Tue, 07 Jun 2011 16:00:12 GMT-05:0000001648-200103000-00019
http://journals.lww.com/epidem/Fulltext/2003/11000/Marginal_Structural_Models_as_a_Tool_for.9.aspx
<![CDATA[Marginal Structural Models as a Tool for Standardization]]>In this article, we show the general relation between standardization methods and marginal structural models. Standardization has been recognized as a method to control confounding and to estimate causal parameters of interest. Because standardization requires stratification by confounders, the sparse-data problem will occur when stratified by many confounders and one then might have an unstable estimator. A new class of causal models called marginal structural models has recently been proposed. In marginal structural models, the parameters are consistently estimated by the inverse-probability-of-treatment weighting method. Marginal structural models give a nonparametric standardization using the total group (exposed and unexposed) as the standard. In epidemiologic analysis, it is also important to know the change in the average risk of the exposed (or the unexposed) subgroup produced by exposure, which corresponds to the exposed (or the unexposed) group as the standard. We propose modifications of the weights in the marginal structural models, which give the nonparametric estimation of standardized parameters. With the proposed weights, we can use the marginal structural models as a useful tool for the nonparametric multivariate standardization.]]>Tue, 07 Jun 2011 16:01:01 GMT-05:0000001648-200311000-00009
http://journals.lww.com/epidem/Fulltext/2006/05000/Estimation_of_Direct_Causal_Effects.12.aspx
<![CDATA[Estimation of Direct Causal Effects]]>Abstract:
Many common problems in epidemiologic and clinical research involve estimating the effect of an exposure on an outcome while blocking the exposure's effect on an intermediate variable. Effects of this kind are termed direct effects. Estimation of direct effects is typically the goal of research aimed at understanding mechanistic pathways by which an exposure acts to cause or prevent disease, as well as in many other settings. Although multivariable regression is commonly used to estimate direct effects, this approach requires assumptions beyond those required for the estimation of total causal effects. In addition, when the exposure and intermediate variables interact to cause disease, multivariable regression estimates a particular type of direct effect—the effect of an exposure on an outcome when the intermediate is fixed at a specified level. Using the counterfactual framework, we distinguish this definition of a direct effect (controlled direct effect) from an alternative definition, in which the effect of the exposure on the intermediate is blocked, but the intermediate is otherwise allowed to vary as it would in the absence of exposure (natural direct effect). We illustrate the difference between controlled and natural direct effects using several examples. We present an estimation approach for natural direct effects that can be implemented using standard statistical software, and we review the assumptions underlying our approach (which are less restrictive than those proposed by previous authors).]]>Tue, 07 Jun 2011 16:01:55 GMT-05:0000001648-200605000-00012
http://journals.lww.com/epidem/Fulltext/2009/01000/The_Consistency_Statement_in_Causal_Inference__A.3.aspx
<![CDATA[The Consistency Statement in Causal Inference: A Definition or an Assumption?]]>No abstract available]]>Tue, 07 Jun 2011 16:02:43 GMT-05:0000001648-200901000-00003
http://journals.lww.com/epidem/Fulltext/2009/01000/Interactions_in_Epidemiology__Relevance,.5.aspx
<![CDATA[Interactions in Epidemiology: Relevance, Identification, and Estimation]]>No abstract available]]>Tue, 07 Jun 2011 16:04:50 GMT-05:0000001648-200901000-00005
http://journals.lww.com/epidem/Fulltext/2009/05000/Bringing_Causal_Models_Into_the_Mainstream.20.aspx
<![CDATA[Bringing Causal Models Into the Mainstream]]>No abstract available]]>Tue, 07 Jun 2011 16:06:33 GMT-05:0000001648-200905000-00020
http://journals.lww.com/epidem/Fulltext/2009/11000/Estimating_Direct_Effects_in_Cohort_and.14.aspx
<![CDATA[Estimating Direct Effects in Cohort and Case–Control Studies]]>Abstract:
