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- 1. International surveys: Motives and methodologies ArchivedArticles and reports: 12-001-X200800210754Description:
The context of the discussion is the increasing incidence of international surveys, of which one is the International Tobacco Control (ITC) Policy Evaluation Project, which began in 2002. The ITC country surveys are longitudinal, and their aim is to evaluate the effects of policy measures being introduced in various countries under the WHO Framework Convention on Tobacco Control. The challenges of organization, data collection and analysis in international surveys are reviewed and illustrated. Analysis is an increasingly important part of the motivation for large scale cross-cultural surveys. The fundamental challenge for analysis is to discern the real response (or lack of response) to policy change, separating it from the effects of data collection mode, differential non-response, external events, time-in-sample, culture, and language. Two problems relevant to statistical analysis are discussed. The first problem is the question of when and how to analyze pooled data from several countries, in order to strengthen conclusions which might be generally valid. While in some cases this seems to be straightforward, there are differing opinions on the extent to which pooling is possible and reasonable. It is suggested that for formal comparisons, random effects models are of conceptual use. The second problem is to find models of measurement across cultures and data collection modes which will enable calibration of continuous, binary and ordinal responses, and produce comparisons from which extraneous effects have been removed. It is noted that hierarchical models provide a natural way of relaxing requirements of model invariance across groups.
Release date: 2008-12-23 - Articles and reports: 82-003-X200800410747Geography: CanadaDescription:
A selective approach may be used in an ecological study where the aim is to choose a subset of units of analysis (UAs) and produce interpretations about a population of interest (PI) based solely on those UAs. The results for the PI will be reliable if that population is concentrated in the selected UAs and rare in other UAs. This article presents a graphical tool that helps determine whether these conditions are satisfied.
Release date: 2008-12-17 - 3. Canadian Environmental Sustainability Indicators: Air Quality Indicators: Data Sources and MethodsJournals and periodicals: 16-254-XGeography: CanadaDescription:
This report presents details on the data sources and methods underlying the air quality indicators as they were reported in Canadian Environmental Sustainability Indicators, 2007 (16-251-XWE). The air quality indicators focus on human exposure to ground-level ozone and fine particulate matter.
Details on the indicators reported after 2007 can be found on Environment Canada's site: &&www.ec.gc.ca/indicateurs-indicators/
Release date: 2008-06-20 - 4. Canadian Environmental Sustainability Indicators: Freshwater Quality Indicator: Data Sources and Methods ArchivedJournals and periodicals: 16-256-XGeography: CanadaDescription:
This report presents details on the data sources and methods underlying the freshwater quality indicator as it was reported in the Canadian Environmental Sustainability Indicators, 2007 (16-251-XWE). The national freshwater quality indicator provides an overall measure of the suitability of water bodies to support aquatic life in selected monitoring sites in Canada.
Details on this indicator reported after 2007 can be found on Environment Canada's site: www.ec.gc.ca/indicateurs-indicators/
Release date: 2008-06-20 - Articles and reports: 75F0002M2008004Description:
Low income cut-offs (LICOs) are income thresholds, determined by analysing family expenditure data, below which families will devote a larger share of income to the necessities of food, shelter and clothing than the average family would. To reflect differences in the costs of necessities among different community and family sizes, LICOs are defined for five categories of community size and seven of family size.
Low income Measures (LIMs), on the other hand, are strictly relative measures of low income, set at 50% of adjusted median family income. These measures are categorized according to the number of adults and children present in families, reflecting the economies of scale inherent in family size and composition. This publication incorporates a detailed description of the methods used to arrive at both measurements. It also explains how base years are defined and how LICOs are updated using the Consumer Price Index.
Release date: 2008-06-04 - 6. Methodological challenges in analyzing nutrition data from the Canadian Community Health Survey - Nutrition ArchivedArticles and reports: 11-522-X200600110394Description:
Statistics Canada conducted the Canadian Community Health Survey - Nutrition in 2004. The survey's main objective was to estimate the distributions of Canadians' usual dietary intake at the provincial level for 15 age-sex groups. Such distributions are generally estimated with the SIDE application, but with the choices that were made concerning sample design and method of estimating sampling variability, obtaining those estimates is not a simple matter. This article describes the methodological challenges in estimating usual intake distributions from the survey data using SIDE.
