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  • Articles and reports: 12-001-X202300200005
    Description: Population undercoverage is one of the main hurdles faced by statistical analysis with non-probability survey samples. We discuss two typical scenarios of undercoverage, namely, stochastic undercoverage and deterministic undercoverage. We argue that existing estimation methods under the positivity assumption on the propensity scores (i.e., the participation probabilities) can be directly applied to handle the scenario of stochastic undercoverage. We explore strategies for mitigating biases in estimating the mean of the target population under deterministic undercoverage. In particular, we examine a split population approach based on a convex hull formulation, and construct estimators with reduced biases. A doubly robust estimator can be constructed if a followup subsample of the reference probability survey with measurements on the study variable becomes feasible. Performances of six competing estimators are investigated through a simulation study and issues which require further investigation are briefly discussed.
    Release date: 2024-01-03

  • Articles and reports: 11-522-X202100100017
    Description: The outbreak of the COVID-19 pandemic required the Government of Canada to provide relevant and timely information to support decision-making around a host of issues, including personal protective equipment (PPE) procurement and deployment. Our team built a compartmental epidemiological model from an existing code base to project PPE demand under a range of epidemiological scenarios. This model was further enhanced using data science techniques, which allowed for the rapid development and dissemination of model results to inform policy decisions.

    Key Words: COVID-19; SARS-CoV-2; Epidemiological model; Data science; Personal Protective Equipment (PPE); SEIR

    Release date: 2021-10-22

  • Articles and reports: 12-001-X202000100003
    Description:

    Probability sampling designs are sometimes used in conjunction with model-based predictors of finite population quantities. These designs should minimize the anticipated variance (AV), which is the variance over both the superpopulation and sampling processes, of the predictor of interest. The AV-optimal design is well known for model-assisted estimators which attain the Godambe-Joshi lower bound for the AV of design-unbiased estimators. However, no optimal probability designs have been found for model-based prediction, except under conditions such that the model-based and model-assisted estimators coincide; these cases can be limiting. This paper shows that the Godambe-Joshi lower bound is an upper bound for the AV of the best linear unbiased estimator of a population total, where the upper bound is over the space of all covariate sets. Therefore model-assisted optimal designs are a sensible choice for model-based prediction when there is uncertainty about the form of the final model, as there often would be prior to conducting the survey. Simulations confirm the result over a range of scenarios, including when the relationship between the target and auxiliary variables is nonlinear and modeled using splines. The AV is lowest relative to the bound when an important design variable is not associated with the target variable.

    Release date: 2020-06-30

  • Articles and reports: 82-003-X201901200003
    Description:

    This article provides a description of the Canadian Census Health and Environment Cohorts (CanCHECs), a population-based linked datasets of the household population at the time of census collection. The CanCHEC datasets are rich national data resources that can be used to measure and examine health inequalities across socioeconomic and ethnocultural dimensions for different periods and locations. These datasets can also be used to examine the effects of exposure to environmental factors on human health.

    Release date: 2019-12-18

  • Articles and reports: 11-633-X2018016
    Description:

    Record linkage has been identified as a potential mechanism to add treatment information to the Canadian Cancer Registry (CCR). The purpose of the Canadian Cancer Treatment Linkage Project (CCTLP) pilot is to add surgical treatment data to the CCR. The Discharge Abstract Database (DAD) and the National Ambulatory Care Reporting System (NACRS) were linked to the CCR, and surgical treatment data were extracted. The project was funded through the Cancer Data Development Initiative (CDDI) of the Canadian Partnership Against Cancer (CPAC).

    The CCTLP was developed as a feasibility study in which patient records from the CCR would be linked to surgical treatment records in the DAD and NACRS databases, maintained by the Canadian Institute for Health Information. The target cohort to whom surgical treatment data would be linked was patients aged 19 or older registered on the CCR (2010 through 2012). The linkage was completed in Statistics Canada’s Social Data Linkage Environment (SDLE).

    Release date: 2018-03-27

  • Articles and reports: 11-633-X2018013
    Description:

    Since 2008, a number of population censuses have been linked to administrative health data and to financial data. These linked datasets have been instrumental in examining health inequalities and have been used in environmental health research. This paper describes the creation of the 1996 Canadian Census Health and Environment Cohort (CanCHEC)—3.57 million respondents to the census long-form questionnaire who were retrospectively followed for mortality and mobility for 16.6 years from 1996 to 2012. The 1996 CanCHEC was limited to census respondents who were aged 19 or older on Census Day (May 14, 1996), were residents of Canada, were not residents of institutions, and had filed an income tax return. These respondents were linked to death records from the Canadian Mortality Database or to the T1 Personal Master File, and to a postal code history from a variety of sources. This is the third in a set of CanCHECs that, when combined, make it possible to examine mortality trends and environmental exposures by socioeconomic characteristics over three census cycles and 21 years of census, tax, and mortality data. This report describes linkage methodologies, validation and bias assessment, and the characteristics of the 1996 CanCHEC. Representativeness of the 1996 CanCHEC relative to the adult population of Canada is also assessed.

