associated with the response will be assigned to a latitude and longitude within the Nova Scotia Zone 6 health region. Kernel density estimators and additional spatial interpolators were used to develop statistically-smoothed surfaces of the distribution of respondent values for each question. The smoothing process eliminates the possibility of revealing individual respondent location and confidential Statistics Canada sampling frame information. Using responses from similar questions across multiple surveys improves the likelihood of detecting heterogeneity among the responses within the health region area, as well as the accuracy of the smoothed map classification.
Release date: 2008-03-17
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