Literature DB >> 34636065

Estimating the optimal timing of surgery by imputing potential outcomes.

Xiaofei Chen1,2, Daniel F Heitjan1,2, Gerald Greil3, Haekyung Jeon-Slaughter4.   

Abstract

Hypoplastic left heart syndrome is a congenital anomaly that is uniformly fatal in infancy without immediate treatment. The standard treatment consists of an initial Norwood procedure (stage 1) followed some months later by stage 2 palliation (S2P). The ideal timing of the S2P is uncertain. The Single Ventricle Reconstruction Trial (SVRT) randomized the procedure used in the initial Norwood operation, leaving the timing of the S2P to the discretion of the surgical team. To estimate the causal effect of the timing of S2P, we propose to impute the potential post-S2P survival outcomes using statistical models under the Rubin Causal Model framework. With this approach, it is straightforward to estimate the causal effect of S2P timing on post-S2P survival by directly comparing the imputed potential outcomes. Specifically, we consider a lognormal model and a restricted cubic spline model, evaluating their performance in Monte Carlo studies. When applied to the SVRT data, the models give somewhat different imputed values, but both support the conclusion that the optimal time for the S2P is at 6 months after the Norwood procedure.
© 2021 John Wiley & Sons Ltd.

Entities:  

Keywords:  Bayesian bootstrap; lognormal model; multiple imputation; potential outcome; restricted cubic spline

Mesh:

Year:  2021        PMID: 34636065      PMCID: PMC8671372          DOI: 10.1002/sim.9217

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  15 in total

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2.  Imputation approaches for potential outcomes in causal inference.

Authors:  Daniel Westreich; Jessie K Edwards; Stephen R Cole; Robert W Platt; Sunni L Mumford; Enrique F Schisterman
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3.  Propensity score matching and subclassification in observational studies with multi-level treatments.

Authors:  Shu Yang; Guido W Imbens; Zhanglin Cui; Douglas E Faries; Zbigniew Kadziola
Journal:  Biometrics       Date:  2016-03-17       Impact factor: 2.571

4.  Estimation of the optimal regime in treatment of prostate cancer recurrence from observational data using flexible weighting models.

Authors:  Jincheng Shen; Lu Wang; Jeremy M G Taylor
Journal:  Biometrics       Date:  2016-11-28       Impact factor: 2.571

5.  Discussion of PENCOMP.

Authors:  Joseph Antonelli; Michael J Daniels
Journal:  J Am Stat Assoc       Date:  2019-04-19       Impact factor: 5.033

6.  Bootstrap inference when using multiple imputation.

Authors:  Michael Schomaker; Christian Heumann
Journal:  Stat Med       Date:  2018-04-16       Impact factor: 2.373

Review 7.  Applications of multiple imputation in medical studies: from AIDS to NHANES.

Authors:  J Barnard; X L Meng
Journal:  Stat Methods Med Res       Date:  1999-03       Impact factor: 3.021

8.  Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls.

Authors:  Jonathan A C Sterne; Ian R White; John B Carlin; Michael Spratt; Patrick Royston; Michael G Kenward; Angela M Wood; James R Carpenter
Journal:  BMJ       Date:  2009-06-29

9.  Modeling the causal effect of treatment initiation time on survival: Application to HIV/TB co-infection.

Authors:  Liangyuan Hu; Joseph W Hogan; Ann W Mwangi; Abraham Siika
Journal:  Biometrics       Date:  2017-09-28       Impact factor: 2.571

Review 10.  Hypoplastic left heart syndrome: from comfort care to long-term survival.

Authors:  Mouhammad Yabrodi; Christopher W Mastropietro
Journal:  Pediatr Res       Date:  2016-10-04       Impact factor: 3.756

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