Analytic Bounds on Causal Risk Differences in Directed Acyclic Graphs Involving Three Observed Binary Variables.

We apply a linear programming approach which uses the causal risk difference (RD(C)) as the objective function and provides minimum and maximum values that RD(C) can achieve under any set of linear constraints on the potential response type distribution. We consider two scenarios involving binary exposure X, covariate Z and outcome Y. In the first, Z is not affected by X, and is a potential confounder of the causal effect of X on Y.

Bounding causal effects under uncontrolled confounding using counterfactuals.

Common sensitivity analysis methods for unmeasured confounders provide a corrected point estimate of causal effect for each specified set of unknown parameter values. This article reviews alternative methods for generating deterministic nonparametric bounds on the magnitude of the causal effect using linear programming methods and potential outcomes models. The bounds are generated using only the observed table.

Improved estimation of controlled direct effects in the presence of unmeasured confounding of intermediate variables.

Adjusting for a causal intermediate is a common analytic strategy for estimating an average causal direct effect (ACDE). The ACDE is the component of the total exposure effect that is not relayed through the specified intermediate. Even if the total effect is unconfounded, the usual ACDE estimate may be biased when an unmeasured variable affects the intermediate and outcome variables.

A further critique of the analytic strategy of adjusting for covariates to identify biologic mediation.

BACKGROUND: Epidemiologic research is often devoted to etiologic investigation, and so techniques that may facilitate mechanistic inferences are attractive. Some of these techniques rely on rigid and/or unrealistic assumptions, making the biologic inferences tenuous. The methodology investigated here is effect decomposition: the contrast between effect measures estimated with and without adjustment for one or more variables hypothesized to lie on the pathway through which the exposure exerts its effect.

Causal inference from randomized trials in social epidemiology.

Social epidemiology is the study of relations between social factors and health status in populations. Although recent decades have witnessed a rapid development of this research program in scope and sophistication, causal inference has proven to be a persistent dilemma due to the natural assignment of exposure level based on unmeasured attributes of individuals, which may lead to substantial confounding. Some optimism has been expressed about randomized social interventions as a solution to this long-standing inferential problem.

Carcinoma of the head and neck: a 5- to 20-year experience with preoperative chemotherapy, uncompromised surgery, and selective radiotherapy.

Background: A 5- to 20-year evaluation of preoperative chemotherapy uncompromised surgery and selective radiotherapy in stage III/IV head and neck squamous cell carcinoma.

Methods: Eighty-two consecutive patients, single surgeon previously untreated, operable, and resectable for cure. Sites included the oral cavity, oropharynx, larynx, and hypopharynx.