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- Articles and reports: 11-522-X20020016732Description:
Analysis of dose-response relationships has long been important in toxicology. More recently, this type of analysis has been employed to evaluate public education campaigns. The data that are collected in such evaluations are likely to come from standard household survey designs with all the usual complexities of multiple stages, stratification and variable selection probabilities. On a recent evaluation, a system was developed with the following features: categorization of doses into three or four levels, propensity scoring of dose selection and a new jack-knifed Jonckheere-Terpstra test for a monotone dose-response relationship. This system allows rapid production of tests for monotone dose-response relationships that are corrected both for sample design and for confounding. The focus of this paper will be the results of a Monte-Carlo simulation of the properties of the jack-knifed Jonckheere-Terpstra.
Moreover, there is no experimental control over dosages and the possibility of confounding variables must be considered. Standard regressions in WESVAR and SUDAAN could be used to determine if there is a linear dose-response relationship while controlling on confounders, but such an approach obviously has low power to detect nonlinear but monotone dose-response relationships and is time-consuming to implement if there are a large number of possible outcomes of interest.
Release date: 2004-09-13 - Articles and reports: 11-522-X20020016734Description:
According to recent literature, the calibration method has gained much popularity on survey sampling and calibration estimators are routinely computed by many survey organizations. The choice of calibration variables for all existing approaches, however, remains ad hoc. In this article, we show that the model-calibration estimator for the finite population mean, which was proposed by Wu and Sitter (2001) through an intuitive argument, is indeed optimal among a class of calibration estimators. We further present optimal calibration estimators for the finite population distribution function, the population variance, variance of a linear estimator and other quadratic finite population functions under a unified framework. A limited simulation study shows that the improvement of these optimal estimators over the conventional ones can be substantial. The question of when and how auxiliary information can be used for both the estimation of the population mean using a generalized regression estimator and the estimation of its variance through calibration is addressed clearly under the proposed general methodology. Constructions of proposed estimators under two-phase sampling and some fundamental issues in using auxiliary information from survey data are also addressed under the context of optimal estimation.
Release date: 2004-09-13 - 3. Accuracy estimation with clustered dataset ArchivedArticles and reports: 11-522-X20020016737Description:
If the dataset available to machine learning results from cluster sampling (e.g., patients from a sample of hospital wards), the usual cross-validation error rate estimate can lead to biased and misleading results. In this technical paper, an adapted cross-validation is described for this case. Using a simulation, the sampling distribution of the generalization error rate estimate, under cluster or simple random sampling hypothesis, is compared with the true value. The results highlight the impact of the sampling design on inference: clearly, clustering has a significant impact; the repartition between learning set and test set should result from a random partition of the clusters, not from a random partition of the examples. With cluster sampling, standard cross-validation underestimates the generalization error rate, and is deficient for model selection. These results are illustrated with a real application of automatic identification of spoken language.
Release date: 2004-09-13 - Articles and reports: 11-522-X20020016745Description:
The attractiveness of the Regression Discontinuity Design (RDD) rests on its close similarity to a normal experimental design. On the other hand, it is of limited applicability since it is not often the case that units are assigned to the treatment group on the basis of an observable (to the analyst) pre-program measure. Besides, it only allows identification of the mean impact on a very specific subpopulation. In this technical paper, we show that the RDD straightforwardly generalizes to the instances in which the units' eligibility is established on an observable pre-program measure with eligible units allowed to freely self-select into the program. This set-up also proves to be very convenient for building a specification test on conventional non-experimental estimators of the program mean impact. The data requirements are clearly described.
Release date: 2004-09-13 - 5. Health state preference scores for Canadians ArchivedArticles and reports: 82-005-X20040017000Geography: CanadaDescription:
This newsletter article describes the process and preliminary results of eliciting scores from small groups of lay Canadians about their relative preference for a health state compared with full health. Health preference measurement helps assess the relative impact of diseases on health-related quality of life. This is part of the Population Health Impact of Disease in Canada (PHI) research program.