    Release date: 2018-01-22

  • Articles and reports: 11-633-X2017005
    Description:

    Hospitalization rates are among commonly reported statistics related to health-care service use. The variety of methods for calculating confidence intervals for these and other health-related rates suggests a need to classify, compare and evaluate these methods. Zeno is a tool developed to calculate confidence intervals of rates based on several formulas available in the literature. This report describes the contents of the main sheet of the Zeno Tool and indicates which formulas are appropriate, based on users’ assumptions and scope of analysis.

    Release date: 2017-01-19

  • Articles and reports: 82-622-X2015009
    Description:

    The Canadian Cancer Registry (CCR) represents a collaborative effort between Statistics Canada and the thirteen provincial and territorial cancer registries to create a single database to report annually on cancer incidence and survival at the national and jurisdictional level. While gains have been made to ensure high quality, standardized, and comparable data, the CCR currently lacks information on cancer treatment. The Canadian Council of Cancer Registries (CCCR) identified the need to capture treatment data at the national level as a key strategic priority for 2013/2014. Record linkage was identified as one possible approach to fill this information gap.

    The purpose of this study is to examine the feasibility of using record linkage to add cancer treatment information for selected cancers: breast, colorectal and prostate. The objectives are twofold: to assess the quality of the linkage processes and the validity of using linked data to estimate cancer treatment rates at the provincial level. The study is based on the Canadian Cancer Registry (2005 to 2008) linked to the Discharge Abstract Database (DAD) and the National Ambulatory Care Reporting System (NACRS) for four provinces (Ontario, Manitoba, Nova Scotia and Prince Edward Island). The linkage was proposed by Statistics Canada, the CCCR and the Canadian Institute for Health Information (CIHI). The linkage was approved and conducted at Statistics Canada.

    Release date: 2015-11-23

  • Articles and reports: 12-001-X201400214110
    Description:

    In developing the sample design for a survey we attempt to produce a good design for the funds available. Information on costs can be used to develop sample designs that minimise the sampling variance of an estimator of total for fixed cost. Improvements in survey management systems mean that it is now sometimes possible to estimate the cost of including each unit in the sample. This paper develops relatively simple approaches to determine whether the potential gains arising from using this unit level cost information are likely to be of practical use. It is shown that the key factor is the coefficient of variation of the costs relative to the coefficient of variation of the relative error on the estimated cost coefficients.

    Release date: 2014-12-19

  • Articles and reports: 12-001-X201400114004
    Description:

    In 2009, two major surveys in the Governments Division of the U.S. Census Bureau were redesigned to reduce sample size, save resources, and improve the precision of the estimates (Cheng, Corcoran, Barth and Hogue 2009). The new design divides each of the traditional state by government-type strata with sufficiently many units into two sub-strata according to each governmental unit’s total payroll, in order to sample less from the sub-stratum with small size units. The model-assisted approach is adopted in estimating population totals. Regression estimators using auxiliary variables are obtained either within each created sub-stratum or within the original stratum by collapsing two sub-strata. A decision-based method was proposed in Cheng, Slud and Hogue (2010), applying a hypothesis test to decide which regression estimator is used within each original stratum. Consistency and asymptotic normality of these model-assisted estimators are established here, under a design-based or model-assisted asymptotic framework. Our asymptotic results also suggest two types of consistent variance estimators, one obtained by substituting unknown quantities in the asymptotic variances and the other by applying the bootstrap. The performance of all the estimators of totals and of their variance estimators are examined in some empirical studies. The U.S. Annual Survey of Public Employment and Payroll (ASPEP) is used to motivate and illustrate our study.

