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Erschienen in: Journal of Cancer Research and Clinical Oncology 6/2019

24.04.2019 | Original Article – Cancer Research

Different statistical techniques dealing with confounding in observational research: measuring the effect of breast-conserving therapy and mastectomy on survival

verfasst von: Marissa C. van Maaren, Saskia le Cessie, Luc J. A. Strobbe, Catharina G. M. Groothuis-Oudshoorn, Philip M. P. Poortmans, Sabine Siesling

Erschienen in: Journal of Cancer Research and Clinical Oncology | Ausgabe 6/2019

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Abstract

Purpose

Propensity trimming, hierarchical modelling and instrumental variable (IV) analysis are statistical techniques dealing with confounding, cluster-related variation or confounding by severity. This study aimed to explain (dis)advantages of these techniques in estimating the effect of breast-conserving therapy (BCT) and mastectomy on 10-year distant metastasis-free survival (DMFS).

Methods

All women diagnosed in 2005 with primary T1-2N0-1 breast cancer treated with BCT or mastectomy were selected from the Netherlands Cancer Registry. We used multivariable Cox regression to correct for confounding. Propensity trimming was used to create a more homogeneous population for which the treatment choice was not self-evident. Hospital of surgery was used as hierarchical level to handle hospital-related variation, and as IV to deal with unmeasured confounding.

Results

Multivariable Cox regression showed higher 10-year DMFS for BCT than mastectomy [HR 0.70 (95% CI 0.60–82)]. Propensity trimming on the 10–90th and the 20–80th percentile of the propensity score distribution and hierarchical modelling showed similar HRs. IV analysis showed no significant difference between BCT and mastectomy.

Conclusion

Unmeasured confounding is very difficult to eliminate in observational research. We cannot conclude that BCT or mastectomy has a causal relationship with 10-year DMFS. It is crucial to critically evaluate all model’s assumptions, and to be careful in drawing firm conclusions.
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Metadaten
Titel
Different statistical techniques dealing with confounding in observational research: measuring the effect of breast-conserving therapy and mastectomy on survival
verfasst von
Marissa C. van Maaren
Saskia le Cessie
Luc J. A. Strobbe
Catharina G. M. Groothuis-Oudshoorn
Philip M. P. Poortmans
Sabine Siesling
Publikationsdatum
24.04.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal of Cancer Research and Clinical Oncology / Ausgabe 6/2019
Print ISSN: 0171-5216
Elektronische ISSN: 1432-1335
DOI
https://doi.org/10.1007/s00432-019-02919-x

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