Misclassification in 2 X 2 Tables

Bross, Irwin D. J.

doi:10.2307/3001619

Cited by 374 publications

(240 citation statements)

References 0 publications

Supporting

Mentioning

232

Contrasting

Unclassified

Order By: Relevance

“…In order to distinguish between ingested cocaine and passive environmental exposure to the drug, the hair specimen is washed repeatedly (5 times) prior to analyses. Moreover, if there were any false positive results these would be equally distributed between the two groups and would thus decrease the magnitude of estimates (50). Meconium also provides a cumulative measure of exposure, yet in this study meconium was less sensitive than hair in detecting and quantifying cocaine exposure.…”

Section: Discussionmentioning

confidence: 78%

Prenatal cocaine exposures and dose-related cocaine effects on infant tone and behavior

Chiriboga

Kuhn

Wasserman

2007

Neurotoxicology and Teratology

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 78%

Prenatal cocaine exposures and dose-related cocaine effects on infant tone and behavior

Chiriboga

Kuhn

Wasserman

2007

Neurotoxicology and Teratology

View full text Add to dashboard Cite

show abstract

“…The classical Cox [4,31] partial likelihood score function in the case with no measurement error is given by (5) where Now define (6) (7) (8) (9) (10) where x(w, z) denotes the x vector formed by the subvectors w and z. Our proposed corrected score function is then given by the following obvious analogue of (5): (11) The proposed corrected score estimator is the solution to U* (β)=0, where U* denotes the vector whose components are .…”

Section: The Methodsmentioning

confidence: 99%

“…Correction for misclassification entails estimating the classification probabilities through one of the designs listed in the preceding paragraph. Given appropriate estimates of the classification probabilities, consistent estimates of the relative risk and related inferences can proceed using 'matrix methods' [5,10,11] or maximum likelihood methods [12,13].…”

Section: Introductionmentioning

confidence: 99%

Corrected score estimation in the proportional hazards model with misclassified discrete covariates

Zucker

Spiegelman

2008

Statistics in Medicine

View full text Add to dashboard Cite

SUMMARYWe consider Cox proportional hazards regression when the covariate vector includes error-prone discrete covariates along with error-free covariates, which may be discrete or continuous. The misclassification in the discrete error-prone covariates is allowed to be of any specified form. Building on the work of Nakamura and his colleagues, we present a corrected score method for this setting. The method can handle all three major study designs (internal validation design, external validation design, and replicate measures design), both functional and structural error models, and time-dependent covariates satisfying a certain 'localized error' condition. We derive the asymptotic properties of the method and indicate how to adjust the covariance matrix of the regression coefficient estimates to account for estimation of the misclassification matrix. We present the results of a finite-sample simulation study under Weibull survival with a single binary covariate having known misclassification rates. The performance of the method described here was similar to that of related methods we have examined in previous works. Specifically, our new estimator performed as well as or, in a few cases, better than the full Weibull maximum likelihood estimator. We also present simulation results for our method for the case where the misclassification probabilities are estimated from an external replicate measures study. Our method generally performed well in these simulations. The new estimator has a broader range of applicability than many other estimators proposed in the literature, including those described in our own earlier work, in that it can handle time-dependent covariates with an arbitrary misclassification structure. We illustrate the method on data from a study of the relationship between dietary calcium intake and distal colon cancer.

show abstract

“…While the impact of misclassification on the results of statistical analyses has been studied since the 1950s in the biomedical as well as in the social sciences [9][10][11][12][13][14][15][16], no exact test or confidence interval for the true PR has been proposed. In the next section we describe the proposed procedures, then we present two applications, finally we summarize the properties of the method.…”

Section: Introductionmentioning

confidence: 99%

Exact inference for the risk ratio with an imperfect diagnostic test

Reiczigel

Singer²,

Láng

2016

Epidemiol. Infect.

View full text Add to dashboard Cite

SUMMARYThe risk ratio quantifies the risk of disease in a study population relative to a reference population. Standard methods of estimation and testing assume a perfect diagnostic test having sensitivity and specificity of 100%. However, this assumption typically does not hold, and this may invalidate naive estimation and testing for the risk ratio. We propose procedures that control for sensitivity and specificity of the diagnostic test, given the risks are measured by proportions, as it is in crosssectional studies or studies with fixed follow-up times. These procedures provide an exact unconditional test and confidence interval for the true risk ratio. The methods also cover the case when sensitivity and specificity differ in the two groups (differential misclassification). The resulting test and confidence interval may be useful in epidemiological studies as well as in clinical and vaccine trials. We illustrate the method with real-life examples which demonstrate that ignoring sensitivity and specificity of the diagnostic test may lead to considerable bias in the estimated risk ratio.

show abstract

Misclassification in 2 X 2 Tables

Cited by 374 publications

References 0 publications

Prenatal cocaine exposures and dose-related cocaine effects on infant tone and behavior

Prenatal cocaine exposures and dose-related cocaine effects on infant tone and behavior

Corrected score estimation in the proportional hazards model with misclassified discrete covariates

Exact inference for the risk ratio with an imperfect diagnostic test

Contact Info

Product

Resources

About