    Release date: 2014-06-27
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Analysis (19)

Analysis (19) (0 to 10 of 19 results)

  • Articles and reports: 12-001-X202300200005
    Description: Population undercoverage is one of the main hurdles faced by statistical analysis with non-probability survey samples. We discuss two typical scenarios of undercoverage, namely, stochastic undercoverage and deterministic undercoverage. We argue that existing estimation methods under the positivity assumption on the propensity scores (i.e., the participation probabilities) can be directly applied to handle the scenario of stochastic undercoverage. We explore strategies for mitigating biases in estimating the mean of the target population under deterministic undercoverage. In particular, we examine a split population approach based on a convex hull formulation, and construct estimators with reduced biases. A doubly robust estimator can be constructed if a followup subsample of the reference probability survey with measurements on the study variable becomes feasible. Performances of six competing estimators are investigated through a simulation study and issues which require further investigation are briefly discussed.
    Release date: 2024-01-03

  • Articles and reports: 11-522-X202100100017
    Description: The outbreak of the COVID-19 pandemic required the Government of Canada to provide relevant and timely information to support decision-making around a host of issues, including personal protective equipment (PPE) procurement and deployment. Our team built a compartmental epidemiological model from an existing code base to project PPE demand under a range of epidemiological scenarios. This model was further enhanced using data science techniques, which allowed for the rapid development and dissemination of model results to inform policy decisions.

    Key Words: COVID-19; SARS-CoV-2; Epidemiological model; Data science; Personal Protective Equipment (PPE); SEIR

    Release date: 2021-10-22

  • Articles and reports: 12-001-X202000100003
    Description:

    Probability sampling designs are sometimes used in conjunction with model-based predictors of finite population quantities. These designs should minimize the anticipated variance (AV), which is the variance over both the superpopulation and sampling processes, of the predictor of interest. The AV-optimal design is well known for model-assisted estimators which attain the Godambe-Joshi lower bound for the AV of design-unbiased estimators. However, no optimal probability designs have been found for model-based prediction, except under conditions such that the model-based and model-assisted estimators coincide; these cases can be limiting. This paper shows that the Godambe-Joshi lower bound is an upper bound for the AV of the best linear unbiased estimator of a population total, where the upper bound is over the space of all covariate sets. Therefore model-assisted optimal designs are a sensible choice for model-based prediction when there is uncertainty about the form of the final model, as there often would be prior to conducting the survey. Simulations confirm the result over a range of scenarios, including when the relationship between the target and auxiliary variables is nonlinear and modeled using splines. The AV is lowest relative to the bound when an important design variable is not associated with the target variable.

    Release date: 2020-06-30

  • Articles and reports: 82-003-X201901200003
    Description:

    This article provides a description of the Canadian Census Health and Environment Cohorts (CanCHECs), a population-based linked datasets of the household population at the time of census collection. The CanCHEC datasets are rich national data resources that can be used to measure and examine health inequalities across socioeconomic and ethnocultural dimensions for different periods and locations. These datasets can also be used to examine the effects of exposure to environmental factors on human health.

    Release date: 2019-12-18

  • Articles and reports: 11-633-X2018016
    Description:

    Record linkage has been identified as a potential mechanism to add treatment information to the Canadian Cancer Registry (CCR). The purpose of the Canadian Cancer Treatment Linkage Project (CCTLP) pilot is to add surgical treatment data to the CCR. The Discharge Abstract Database (DAD) and the National Ambulatory Care Reporting System (NACRS) were linked to the CCR, and surgical treatment data were extracted. The project was funded through the Cancer Data Development Initiative (CDDI) of the Canadian Partnership Against Cancer (CPAC).

    The CCTLP was developed as a feasibility study in which patient records from the CCR would be linked to surgical treatment records in the DAD and NACRS databases, maintained by the Canadian Institute for Health Information. The target cohort to whom surgical treatment data would be linked was patients aged 19 or older registered on the CCR (2010 through 2012). The linkage was completed in Statistics Canada’s Social Data Linkage Environment (SDLE).

    Release date: 2018-03-27

  • Articles and reports: 11-633-X2018013
    Description:

    Since 2008, a number of population censuses have been linked to administrative health data and to financial data. These linked datasets have been instrumental in examining health inequalities and have been used in environmental health research. This paper describes the creation of the 1996 Canadian Census Health and Environment Cohort (CanCHEC)—3.57 million respondents to the census long-form questionnaire who were retrospectively followed for mortality and mobility for 16.6 years from 1996 to 2012. The 1996 CanCHEC was limited to census respondents who were aged 19 or older on Census Day (May 14, 1996), were residents of Canada, were not residents of institutions, and had filed an income tax return. These respondents were linked to death records from the Canadian Mortality Database or to the T1 Personal Master File, and to a postal code history from a variety of sources. This is the third in a set of CanCHECs that, when combined, make it possible to examine mortality trends and environmental exposures by socioeconomic characteristics over three census cycles and 21 years of census, tax, and mortality data. This report describes linkage methodologies, validation and bias assessment, and the characteristics of the 1996 CanCHEC. Representativeness of the 1996 CanCHEC relative to the adult population of Canada is also assessed.

