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• 1. Modeling the burden of cancer in Canada Archived

  Articles and reports: 82-005-X20020016479

  Geography: Canada

  Description:

  The Population Health Model (POHEM) is a policy analysis tool that helps answer "what-if" questions about the health and economic burden of specific diseases and the cost-effectiveness of administering new diagnostic and therapeutic interventions. This simulation model is particularly pertinent in an era of fiscal restraint, when new therapies are generally expensive and difficult policy decisions are being made. More important, it provides a base for a broader framework to inform policy decisions using comprehensive disease data and risk factors. Our "base case" models comprehensively estimate the lifetime costs of treating breast, lung and colorectal cancer in Canada. Our cancer models have shown the large financial burden of diagnostic work-up and initial therapy, as well as the high costs of hospitalizing those dying of cancer. Our core cancer models (lung, breast and colorectal cancer) have been used to evaluate the impact of new practice patterns. We have used these models to evaluate new chemotherapy regimens as therapeutic options for advanced lung cancer; the health and financial impact of reducing the hospital length of stay for initial breast cancer surgery; and the potential impact of population-based screening for colorectal cancer. To date, the most interesting intervention we have studied has been the use of tamoxifen to prevent breast cancer among high risk women.

  Release date: 2002-10-08

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• 2. Variance estimation for the general regression estimator Archived

  Articles and reports: 12-001-X20020016424

  Description:

  A variety of estimators for the variance of the General Regression (GREG) estimator of a mean have been proposed in the sampling literature, mainly with the goal of estimating the design-based variance. Under certain conditions, estimators can be easily constructed that are approximately unbiased for both the design-variance and the model-variance. Several dual-purpose estimators are studied here in single-stage sampling. These choices are robust estimators of a model-variance even if the model that motivates the GREG has an incorrect variance parameter.

  A key feature of the robust estimators is the adjustment of squared residuals by factors analogous to the leverages used in standard regression analysis. We also show that the delete-one jackknife estimator implicitly includes the leverage adjustments and is a good choice from either the design-based or model-based perspective. In a set of simulations, these variance estimators have small bias and produce confidence intervals with near-nominal coverage rates for several sampling methods, sample sizes and populations in single-stage sampling.

  We also present simulation results for a skewed population where all variance estimators perform poorly. Samples that do not adequately represent the units with large values lead to estimated means that are too small, variance estimates that are too small and confidence intervals that cover at far less than the nominal rate. These defects can be avoided at the design stage by selecting samples that cover the extreme units well. However, in populations with inadequate design information this will not be feasible.

  Release date: 2002-07-05

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• 1. Modeling the burden of cancer in Canada Archived

  Articles and reports: 82-005-X20020016479

  Geography: Canada

  Description:

  The Population Health Model (POHEM) is a policy analysis tool that helps answer "what-if" questions about the health and economic burden of specific diseases and the cost-effectiveness of administering new diagnostic and therapeutic interventions. This simulation model is particularly pertinent in an era of fiscal restraint, when new therapies are generally expensive and difficult policy decisions are being made. More important, it provides a base for a broader framework to inform policy decisions using comprehensive disease data and risk factors. Our "base case" models comprehensively estimate the lifetime costs of treating breast, lung and colorectal cancer in Canada. Our cancer models have shown the large financial burden of diagnostic work-up and initial therapy, as well as the high costs of hospitalizing those dying of cancer. Our core cancer models (lung, breast and colorectal cancer) have been used to evaluate the impact of new practice patterns. We have used these models to evaluate new chemotherapy regimens as therapeutic options for advanced lung cancer; the health and financial impact of reducing the hospital length of stay for initial breast cancer surgery; and the potential impact of population-based screening for colorectal cancer. To date, the most interesting intervention we have studied has been the use of tamoxifen to prevent breast cancer among high risk women.

  Release date: 2002-10-08

  ---

• 2. Variance estimation for the general regression estimator Archived

  Articles and reports: 12-001-X20020016424

  Description:

  A variety of estimators for the variance of the General Regression (GREG) estimator of a mean have been proposed in the sampling literature, mainly with the goal of estimating the design-based variance. Under certain conditions, estimators can be easily constructed that are approximately unbiased for both the design-variance and the model-variance. Several dual-purpose estimators are studied here in single-stage sampling. These choices are robust estimators of a model-variance even if the model that motivates the GREG has an incorrect variance parameter.

  A key feature of the robust estimators is the adjustment of squared residuals by factors analogous to the leverages used in standard regression analysis. We also show that the delete-one jackknife estimator implicitly includes the leverage adjustments and is a good choice from either the design-based or model-based perspective. In a set of simulations, these variance estimators have small bias and produce confidence intervals with near-nominal coverage rates for several sampling methods, sample sizes and populations in single-stage sampling.

  We also present simulation results for a skewed population where all variance estimators perform poorly. Samples that do not adequately represent the units with large values lead to estimated means that are too small, variance estimates that are too small and confidence intervals that cover at far less than the nominal rate. These defects can be avoided at the design stage by selecting samples that cover the extreme units well. However, in populations with inadequate design information this will not be feasible.

  Release date: 2002-07-05

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