Estimating the effect of an exposure on an outcome, other than through some given mediator, requires adjustment for all risk factors of the mediator that are also associated with the outcome. When these risk factors are themselves affected by the exposure, then standard regression methods do not apply. In this article, I review methods for accommodating this and discuss their limitations for estimating the controlled direct effect (ie, the exposure effect when controlling the mediator at a specified level uniformly in the population). In addition, I propose a powerful and easy-to-apply alternative that uses G-estimation in structural nested models to address these limitations both for cohort and case–control studies.]]>Tue, 07 Jun 2011 16:07:28 GMT-05:0000001648-200911000-00014
http://journals.lww.com/epidem/Fulltext/2009/11000/Mediating_Various_Direct_effect_Approaches.15.aspx
<![CDATA[Mediating Various Direct-effect Approaches]]>No abstract available]]>Tue, 07 Jun 2011 16:08:40 GMT-05:0000001648-200911000-00015
http://journals.lww.com/epidem/Fulltext/2009/11000/Concerning_the_Consistency_Assumption_in_Causal.18.aspx
<![CDATA[Concerning the Consistency Assumption in Causal Inference]]>Abstract:
Cole and Frangakis (Epidemiology. 2009;20:3–5) introduced notation for the consistency assumption in causal inference. I extend this notation and propose a refinement of the consistency assumption that makes clear that the consistency statement, as ordinarily given, is in fact an assumption and not an axiom or definition. The refinement is also useful in showing that additional assumptions (referred to here as treatment-variation irrelevance assumptions), stronger than those given by Cole and Frangakis, are in fact necessary in articulating the ordinary assumptions of ignorability or exchangeability. The refinement furthermore sheds light on the distinction between intervention and choice in reasoning about causality. A distinction between the range of treatment variations for which potential outcomes can be defined and the range for which treatment comparisons are made is discussed in relation to issues of nonadherence. The use of stochastic counterfactuals can help relax what is effectively being presupposed by the treatment-variation irrelevance assumption and the consistency assumption.]]>Tue, 07 Jun 2011 16:09:10 GMT-05:0000001648-200911000-00018
http://journals.lww.com/epidem/Fulltext/2009/11000/Causal_Models.28.aspx
<![CDATA[Causal Models]]>No abstract available]]>Tue, 07 Jun 2011 16:09:53 GMT-05:0000001648-200911000-00028
http://journals.lww.com/epidem/Fulltext/2009/11000/Causal_Models.29.aspx
<![CDATA[Causal Models]]>No abstract available]]>Tue, 07 Jun 2011 16:10:22 GMT-05:0000001648-200911000-00029
http://journals.lww.com/epidem/Fulltext/2010/01000/DAG_Program___Identifying_Minimal_Sufficient.29.aspx
<![CDATA[DAG Program:: Identifying Minimal Sufficient Adjustment Sets]]>No abstract available]]>Tue, 07 Jun 2011 16:11:30 GMT-05:0000001648-201001000-00029
http://journals.lww.com/epidem/Fulltext/2010/05000/Negative_Controls__A_Tool_for_Detecting.17.aspx
<![CDATA[Negative Controls: A Tool for Detecting Confounding and Bias in Observational Studies]]>Abstract:
Noncausal associations between exposures and outcomes are a threat to validity of causal inference in observational studies. Many techniques have been developed for study design and analysis to identify and eliminate such errors. Such problems are not expected to compromise experimental studies, where careful standardization of conditions (for laboratory work) and randomization (for population studies) should, if applied properly, eliminate most such noncausal associations. We argue, however, that a routine precaution taken in the design of biologic laboratory experiments—the use of “negative controls”—is designed to detect both suspected and unsuspected sources of spurious causal inference. In epidemiology, analogous negative controls help to identify and resolve confounding as well as other sources of error, including recall bias or analytic flaws. We distinguish 2 types of negative controls (exposure controls and outcome controls), describe examples of each type from the epidemiologic literature, and identify the conditions for the use of such negative controls to detect confounding. We conclude that negative controls should be more commonly employed in observational studies, and that additional work is needed to specify the conditions under which negative controls will be sensitive detectors of other sources of error in observational studies.]]>Tue, 07 Jun 2011 16:12:16 GMT-05:0000001648-201005000-00017
http://journals.lww.com/epidem/Fulltext/2010/07000/dagR__A_Suite_of_R_Functions_for_Directed_Acyclic.26.aspx
<![CDATA[dagR: A Suite of R Functions for Directed Acyclic Graphs]]>No abstract available]]>Tue, 07 Jun 2011 16:13:20 GMT-05:0000001648-201007000-00026
http://journals.lww.com/epidem/Fulltext/2010/09000/Sufficient_cause_Interaction.33.aspx
<![CDATA[Sufficient-cause Interaction]]>No abstract available]]>Tue, 07 Jun 2011 16:14:22 GMT-05:0000001648-201009000-00033
http://journals.lww.com/epidem/Fulltext/2010/11000/On_the_Consistency_Rule_in_Causal_Inference_.19.aspx
<![CDATA[On the Consistency Rule in Causal Inference: Axiom, Definition, Assumption, or Theorem?]]>Abstract:
In 2 recent communications, Cole and Frangakis (Epidemiology. 2009;20:3–5) and VanderWeele (Epidemiology. 2009;20:880–883) conclude that the consistency rule used in causal inference is an assumption that precludes any side-effects of treatment/exposure on the outcomes of interest. They further develop auxiliary notation to make this assumption formal and explicit. I argue that the consistency rule is a theorem in the logic of counterfactuals and need not be altered. Instead, warnings of potential side-effects should be embodied in standard modeling practices that make causal assumptions explicit and transparent.]]>Tue, 07 Jun 2011 16:15:09 GMT-05:0000001648-201011000-00019
http://journals.lww.com/epidem/Fulltext/2011/01000/On_the_Link_Between_Sufficient_cause_Model_and.26.aspx
<![CDATA[On the Link Between Sufficient-cause Model and Potential-outcome Model]]>No abstract available]]>Tue, 07 Jun 2011 16:16:05 GMT-05:0000001648-201101000-00026
http://journals.lww.com/epidem/Fulltext/2011/05000/Compound_Treatments_and_Transportability_of_Causal.18.aspx
<![CDATA[Compound Treatments and Transportability of Causal Inference]]>Abstract:
Ill-defined causal questions present serious problems for observational studies—problems that are largely unappreciated. This paper extends the usual counterfactual framework to consider causal questions about compound treatments for which there are many possible implementations (for example, “prevention of obesity”). We describe the causal effect of compound treatments and their identifiability conditions, with a special emphasis on the consistency condition. We then discuss the challenges of using the estimated effect of a compound treatment in one study population to inform decisions in the same population and in other populations. These challenges arise because the causal effect of compound treatments depends on the distribution of the versions of treatment in the population. Such causal effects can be unpredictable when the versions of treatment are unknown. We discuss how such issues of “transportability” are related to the consistency condition in causal inference. With more carefully framed questions, the results of epidemiologic studies can be of greater value to decision-makers.]]>Tue, 07 Jun 2011 16:17:20 GMT-05:0000001648-201105000-00018
http://journals.lww.com/epidem/Fulltext/2011/05000/Compound_Treatments,_Transportability,_and_the.19.aspx
<![CDATA[Compound Treatments, Transportability, and the Structural Causal Model: The Power and Simplicity of Causal Graphs]]>No abstract available]]>Tue, 07 Jun 2011 16:18:18 GMT-05:0000001648-201105000-00019
http://journals.lww.com/epidem/Fulltext/2011/07000/Direct_and_Indirect_Effects_in_a_Survival_Context.24.aspx
<![CDATA[Direct and Indirect Effects in a Survival Context]]>Abstract:
A cornerstone of epidemiologic research is to understand the causal pathways from an exposure to an outcome. Mediation analysis based on counterfactuals is an important tool when addressing such questions. However, none of the existing techniques for formal mediation analysis can be applied to survival data. This is a severe shortcoming, as many epidemiologic questions can be addressed only with censored survival data. A solution has been to use a number of Cox models (with and without the potential mediator), but this approach does not allow a causal interpretation and is not mathematically consistent. In this paper, we propose a simple measure of mediation in a survival setting. The measure is based on counterfactuals, and measures the natural direct and indirect effects. The method allows a causal interpretation of the mediated effect (in terms of additional cases per unit of time) and is mathematically consistent. The technique is illustrated by analyzing socioeconomic status, work environment, and long-term sickness absence. A detailed implementation guide is included in an online eAppendix (http://links.lww.com/EDE/A476).]]>Tue, 07 Jun 2011 16:18:58 GMT-05:0000001648-201107000-00024
http://journals.lww.com/epidem/Fulltext/2011/07000/Causal_Mediation_Analysis_With_Survival_Data.25.aspx
<![CDATA[Causal Mediation Analysis With Survival Data]]>No abstract available]]>Tue, 07 Jun 2011 16:19:29 GMT-05:0000001648-201107000-00025
http://journals.lww.com/epidem/Fulltext/2011/07000/Differences_Between_Marginal_Structural_Models_and.26.aspx
<![CDATA[Differences Between Marginal Structural Models and Conventional Models in Their Exposure Effect Estimates: A Systematic Review]]>Background:
Marginal structural models were developed to address time-varying confounding in nonrandomized exposure effect studies. It is unclear how estimates from marginal structural models and conventional models might differ in real settings.
Methods:
We systematically reviewed the literature on marginal structural models since 2000.
Results:
Data to compare marginal structural models and conventional models were obtained from 65 papers reporting 164 exposure-outcome associations. In 58 (40%), estimates differed by at least 20%, and in 18 (11%), the 2 techniques resulted in estimates with opposite interpretations. In 88 papers, marginal structural models were used to analyze real data; only 53 (60%) papers reported the use of stabilized inverse-probability weights and only 28 (32%) reported that they verified that the mean of the stabilized inverse-probability weights was close to 1.0.
Conclusions:
We found important differences in results from marginal structural models and from conventional models in real studies. Furthermore, reporting of marginal structural models can be improved.]]>Tue, 07 Jun 2011 16:19:58 GMT-05:0000001648-201107000-00026
http://journals.lww.com/epidem/Fulltext/2011/09000/Causal_Interactions_in_the_Proportional_Hazards.17.aspx
<![CDATA[Causal Interactions in the Proportional Hazards Model]]>Abstract:
The paper relates estimation and testing for additive interaction in proportional hazards models to causal interactions within the counterfactual framework. A definition of a causal interaction for time-to-event outcomes is given that generalizes existing definitions for dichotomous outcomes. Conditions are given concerning the relative excess risk due to interaction in proportional hazards models that imply the presence of a causal interaction at some point in time. Further results are given that allow for assessing the range of times and baseline survival probabilities for which parameter estimates indicate that a causal interaction is present, and for deriving lower bounds on the prevalence of such causal interactions. An interesting feature of the time-to-event setting is that causal interactions can disappear as time progresses, ie, whether a causal interaction is present depends on the follow-up time. The results are illustrated by hypothetical and data analysis examples.]]>Tue, 18 Oct 2011 10:48:23 GMT-05:0000001648-201109000-00017
http://journals.lww.com/epidem/Fulltext/2011/09000/Transportability_and_Causal_Generalization.23.aspx
<![CDATA[Transportability and Causal Generalization]]>No abstract available]]>Tue, 18 Oct 2011 10:51:56 GMT-05:0000001648-201109000-00023
http://journals.lww.com/epidem/Fulltext/2011/11000/Alternative_Assumptions_for_the_Identification_of.1.aspx
<![CDATA[Alternative Assumptions for the Identification of Direct and Indirect Effects]]>Abstract:
The assessment of mediation is important for testing the mechanisms that explain an observed relationship between exposure and disease. Several types of direct and indirect effects have been defined, broadly characterized as either controlled or natural. The identification of these effects requires a stricter set of assumptions than those necessary for the identification of the total effect of exposure on disease. The particular assumptions that are required differ depending on the type of effect. We use an approach based on response types to derive new assumptions for the identification of direct and indirect effects, both controlled and natural. These assumptions are stated in terms of response types and potential outcomes, and are compared with those already in the literature. This approach yields an alternative, and sometimes less stringent, set of assumptions for the identification of direct and indirect effects than those previously proposed.]]>Tue, 18 Oct 2011 10:53:49 GMT-05:0000001648-201111000-00001
http://journals.lww.com/epidem/Fulltext/2012/05000/Completion_Potentials_of_Sufficient_Component.15.aspx