Release date: 2008-03-17 - 7. Measurement error in life history data ArchivedArticles and reports: 11-522-X200600110397Description:
In practice it often happens that some collected data are subject to measurement error. Sometimes covariates (or risk factors) of interest may be difficult to observe precisely due to physical location or cost. Sometimes it is impossible to measure covariates accurately due to the nature of the covariates. In other situations, a covariate may represent an average of a certain quantity over time, and any practical way of measuring such a quantity necessarily features measurement error. When carrying out statistical inference in such settings, it is important to account for the effects of mismeasured covariates; otherwise, erroneous or even misleading results may be produced. In this paper, we discuss several measurement error examples arising in distinct contexts. Specific attention is focused on survival data with covariates subject to measurement error. We discuss a simulation-extrapolation method for adjusting for measurement error effects. A simulation study is reported.
Release date: 2008-03-17 - 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 - 9. Estimation of attributable number of deaths and standard errors from simple and complex sampled cohorts ArchivedArticles and reports: 11-522-X200600110400Description:
Estimates of the attributable number of deaths (AD) from all-causes can be obtained by first estimating population attributable risk (AR) adjusted for confounding covariates, and then multiplying the AR by the number of deaths determined from vital mortality statistics that occurred for a specific time period. Proportional hazard regression estimates of adjusted relative hazards obtained from mortality follow-up data from a cohort or a survey is combined with a joint distribution of risk factor and confounding covariates to compute an adjusted AR. Two estimators of adjusted AR are examined, which differ according to the reference population that the joint distribution of risk factor and confounders is obtained. The two types of reference populations considered: (i) the population that is represented by the baseline cohort and (ii) a population that is external to the cohort. Methods based on influence function theory are applied to obtain expressions for estimating the variance of the AD estimator. These variance estimators can be applied to data that range from simple random samples to (sample) weighted multi-stage stratified cluster samples from national household surveys. The variance estimation of AD is illustrated in an analysis of excess deaths due to having a non-ideal body mass index using data from the second National Health and Examination Survey (NHANES) Mortality Study and the 1999-2002 NHANES. These methods can also be used to estimate the attributable number of cause-specific deaths or incident cases of a disease and their standard errors when the time period for the accrual of is short.
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
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Analysis (18)
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- 1. International surveys: Motives and methodologies ArchivedArticles and reports: 12-001-X200800210754Description:
The context of the discussion is the increasing incidence of international surveys, of which one is the International Tobacco Control (ITC) Policy Evaluation Project, which began in 2002. The ITC country surveys are longitudinal, and their aim is to evaluate the effects of policy measures being introduced in various countries under the WHO Framework Convention on Tobacco Control. The challenges of organization, data collection and analysis in international surveys are reviewed and illustrated. Analysis is an increasingly important part of the motivation for large scale cross-cultural surveys. The fundamental challenge for analysis is to discern the real response (or lack of response) to policy change, separating it from the effects of data collection mode, differential non-response, external events, time-in-sample, culture, and language. Two problems relevant to statistical analysis are discussed. The first problem is the question of when and how to analyze pooled data from several countries, in order to strengthen conclusions which might be generally valid. While in some cases this seems to be straightforward, there are differing opinions on the extent to which pooling is possible and reasonable. It is suggested that for formal comparisons, random effects models are of conceptual use. The second problem is to find models of measurement across cultures and data collection modes which will enable calibration of continuous, binary and ordinal responses, and produce comparisons from which extraneous effects have been removed. It is noted that hierarchical models provide a natural way of relaxing requirements of model invariance across groups.
Release date: 2008-12-23 - Articles and reports: 82-003-X200800410747Geography: CanadaDescription:
A selective approach may be used in an ecological study where the aim is to choose a subset of units of analysis (UAs) and produce interpretations about a population of interest (PI) based solely on those UAs. The results for the PI will be reliable if that population is concentrated in the selected UAs and rare in other UAs. This article presents a graphical tool that helps determine whether these conditions are satisfied.
Release date: 2008-12-17 - 3. Canadian Environmental Sustainability Indicators: Air Quality Indicators: Data Sources and MethodsJournals and periodicals: 16-254-XGeography: CanadaDescription:
This report presents details on the data sources and methods underlying the air quality indicators as they were reported in Canadian Environmental Sustainability Indicators, 2007 (16-251-XWE). The air quality indicators focus on human exposure to ground-level ozone and fine particulate matter.