Release date: 2004-08-05
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- Articles and reports: 11-522-X20020016732Description:
Analysis of dose-response relationships has long been important in toxicology. More recently, this type of analysis has been employed to evaluate public education campaigns. The data that are collected in such evaluations are likely to come from standard household survey designs with all the usual complexities of multiple stages, stratification and variable selection probabilities. On a recent evaluation, a system was developed with the following features: categorization of doses into three or four levels, propensity scoring of dose selection and a new jack-knifed Jonckheere-Terpstra test for a monotone dose-response relationship. This system allows rapid production of tests for monotone dose-response relationships that are corrected both for sample design and for confounding. The focus of this paper will be the results of a Monte-Carlo simulation of the properties of the jack-knifed Jonckheere-Terpstra.
Moreover, there is no experimental control over dosages and the possibility of confounding variables must be considered. Standard regressions in WESVAR and SUDAAN could be used to determine if there is a linear dose-response relationship while controlling on confounders, but such an approach obviously has low power to detect nonlinear but monotone dose-response relationships and is time-consuming to implement if there are a large number of possible outcomes of interest.
Release date: 2004-09-13 - Articles and reports: 11-522-X20020016734Description:
According to recent literature, the calibration method has gained much popularity on survey sampling and calibration estimators are routinely computed by many survey organizations. The choice of calibration variables for all existing approaches, however, remains ad hoc. In this article, we show that the model-calibration estimator for the finite population mean, which was proposed by Wu and Sitter (2001) through an intuitive argument, is indeed optimal among a class of calibration estimators. We further present optimal calibration estimators for the finite population distribution function, the population variance, variance of a linear estimator and other quadratic finite population functions under a unified framework. A limited simulation study shows that the improvement of these optimal estimators over the conventional ones can be substantial. The question of when and how auxiliary information can be used for both the estimation of the population mean using a generalized regression estimator and the estimation of its variance through calibration is addressed clearly under the proposed general methodology. Constructions of proposed estimators under two-phase sampling and some fundamental issues in using auxiliary information from survey data are also addressed under the context of optimal estimation.
Release date: 2004-09-13 - 3. Accuracy estimation with clustered dataset ArchivedArticles and reports: 11-522-X20020016737Description:
If the dataset available to machine learning results from cluster sampling (e.g., patients from a sample of hospital wards), the usual cross-validation error rate estimate can lead to biased and misleading results. In this technical paper, an adapted cross-validation is described for this case. Using a simulation, the sampling distribution of the generalization error rate estimate, under cluster or simple random sampling hypothesis, is compared with the true value. The results highlight the impact of the sampling design on inference: clearly, clustering has a significant impact; the repartition between learning set and test set should result from a random partition of the clusters, not from a random partition of the examples. With cluster sampling, standard cross-validation underestimates the generalization error rate, and is deficient for model selection. These results are illustrated with a real application of automatic identification of spoken language.
Release date: 2004-09-13 - Articles and reports: 11-522-X20020016745Description:
The attractiveness of the Regression Discontinuity Design (RDD) rests on its close similarity to a normal experimental design. On the other hand, it is of limited applicability since it is not often the case that units are assigned to the treatment group on the basis of an observable (to the analyst) pre-program measure. Besides, it only allows identification of the mean impact on a very specific subpopulation. In this technical paper, we show that the RDD straightforwardly generalizes to the instances in which the units' eligibility is established on an observable pre-program measure with eligible units allowed to freely self-select into the program. This set-up also proves to be very convenient for building a specification test on conventional non-experimental estimators of the program mean impact. The data requirements are clearly described.
Release date: 2004-09-13 - 5. Health state preference scores for Canadians ArchivedArticles and reports: 82-005-X20040017000Geography: CanadaDescription:
This newsletter article describes the process and preliminary results of eliciting scores from small groups of lay Canadians about their relative preference for a health state compared with full health. Health preference measurement helps assess the relative impact of diseases on health-related quality of life. This is part of the Population Health Impact of Disease in Canada (PHI) research program.
Release date: 2004-08-05
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