Accommodating stochastic departures from percentile invariance in causal models

Summary.  Consider a clinical trial in which participants are randomized to a single-dose treatment or a placebo control and assume that the adherence level is accurately recorded. If the treatment is effective, then good adherers in the treatment group should do better than poor ad- herers because they received more drug; the treatment group data follow a dose–response curve. But, good adherers to the placebo often do better than poor adherers, so the observed adherence–response in the treatment group cannot be completely attributed to the treatment. Efron and Feldman proposed an adjustment to the observed adherence–response in the treatment group by using the adherence–response in the control group. It relies on a percentile invariance assumption under which each participant's adherence percentile within their assigned treatment group does not depend on the assigned group (active drug or placebo). The Efron and Feldman approach is valid under percentile invariance, but not necessarily under departures from it. We propose an analysis based on a generalization of percentile invariance that allows adherence percentiles to be stochastically permuted across treatment groups, using a broad class of stochastic permutation models. We show that approximate maximum likelihood estimates of the underlying dose–response curve perform well when the stochastic permutation process is correctly specified and are quite robust to model misspecification.     

Why a time effect often has a limited impact on capture‐recapture estimates in closed populations

The author is concerned with log‐linear estimators of the size N of a population in a capture‐recapture experiment featuring heterogeneity in the individual capture probabilities and a time effect. He also considers models where the first capture influences the probability of subsequent captures. He derives several results from a new inequality associated with a dispersive ordering for discrete random variables. He shows that in a log‐linear model with inter‐individual heterogeneity, the estimator N is an increasing function of the heterogeneity parameter. He also shows that the inclusion of a time effect in the capture probabilities decreases N in models without heterogeneity. He further argues that a model featuring heterogeneity can accommodate a time effect through a small change in the heterogeneity parameter. He demonstrates these results using an inequality for the estimators of the heterogeneity parameters and illustrates them in a Monte Carlo experiment     

The conical structure of adjustive behavior

Wellbeing and coping have usually been treated as separate topics in previous research. The present study proposes a general framework for adjustive behavior which incorporates both previous topics as special cases. This framework shows definitional similarities and differences among wellbeing, coping and further special cases. Empirical data are presented which show a correspondence between the facets of the definitional system and the matrix of intercorrelations of the variables defined thereby. The correspondence is that of a conex, that is, the facets correspond to a conical coordinate system for the SSA space that reproduces the correlation matrix.The data were gathered as part of the Continuing Survey conducted jointly by the Israel Institute of Applied Social Research and the Hebrew University's Communications Institute. The population sampled is the adult urban and rural Jewish population of Israel, over 20 years of age in mid-December 1977. Interviews were conducted in the home through closed questionnaires, 27 of the items being of adjustive behavior.     

Quantifying Potential Health Impacts of Cadmium in Cigarettes on Smoker Risk of Lung Cancer: A Portfolio-of-Mechanisms Approach

This article introduces an approach to estimating the uncertain potential effects on lung cancer risk of removing a particular constituent, cadmium (Cd), from cigarette smoke, given the useful but incomplete scientific information available about its modes of action. The approach considers normal cell proliferation; DNA repair inhibition in normal cells affected by initiating events; proliferation, promotion, and progression of initiated cells; and death or sparing of initiated and malignant cells as they are further transformed to become fully tumorigenic. Rather than estimating unmeasured model parameters by curve fitting to epidemiological or animal experimental tumor data, we attempt rough estimates of parameters based on their biological interpretations and comparison to corresponding genetic polymorphism data. The resulting parameter estimates are admittedly uncertain and approximate, but they suggest a portfolio approach to estimating impacts of removing Cd that gives usefully robust conclusions. This approach views Cd as creating a portfolio of uncertain health impacts that can be expressed as biologically independent relative risk factors having clear mechanistic interpretations. Because Cd can act through many distinct biological mechanisms, it appears likely (subjective probability greater than 40%) that removing Cd from cigarette smoke would reduce smoker risks of lung cancer by at least 10%, although it is possible (consistent with what is known) that the true effect could be much larger or smaller. Conservative estimates and assumptions made in this calculation suggest that the true impact could be greater for some smokers. This conclusion appears to be robust to many scientific uncertainties about Cd and smoking effects.     

Runaway Reactions, Their Courses, and the Methods to Establish Safe Process Conditions

The purpose of this paper is to describe the various process deviations which can cause a runaway reaction to occur, and to discuss the experimental information necessary for risk assessment, the choice of a safe process, and the mitigation of the consequences of the runaway reaction. Ten typical hazardous process situations have been identified, considering various modes of initiation, homogeneous and heterogeneous, Arrhenius, and autocatalytic reactions. Each possible hazardous process deviation is illustrated by examples from the process industry and/or relevant experimental information obtained from laboratory experiments.