Fitting regression models with response‐biased samples

Scott, Alastair; Wild, C. J.

doi:10.1002/cjs.10114

Cited by 33 publications

(44 citation statements)

References 30 publications

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“…Scott and Wild [35] recently suggested estimating the sampling probabilities π that occur in the second term of the PL equations (2) using a parametric model π ( y , x;α ) = Pr( R = 1 |y , x;α ), as has just been illustrated for WL. Denote the PL equations with estimated π by S 0 ( β , α ) and let…”

Section: Accounting For More Of the Phase I Informationmentioning

confidence: 99%

“…Scott and Wild [35] also suggested that additional information about α might be obtained from the PL equations with estimated weights by solving the system…”

Section: Accounting For More Of the Phase I Informationmentioning

confidence: 99%

“…[35] This function also implements WL with estimated weights. When applied with weights estimated using a saturated model for the combination of case-control indicator, trial, prevalent CVD and decade of age (Table 1), it effectively replicated the results shown in Table 2, with slight differences in the SEs and none in the coefficients.…”

Section: Accounting For More Of the Phase I Informationmentioning

confidence: 99%

“…These ideas were further developed by Scott and Wild [35] for three phase sampling designs. Suppose that at Phase I the outcome y and certain components of the covariate vector, say x (1) , are observed for all subjects.…”

Section: Accounting For More Of the Phase I Informationmentioning

confidence: 99%

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Using the Whole Cohort in the Analysis of Case-Control Data

et al. 2013

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Standard analyses of data from case-control studies that are nested in a large cohort ignore information available for cohort members not sampled for the sub-study. This paper reviews several methods designed to increase estimation efficiency by using more of the data, treating the case-control sample as a two or three phase stratified sample. When applied to a study of coronary heart disease among women in the hormone trials of the Women’s Health Initiative, modest but increasing gains in precision of regression coefficients were observed depending on the amount of cohort information used in the analysis. The gains were particularly evident for pseudo- or maximum likelihood estimates whose validity depends on the assumed model being correct. Larger standard errors were obtained for coefficients estimated by inverse probability weighted methods that are more robust to model misspecification. Such misspecification may have been responsible for an important difference in one key regression coefficient estimated using the weighted compared with the more efficient methods.

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Section: Accounting For More Of the Phase I Informationmentioning

confidence: 99%

“…Scott and Wild [35] also suggested that additional information about α might be obtained from the PL equations with estimated weights by solving the system…”

Section: Accounting For More Of the Phase I Informationmentioning

confidence: 99%

Section: Accounting For More Of the Phase I Informationmentioning

confidence: 99%

Section: Accounting For More Of the Phase I Informationmentioning

confidence: 99%

See 2 more Smart Citations

Using the Whole Cohort in the Analysis of Case-Control Data

et al. 2013

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“…Regression analysis under informative sampling is, in particular, an important topic in this area. See Chambers (2003), Pfeffermann and Sverchkov (2009), Scott and Wild (2011), Kim and Skinner (2013), and references therein.…”

Section: Introductionmentioning

confidence: 99%

A semiparametric inference to regression analysis with missing covariates in survey data

Yang¹,

Kim²

2017

STAT SINICA

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Parameter estimation in parametric regression models with missing covariates is considered under a survey sampling setup. Under missingness at random, a semiparametric maximum likelihood approach is proposed which requires no parametric specification of the marginal covariate distribution. By drawing from the von Mises calculus and V-Statistics theory, we obtain an asymptotic linear representation of the semiparametric maximum likelihood estimator (SMLE) of the regression parameters, which allows for a consistent estimator of asymptotic variance. An EM algorithm for computation is then developed to implement the proposed method using fractional imputation. Simulation results suggest that the SMLE method is robust, whereas the fully parametric method is subject to severe bias under model misspecification. A rangeland study from the National Resources Inventory (NRI) is used to illustrate the practical use of the proposed methodology.

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Weighting in the regression analysis of survey data with a cross‐national application

Skinner

Mason

2012

Can J Statistics

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A class of survey weighting methods provides consistent estimation of regression coefficients under unequal probability sampling. The minimization of the variance of the estimated coefficients within this class is considered. A series of approximations leads to a simple modification of the usual design weight. One type of application where unequal probabilities of selection arise is in cross-national comparative surveys. In this case, our argument suggests the use of a certain kind of within-country weight. We investigate this idea in an application to data from the European Social Survey, where we fit a logistic regression model with vote in an election as the dependent variable and with various variables of political science interest included as explanatory variables. We show that the use of the modified weights leads to a considerable reduction in standard errors compared to design weighting.

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Fitting regression models with response‐biased samples

Cited by 33 publications

References 30 publications

Using the Whole Cohort in the Analysis of Case-Control Data

Using the Whole Cohort in the Analysis of Case-Control Data

A semiparametric inference to regression analysis with missing covariates in survey data

Weighting in the regression analysis of survey data with a cross‐national application

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