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Super learning to hedge against incorrect inference from arbitrary parametric assumptions in marginal structural modeling.

Abstract：

OBJECTIVE:Clinical trials are unlikely to ever be launched for many comparative effectiveness research (CER) questions. Inferences from hypothetical randomized trials may however be emulated with marginal structural modeling (MSM) using observational data, but success in adjusting for time-dependent confounding and selection bias typically relies on parametric modeling assumptions. If these assumptions are violated, inferences from MSM may be inaccurate. In this article, we motivate the application of a data-adaptive estimation approach called super learning (SL) to avoid reliance on arbitrary parametric assumptions in CER. STUDY DESIGN AND SETTING:Using the electronic health records data from adults with new-onset type 2 diabetes, we implemented MSM with inverse probability weighting (IPW) estimation to evaluate the effect of three oral antidiabetic therapies on the worsening of glomerular filtration rate. RESULTS:Inferences from IPW estimation were noticeably sensitive to the parametric assumptions about the associations between both the exposure and censoring processes and the main suspected source of confounding, that is, time-dependent measurements of hemoglobin A1c. SL was successfully implemented to harness flexible confounding and selection bias adjustment from existing machine learning algorithms. CONCLUSION:Erroneous IPW inference about clinical effectiveness because of arbitrary and incorrect modeling decisions may be avoided with SL.

journal_name

J Clin Epidemiol

journal_title

Journal of clinical epidemiology

authors

Neugebauer R,Fireman B,Roy JA,Raebel MA,Nichols GA,O'Connor PJ

doi

10.1016/j.jclinepi.2013.01.016

subject

Has Abstract

pub_date

2013-08-01 00:00:00

pages

S99-109

issue

8 Suppl

eissn

0895-4356

issn

1878-5921

pii

S0895-4356(13)00168-6

journal_volume

66

pub_type

杂志文章

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