Details on the indicators reported after 2007 can be found on Environment Canada's site: &&www.ec.gc.ca/indicateurs-indicators/
Release date: 2008-06-20 - 4. Canadian Environmental Sustainability Indicators: Freshwater Quality Indicator: Data Sources and Methods ArchivedJournals and periodicals: 16-256-XGeography: CanadaDescription:
This report presents details on the data sources and methods underlying the freshwater quality indicator as it was reported in the Canadian Environmental Sustainability Indicators, 2007 (16-251-XWE). The national freshwater quality indicator provides an overall measure of the suitability of water bodies to support aquatic life in selected monitoring sites in Canada.
Details on this indicator reported after 2007 can be found on Environment Canada's site: www.ec.gc.ca/indicateurs-indicators/
Release date: 2008-06-20 - Articles and reports: 75F0002M2008004Description:
Low income cut-offs (LICOs) are income thresholds, determined by analysing family expenditure data, below which families will devote a larger share of income to the necessities of food, shelter and clothing than the average family would. To reflect differences in the costs of necessities among different community and family sizes, LICOs are defined for five categories of community size and seven of family size.
Low income Measures (LIMs), on the other hand, are strictly relative measures of low income, set at 50% of adjusted median family income. These measures are categorized according to the number of adults and children present in families, reflecting the economies of scale inherent in family size and composition. This publication incorporates a detailed description of the methods used to arrive at both measurements. It also explains how base years are defined and how LICOs are updated using the Consumer Price Index.
Release date: 2008-06-04 - 6. Methodological challenges in analyzing nutrition data from the Canadian Community Health Survey - Nutrition ArchivedArticles and reports: 11-522-X200600110394Description:
Statistics Canada conducted the Canadian Community Health Survey - Nutrition in 2004. The survey's main objective was to estimate the distributions of Canadians' usual dietary intake at the provincial level for 15 age-sex groups. Such distributions are generally estimated with the SIDE application, but with the choices that were made concerning sample design and method of estimating sampling variability, obtaining those estimates is not a simple matter. This article describes the methodological challenges in estimating usual intake distributions from the survey data using SIDE.
Release date: 2008-03-17 - 7. Measurement error in life history data ArchivedArticles and reports: 11-522-X200600110397Description:
In practice it often happens that some collected data are subject to measurement error. Sometimes covariates (or risk factors) of interest may be difficult to observe precisely due to physical location or cost. Sometimes it is impossible to measure covariates accurately due to the nature of the covariates. In other situations, a covariate may represent an average of a certain quantity over time, and any practical way of measuring such a quantity necessarily features measurement error. When carrying out statistical inference in such settings, it is important to account for the effects of mismeasured covariates; otherwise, erroneous or even misleading results may be produced. In this paper, we discuss several measurement error examples arising in distinct contexts. Specific attention is focused on survival data with covariates subject to measurement error. We discuss a simulation-extrapolation method for adjusting for measurement error effects. A simulation study is reported.
Release date: 2008-03-17 - 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 - 9. Estimation of attributable number of deaths and standard errors from simple and complex sampled cohorts ArchivedArticles and reports: 11-522-X200600110400Description:
Estimates of the attributable number of deaths (AD) from all-causes can be obtained by first estimating population attributable risk (AR) adjusted for confounding covariates, and then multiplying the AR by the number of deaths determined from vital mortality statistics that occurred for a specific time period. Proportional hazard regression estimates of adjusted relative hazards obtained from mortality follow-up data from a cohort or a survey is combined with a joint distribution of risk factor and confounding covariates to compute an adjusted AR. Two estimators of adjusted AR are examined, which differ according to the reference population that the joint distribution of risk factor and confounders is obtained. The two types of reference populations considered: (i) the population that is represented by the baseline cohort and (ii) a population that is external to the cohort. Methods based on influence function theory are applied to obtain expressions for estimating the variance of the AD estimator. These variance estimators can be applied to data that range from simple random samples to (sample) weighted multi-stage stratified cluster samples from national household surveys. The variance estimation of AD is illustrated in an analysis of excess deaths due to having a non-ideal body mass index using data from the second National Health and Examination Survey (NHANES) Mortality Study and the 1999-2002 NHANES. These methods can also be used to estimate the attributable number of cause-specific deaths or incident cases of a disease and their standard errors when the time period for the accrual of is short.
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
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