348 research outputs found
Structural Nested Models and G-estimation: The Partially Realized Promise
Structural nested models (SNMs) and the associated method of G-estimation
were first proposed by James Robins over two decades ago as approaches to
modeling and estimating the joint effects of a sequence of treatments or
exposures. The models and estimation methods have since been extended to
dealing with a broader series of problems, and have considerable advantages
over the other methods developed for estimating such joint effects. Despite
these advantages, the application of these methods in applied research has been
relatively infrequent; we view this as unfortunate. To remedy this, we provide
an overview of the models and estimation methods as developed, primarily by
Robins, over the years. We provide insight into their advantages over other
methods, and consider some possible reasons for failure of the methods to be
more broadly adopted, as well as possible remedies. Finally, we consider
several extensions of the standard models and estimation methods.Comment: Published in at http://dx.doi.org/10.1214/14-STS493 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Authors' reply to 'Multiple comparisons controversies are about context and costs, not frequentism versus Bayesianism'
Graphical models for mediation analysis
Mediation analysis seeks to infer how much of the effect of an exposure on an
outcome can be attributed to specific pathways via intermediate variables or
mediators. This requires identification of so-called path-specific effects.
These express how a change in exposure affects those intermediate variables
(along certain pathways), and how the resulting changes in those variables in
turn affect the outcome (along subsequent pathways). However, unlike
identification of total effects, adjustment for confounding is insufficient for
identification of path-specific effects because their magnitude is also
determined by the extent to which individuals who experience large exposure
effects on the mediator, tend to experience relatively small or large mediator
effects on the outcome. This chapter therefore provides an accessible review of
identification strategies under general nonparametric structural equation
models (with possibly unmeasured variables), which rule out certain such
dependencies. In particular, it is shown which path-specific effects can be
identified under such models, and how this can be done
Natural direct and indirect effects on the exposed : effect decomposition under weaker assumptions
We define natural direct and indirect effects on the exposed. We show that these allow for effect decomposition under weaker identification conditions than population natural direct and indirect effects. When no confounders of the mediator-outcome association are affected by the exposure, identification is possible under essentially the same conditions as for controlled direct effects. Otherwise, identification is still possible with additional knowledge on a nonidentifiable selection-bias function which measures the dependence of the mediator effect on the observed exposure within confounder levels, and which evaluates to zero in a large class of realistic data-generating mechanisms. We argue that natural direct and indirect effects on the exposed are of intrinsic interest in various applications. We moreover show that they coincide with the corresponding population natural direct and indirect effects when the exposure is randomly assigned. In such settings, our results are thus also of relevance for assessing population natural direct and indirect effects in the presence of exposure-induced mediator-outcome confounding, which existing methodology has not been able to address
Make the most of your samples : Bayes factor estimators for high-dimensional models of sequence evolution
Background: Accurate model comparison requires extensive computation times, especially for parameter-rich models of sequence evolution. In the Bayesian framework, model selection is typically performed through the evaluation of a Bayes factor, the ratio of two marginal likelihoods (one for each model). Recently introduced techniques to estimate (log) marginal likelihoods, such as path sampling and stepping-stone sampling, offer increased accuracy over the traditional harmonic mean estimator at an increased computational cost. Most often, each model's marginal likelihood will be estimated individually, which leads the resulting Bayes factor to suffer from errors associated with each of these independent estimation processes.
Results: We here assess the original 'model-switch' path sampling approach for direct Bayes factor estimation in phylogenetics, as well as an extension that uses more samples, to construct a direct path between two competing models, thereby eliminating the need to calculate each model's marginal likelihood independently. Further, we provide a competing Bayes factor estimator using an adaptation of the recently introduced stepping-stone sampling algorithm and set out to determine appropriate settings for accurately calculating such Bayes factors, with context-dependent evolutionary models as an example. While we show that modest efforts are required to roughly identify the increase in model fit, only drastically increased computation times ensure the accuracy needed to detect more subtle details of the evolutionary process.
Conclusions: We show that our adaptation of stepping-stone sampling for direct Bayes factor calculation outperforms the original path sampling approach as well as an extension that exploits more samples. Our proposed approach for Bayes factor estimation also has preferable statistical properties over the use of individual marginal likelihood estimates for both models under comparison. Assuming a sigmoid function to determine the path between two competing models, we provide evidence that a single well-chosen sigmoid shape value requires less computational efforts in order to approximate the true value of the (log) Bayes factor compared to the original approach. We show that the (log) Bayes factors calculated using path sampling and stepping-stone sampling differ drastically from those estimated using either of the harmonic mean estimators, supporting earlier claims that the latter systematically overestimate the performance of high-dimensional models, which we show can lead to erroneous conclusions. Based on our results, we argue that highly accurate estimation of differences in model fit for high-dimensional models requires much more computational effort than suggested in recent studies on marginal likelihood estimation
Asking Too Much of Epidemiologic Studies: The Problem of Collider Bias and the Obesity Paradox.
A new approach for within-subject mediation analysis in AB/BA crossover designs
Crossover trials are widely used in psychological and medical research to assess the effect of reversible exposures. In such designs, each subject is randomly allocated to a sequence of conditions, enabling the evaluation of treatment differences within each individual. When there are but two possible exposures -each assessed during one of two time periods-, the crossover study is referred to as an AB/BA design. The goal of this presentation is to discuss mediation analysis in such simple crossover studies. We do so by considering within-subject mediation from a counterfactual-based perspective and by deriving expressions for the direct and indirect effects. Employing simulation studies, the performance of several existing methods will be assessed and compared to that of a novel one we propose. We show that the new method yields unbiased and efficient estimators for the direct and indirect effect, under a minimalistic set of `no unmeasured confounding'-assumptions. Finally, we illustrate the different techniques with data from a neurobehavioral study
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