Journal ArticleDOI
Causal inference with generalized structural mean models
TLDR
In this paper, a generalized structural mean model is proposed to estimate cause-effect relationships in empirical research where exposures are not completely controlled, as in observational studies or with patient noncompliance and self-selected treatment switches in randomized clinical trials.Abstract:
Summary. We estimate cause–effect relationships in empirical research where exposures are not completely controlled, as in observational studies or with patient non-compliance and self-selected treatment switches in randomized clinical trials. Additive and multiplicative structural mean models have proved useful for this but suffer from the classical limitations of linear and log-linear models when accommodating binary data. We propose the generalized structural mean model to overcome these limitations. This is a semiparametric two-stage model which extends the structural mean model to handle non-linear average exposure effects. The first-stage structural model describes the causal effect of received exposure by contrasting the means of observed and potential exposure-free outcomes in exposed subsets of the population. For identification of the structural parameters, a second stage ‘nuisance’ model is introduced. This takes the form of a classical association model for expected outcomes given observed exposure. Under the model, we derive estimating equations which yield consistent, asymptotically normal and efficient estimators of the structural effects. We examine their robustness to model misspecification and construct robust estimators in the absence of any exposure effect. The double-logistic structural mean model is developed in more detail to estimate the effect of observed exposure on the success of treatment in a randomized controlled blood pressure reduction trial with self-selected non-compliance.read more
Citations
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Journal ArticleDOI
A review of instrumental variable estimators for Mendelian randomization
TL;DR: Settings particularly relevant to Mendelian randomization are prioritized in the paper, notably the scenario of a continuous exposure and a continuous or binary outcome.
Journal ArticleDOI
Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants
TL;DR: The feasibility of well-powered, unbiased MR studies will depend upon the amount of variance in the exposure that can be explained by known genetic factors and the 'strength' of the IV set derived from these genetic factors.
Journal ArticleDOI
The effect of elevated body mass index on ischemic heart disease risk: causal estimates from a Mendelian randomisation approach.
Børge G. Nordestgaard,Tom Palmer,Marianne Benn,Marianne Benn,Jeppe Zacho,Jeppe Zacho,Anne Tybjærg-Hansen,George Davey Smith,Nicholas J. Timpson +8 more
TL;DR: A Mendelian randomization analysis conducted by Børge G. Nordestgaard and colleagues using data from observational studies supports a causal relationship between body mass index and risk for ischemic heart disease.
Journal ArticleDOI
Statistical Issues in Life Course Epidemiology
Bianca De Stavola,Dorothea Nitsch,Isabel dos Santos Silva,Valerie McCormack,Rebecca Hardy,Vera Mann,Tim J Cole,Susan M. B. Morton,David A. Leon +8 more
TL;DR: The authors conclude that more than one analytical approach should be adopted to gain more insight into the underlying mechanisms of disease outcomes, and consider a range of modeling approaches.
Journal ArticleDOI
Instrumental Variable Estimation of Causal Risk Ratios and Causal Odds Ratios in Mendelian Randomization Analyses
Tom Palmer,Jonathan A C Sterne,Roger M. Harbord,Debbie A Lawlor,Nuala A. Sheehan,Sha Meng,Raquel Granell,George Davey Smith,Vanessa Didelez +8 more
TL;DR: The authors conclude that point estimates from various IV methods can differ in practical applications, and suggest that structural mean models make weaker assumptions than other IV estimators and can therefore be expected to be consistent in a wider range of situations.
References
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Peter McCullagh,John A. Nelder +1 more
TL;DR: A class of statistical models that generalizes classical linear models-extending them to include many other models useful in statistical analysis, of particular interest for statisticians in medicine, biology, agriculture, social science, and engineering.
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Generalized Linear Models
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Marginal Structural Models and Causal Inference in Epidemiology
TL;DR: In this paper, the authors introduce marginal structural models, a new class of causal models that allow for improved adjustment of confounding in observational studies with exposures or treatments that vary over time, when there exist time-dependent confounders that are also affected by previous treatment.
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Identification of Causal Effects Using Instrumental Variables
TL;DR: It is shown that the instrumental variables (IV) estimand can be embedded within the Rubin Causal Model (RCM) and that under some simple and easily interpretable assumptions, the IV estimand is the average causal effect for a subgroup of units, the compliers.
Journal ArticleDOI
Multiple Imputation After 18+ Years
TL;DR: A description of the assumed context and objectives of multiple imputation is provided, and a review of the multiple imputations framework and its standard results are reviewed.