<![CDATA[Completion Potentials of Sufficient Component Causes]]>Many epidemiologists are familiar with Rothman's sufficient component cause model. In this paper, I propose a new index for this model, the completion potential index I show that, with proper assumptions (monotonicity, independent competing causes, proportional hazards), completion potentials for various classes of sufficient causes are estimable from routine epidemiologic data (cohort, case-control or time-to-event data). I discuss the advantage of the completion potential index over indices of rate ratio, rate difference, causal-pie weight, population attributable fraction, and attributable fraction within the exposed population. Hypothetical and real data examples are used. The completion potential index proposed here allows better characterization of complex interactive effects of multiple monotonic risk factors.]]>Wed, 30 May 2012 12:46:42 GMT-05:0000001648-201205000-00015
http://journals.lww.com/epidem/Fulltext/2012/05000/Mediation_Analysis_With_Multiple_Versions_of_the.16.aspx
<![CDATA[Mediation Analysis With Multiple Versions of the Mediator]]>The causal inference literature has provided definitions of direct and indirect effects based on counterfactuals that generalize the approach found in the social science literature. However, these definitions presuppose well-defined hypothetical interventions on the mediator. In many settings, there may be multiple ways to fix the mediator to a particular value, and these various hypothetical interventions may have very different implications for the outcome of interest. In this paper, we consider mediation analysis when multiple versions of the mediator are present. Specifically, we consider the problem of attempting to decompose a total effect of an exposure on an outcome into the portion through the intermediate and the portion through other pathways. We consider the setting in which there are multiple versions of the mediator but the investigator has access only to data on the particular measurement, not information on which version of the mediator may have brought that value about. We show that the quantity that is estimated as a natural indirect effect using only the available data does indeed have an interpretation as a particular type of mediated effect; however, the quantity estimated as a natural direct effect, in fact, captures both a true direct effect and an effect of the exposure on the outcome mediated through the effect of the version of the mediator that is not captured by the mediator measurement. The results are illustrated using 2 examples from the literature, one in which the versions of the mediator are unknown and another in which the mediator itself has been dichotomized.]]>Wed, 30 May 2012 12:47:21 GMT-05:0000001648-201205000-00016
http://journals.lww.com/epidem/Fulltext/2012/11000/Distribution_Free_Mediation_Analysis_for_Nonlinear.18.aspx
<![CDATA[Distribution-Free Mediation Analysis for Nonlinear Models with Confounding]]>Recently, researchers have used a potential-outcome framework to estimate causally interpretable direct and indirect effects of an intervention or exposure on an outcome. One approach to causal-mediation analysis uses the so-called mediation formula to estimate the natural direct and indirect effects. This approach generalizes the classical mediation estimators and allows for arbitrary distributions for the outcome variable and mediator. A limitation of the standard (parametric) mediation formula approach is that it requires a specified mediator regression model and distribution; such a model may be difficult to construct and may not be of primary interest. To address this limitation, we propose a new method for causal-mediation analysis that uses the empirical distribution function, thereby avoiding parametric distribution assumptions for the mediator. To adjust for confounders of the exposure-mediator and exposure-outcome relationships, inverse-probability weighting is incorporated based on a supplementary model of the probability of exposure. This method, which yields the estimates of the natural direct and indirect effects for a specified reference group, is applied to data from a cohort study of dental caries in very-low-birth-weight adolescents to investigate the oral-hygiene index as a possible mediator. Simulation studies show low bias in the estimation of direct and indirect effects in a variety of distribution scenarios, whereas the standard mediation formula approach can be considerably biased when the distribution of the mediator is incorrectly specified.]]>Fri, 12 Oct 2012 07:45:22 GMT-05:0000001648-201211000-00018