    Release date: 2018-01-22

  • Articles and reports: 11-633-X2017005
    Description:

    Hospitalization rates are among commonly reported statistics related to health-care service use. The variety of methods for calculating confidence intervals for these and other health-related rates suggests a need to classify, compare and evaluate these methods. Zeno is a tool developed to calculate confidence intervals of rates based on several formulas available in the literature. This report describes the contents of the main sheet of the Zeno Tool and indicates which formulas are appropriate, based on users’ assumptions and scope of analysis.

    Release date: 2017-01-19

  • Articles and reports: 82-622-X2015009
    Description:

    The Canadian Cancer Registry (CCR) represents a collaborative effort between Statistics Canada and the thirteen provincial and territorial cancer registries to create a single database to report annually on cancer incidence and survival at the national and jurisdictional level. While gains have been made to ensure high quality, standardized, and comparable data, the CCR currently lacks information on cancer treatment. The Canadian Council of Cancer Registries (CCCR) identified the need to capture treatment data at the national level as a key strategic priority for 2013/2014. Record linkage was identified as one possible approach to fill this information gap.

    The purpose of this study is to examine the feasibility of using record linkage to add cancer treatment information for selected cancers: breast, colorectal and prostate. The objectives are twofold: to assess the quality of the linkage processes and the validity of using linked data to estimate cancer treatment rates at the provincial level. The study is based on the Canadian Cancer Registry (2005 to 2008) linked to the Discharge Abstract Database (DAD) and the National Ambulatory Care Reporting System (NACRS) for four provinces (Ontario, Manitoba, Nova Scotia and Prince Edward Island). The linkage was proposed by Statistics Canada, the CCCR and the Canadian Institute for Health Information (CIHI). The linkage was approved and conducted at Statistics Canada.

    Release date: 2015-11-23

  • Articles and reports: 12-001-X201400214110
    Description:

    In developing the sample design for a survey we attempt to produce a good design for the funds available. Information on costs can be used to develop sample designs that minimise the sampling variance of an estimator of total for fixed cost. Improvements in survey management systems mean that it is now sometimes possible to estimate the cost of including each unit in the sample. This paper develops relatively simple approaches to determine whether the potential gains arising from using this unit level cost information are likely to be of practical use. It is shown that the key factor is the coefficient of variation of the costs relative to the coefficient of variation of the relative error on the estimated cost coefficients.

    Release date: 2014-12-19

  • Articles and reports: 12-001-X201400114004
    Description:

    In 2009, two major surveys in the Governments Division of the U.S. Census Bureau were redesigned to reduce sample size, save resources, and improve the precision of the estimates (Cheng, Corcoran, Barth and Hogue 2009). The new design divides each of the traditional state by government-type strata with sufficiently many units into two sub-strata according to each governmental unit’s total payroll, in order to sample less from the sub-stratum with small size units. The model-assisted approach is adopted in estimating population totals. Regression estimators using auxiliary variables are obtained either within each created sub-stratum or within the original stratum by collapsing two sub-strata. A decision-based method was proposed in Cheng, Slud and Hogue (2010), applying a hypothesis test to decide which regression estimator is used within each original stratum. Consistency and asymptotic normality of these model-assisted estimators are established here, under a design-based or model-assisted asymptotic framework. Our asymptotic results also suggest two types of consistent variance estimators, one obtained by substituting unknown quantities in the asymptotic variances and the other by applying the bootstrap. The performance of all the estimators of totals and of their variance estimators are examined in some empirical studies. The U.S. Annual Survey of Public Employment and Payroll (ASPEP) is used to motivate and illustrate our study.

    Release date: 2014-06-27
Reference (2)

Reference (2) ((2 results))

  • Surveys and statistical programs – Documentation: 82-225-X200701010508
    Description:

    The Record Linkage Overview describes the process used in annual internal record linkage of the Canadian Cancer Registry. The steps include: preparation; pre-processing; record linkage; post-processing; analysis and resolution; resolution entry; and, resolution processing.

    Release date: 2008-01-18

  • Surveys and statistical programs – Documentation: 81-595-M2003005
    Geography: Canada
    Description:

    This paper develops technical procedures that may enable ministries of education to link provincial tests with national and international tests in order to compare standards and report results on a common scale.

    Release date: 2003-05-29
Date modified: