On Instrumental Variables Estimation of Causal Odds Ratios

By Stijn Vansteelandt, J. Bowden, M. Babanezhad, Els Goetghebeur. 2011

125 citations recorded by the source corpus.

Inference for causal effects can benefit from the availability of an instrumental variable (IV) which, by definition, is associated with the given exposure, but not with the outcome of interest other than through a causal exposure effect. Estimation methods for instrumental variables are now well established for continuous outcomes, but much less so for dichotomous outcomes. In this article we review IV estimation of so-called conditional causal odds ratios which express the effect of an arbitrary exposure on a dichotomous outcome conditional on the exposure level, instrumental variable and measured covariates. In addition, we propose IV estimators of so-called marginal causal odds ratios which express the effect of an arbitrary exposure on a dichotomous outcome at the population level, and are therefore of greater public health relevance. We explore interconnections between the different estimators and support the results with extensive simulation studies and three applications.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9fdab50aba8fab97709f8b83330b35b61cc85205

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Citations in the corpus

Structural nested models and G-estimation: the partially realized promise (2014, methodology): “SMMs for natural direct effects have been considered van der Laan and Petersen (2008) and Tchetgen Tchetgen and Shpitser (2011).”
Instrumental Variables: An Econometrician's Perspective (2014, background): “d Rubin (1996), Robins and Greenland (1996), Balke and Pearl (1995, 1997), Greenland (2000), Hernán and Robins (2006), Robins (1994), Robins and Rotnitzky (2004), Vansteelandt and Goetghebeur (2003), Vansteelandt et al. (2011), Hirano et al. (2000), Tan (2006, 2010), Abadie (2002, 2003), Duﬂo, Glenn”
A framework for the investigation of pleiotropy in two‐sample summary data Mendelian randomization (2017, background): “In this case, when a causal effect between the exposure and outcome exists, ratio estimates of causal effect gleaned from individual variants will be attenuated towards the null by varying amounts due to the non-collapsibility of the odds-ratio [34].”
Tutorial in Biostatistics: Instrumental Variable Methods for Causal Inference* (2014, methodology): “For further information on approach (ii), see [48, 49].”
Meta‐analysis and Mendelian randomization: A review (2019, background): “This is due to the noncollapsibility of the odds ratio.(4) However, because genetic effects generally explain a very small amount of variation in the exposure, this means that the range of genetically predicted exposure levels is very narrow around the center of the distribution of X.”
A review of instrumental variable estimators for Mendelian randomization (2015, methodology): “Despite the consequences of non-collapsibility, the two-stage estimator with a logistic second-stage model still provides a valid test of the null hypothesis.(17)”
Evaluating the potential role of pleiotropy in Mendelian randomization studies (2018, background): “Heterogeneity in this case is due to the non-collapsibility of the odds ratio as a summary measure, meaning that each SNP is estimating a slightly different causal parameter (52); • The samples used to estimate the SNP-exposure and SNPoutcome associations are not homogeneous e.”
Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption (2018, background): “For example, even if all SNPs are valid IVs, causal effect heterogeneity will exist whenever the SNPoutcome association is an odds ratio, due to issues of non-collapsibility [4].”
Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score (2018, background): “[52] who also discussed the general intepretation of causal odds ratios.”
Multivariable Mendelian Randomization and Mediation. (2020, methodology): “When the exposure is binary MR analyses can be used to test causal hypotheses, however the effect estimates obtained are not reliable estimates of the size of the causal effect (Angrist et al. 1996; Palmer et al. 2011; Vansteelandt et al. 2011; Clarke and Windmeijer 2012).”