Regression calibration with more surrogates than mismeasured variables
Type
ArticleKAUST Grant Number
KUS-CI-016-04Date
2012-06-29Online Publication Date
2012-06-29Print Publication Date
2012-10-15Permanent link to this record
http://hdl.handle.net/10754/599485
Metadata
Show full item recordAbstract
In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.Citation
Kipnis V, Midthune D, Freedman LS, Carroll RJ (2012) Regression calibration with more surrogates than mismeasured variables. Statistics in Medicine 31: 2713–2732. Available: http://dx.doi.org/10.1002/sim.5435.Sponsors
We thank Dr. Weller for providing us with the data used in their original analysis. Carroll's research was supported by a grant from the National Cancer Institute (CA57030) and by Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST).Publisher
WileyJournal
Statistics in MedicineDOI
10.1002/sim.5435PubMed ID
22744878PubMed Central ID
PMC3640838ae974a485f413a2113503eed53cd6c53
10.1002/sim.5435
Scopus Count
Collections
Publications Acknowledging KAUST SupportRelated articles
- Measurement error in the explanatory variable of a binary regression: regression calibration and integrated conditional likelihood in studies of residential radon and lung cancer.
- Authors: Fearn T, Hill DC, Darby SC
- Issue date: 2008 May 30
- A new method for dealing with measurement error in explanatory variables of regression models.
- Authors: Freedman LS, Fainberg V, Kipnis V, Midthune D, Carroll RJ
- Issue date: 2004 Mar
- A conditional likelihood approach for regression analysis using biomarkers measured with batch-specific error.
- Authors: Wang M, Flanders WD, Bostick RM, Long Q
- Issue date: 2012 Dec 20
- Robust best linear estimator for Cox regression with instrumental variables in whole cohort and surrogates with additive measurement error in calibration sample.
- Authors: Wang CY, Song X
- Issue date: 2016 Nov
- A simulation study of measurement error correction methods in logistic regression.
- Authors: Thoresen M, Laake P
- Issue date: 2000 Sep