scholarly journals Sensitivity analysis of treatment effect to unmeasured confounding in observational studies with survival and competing risks outcomes

2020 ◽  
Vol 39 (24) ◽  
pp. 3397-3411 ◽  
Author(s):  
Rong Huang ◽  
Ronghui Xu ◽  
Parambir S. Dulai
Keyword(s):  
Sensitivity Analysis ◽  
Competing Risks ◽  
Treatment Effect ◽  
Observational Studies ◽  
Unmeasured Confounding

scholarly journals How unmeasured confounding in a competing risks setting can affect treatment effect estimates in observational studies

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Michael Andrew Barrowman ◽  
Niels Peek ◽  
Mark Lambie ◽  
Glen Philip Martin ◽  
Matthew Sperrin
Keyword(s):  
Competing Risks ◽  
Treatment Effect ◽  
Observational Studies ◽  
Unmeasured Confounding

Sensitivity Analysis for Unmeasured Confounding: E-Values for Observational Studies

2017 ◽  
Vol 167 (4) ◽  
pp. 285 ◽  
Author(s):  
A. Russell Localio ◽  
Catherine B. Stack ◽  
Michael E. Griswold
Keyword(s):  
Sensitivity Analysis ◽  
Observational Studies ◽  
Unmeasured Confounding

scholarly journals A note on a sensitivity analysis for unmeasured confounding, and the related E-value

2020 ◽  
Vol 8 (1) ◽  
pp. 229-248
Author(s):  
Arvid Sjölander
Keyword(s):  
Sensitivity Analysis ◽  
Observational Studies ◽  
Real Data ◽  
Feasible Region ◽  
Maximal Strength ◽  
Unmeasured Confounding ◽  
Unmeasured Confounder ◽  
Two Parameters ◽  
Strength Of Association

Abstract Unmeasured confounding is one of the most important threats to the validity of observational studies. In this paper we scrutinize a recently proposed sensitivity analysis for unmeasured confounding. The analysis requires specification of two parameters, loosely defined as the maximal strength of association that an unmeasured confounder may have with the exposure and with the outcome, respectively. The E-value is defined as the strength of association that the confounder must have with the exposure and the outcome, to fully explain away an observed exposure-outcome association. We derive the feasible region of the sensitivity analysis parameters, and we show that the bounds produced by the sensitivity analysis are not always sharp. We finally establish a region in which the bounds are guaranteed to be sharp, and we discuss the implications of this sharp region for the interpretation of the E-value. We illustrate the theory with a real data example and a simulation.

scholarly journals Evaluating costs with unmeasured confounding: A sensitivity analysis for the treatment effect

2013 ◽  
Vol 7 (4) ◽  
pp. 2062-2080 ◽  
Author(s):  
Elizabeth A. Handorf ◽  
Justin E. Bekelman ◽  
Daniel F. Heitjan ◽  
Nandita Mitra
Keyword(s):  
Sensitivity Analysis ◽  
Treatment Effect ◽  
Unmeasured Confounding

Unifying instrumental variable and inverse probability weighting approaches for inference of causal treatment effect and unmeasured confounding in observational studies

2020 ◽  
pp. 096228022097183
Author(s):  
Tao Liu ◽  
Joseph W Hogan
Keyword(s):  
Treatment Effect ◽  
Instrumental Variable ◽  
Observational Studies ◽  
Treatment Effects ◽  
Inverse Probability Weighting ◽  
Probability Weighting ◽  
Unmeasured Confounding ◽  
Inverse Probability ◽  
Causal Treatment ◽  
Weighting Methods

Confounding is a major concern when using data from observational studies to infer the causal effect of a treatment. Instrumental variables, when available, have been used to construct bound estimates on population average treatment effects when outcomes are binary and unmeasured confounding exists. With continuous outcomes, meaningful bounds are more challenging to obtain because the domain of the outcome is unrestricted. In this paper, we propose to unify the instrumental variable and inverse probability weighting methods, together with suitable assumptions in the context of an observational study, to construct meaningful bounds on causal treatment effects. The contextual assumptions are imposed in terms of the potential outcomes that are partially identified by data. The inverse probability weighting component incorporates a sensitivity parameter to encode the effect of unmeasured confounding. The instrumental variable and inverse probability weighting methods are unified using the principal stratification. By solving the resulting system of estimating equations, we are able to quantify both the causal treatment effect and the sensitivity parameter (i.e. the degree of the unmeasured confounding). We demonstrate our method by analyzing data from the HIV Epidemiology Research Study.

Bayesian sensitivity analysis for unmeasured confounding in observational studies

2007 ◽  
Vol 26 (11) ◽  
pp. 2331-2347 ◽  
Author(s):  
Lawrence C. McCandless ◽  
Paul Gustafson ◽  
Adrian Levy
Keyword(s):  
Sensitivity Analysis ◽  
Observational Studies ◽  
Unmeasured Confounding

scholarly journals Assessing Treatment Effect Variation in Observational Studies: Results from a Data Challenge

2019 ◽  
Vol 5 (2) ◽  
pp. 21-35
Author(s):  
Carlos Carvalho ◽  
Avi Feller ◽  
Jared Murray ◽  
Spencer Woody ◽  
David Yeager
Keyword(s):  
Treatment Effect ◽  
Observational Studies
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