Domain estimation under informative linkage

Chambers, Ray; Salvati, Nicola; Fabrizi, Enrico; Silva, Andrea Diniz da

doi:10.1080/24754269.2019.1653158

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…We assume that the linkage error is non‐informative of the regression model, that is, may depend on the errors in the matching process, but not on the model covariates nor on the survival time 28 . This is the key assumption of most secondary analysis approaches in the literature, for which Zhang and Tuoto 23 have proposed a diagnostic test.…”

Section: Cox Regression Analysis With Linked Datamentioning

confidence: 99%

“…We assume that the linkage error is non-informative of the regression model, that is, may depend on the errors in the matching process, but not on the model covariates nor on the survival time. 28 This is the key assumption of most secondary analysis approaches in the literature, for which Zhang and Tuoto 23 have proposed a diagnostic test. Adopting the modelling approach of Copas and Hilton, 29 we suppose that both databases are partitioned into blocks A v and B v , v = 1, … , V, and that the record linkage is performed independently in these blocks.…”

Section: Linkage Error Modelmentioning

confidence: 99%

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Cox regression with linked data

Vo,

Garès,

Zhang

et al. 2023

Statistics in Medicine

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Record linkage is increasingly used, especially in medical studies, to combine data from different databases that refer to the same entities. The linked data can bring analysts novel and valuable knowledge that is impossible to obtain from a single database. However, linkage errors are usually unavoidable, regardless of record linkage methods, and ignoring these errors may lead to biased estimates. While different methods have been developed to deal with the linkage errors in the generalized linear model, there is not much interest on Cox regression model, although this is one of the most important statistical models in clinical and epidemiological research. In this work, we propose an adjusted estimating equation for secondary Cox regression analysis, where linked data have been prepared by a third‐party operator, and no information on matching variables is available to the analyst. Through a Monte Carlo simulation study, the proposed method is shown to lead to substantial bias reductions in the estimation of the parameters of the Cox model caused by false links. An asymptotically unbiased variance estimator for the adjusted estimators of Cox regression coefficients is also proposed. Finally, the proposed method is applied to a linked database from the Brest stroke registry in France.

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Section: Cox Regression Analysis With Linked Datamentioning

confidence: 99%