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J Robins

Publications and source records attributed to J Robins.

At least 19 recordsLinked to original sources
PubMed1994

Invited commentary: ecologic studies--biases, misconceptions, and counterexamples.

Many authors have pointed out that relative-risk estimates derived from ecologic data are vulnerable to biases not found in estimates derived from individual-level data. Nevertheless, biases in ecologic studies still are often dealt with in the same manner as biases in other observational studies, and so are not given adequate treatment. This commentary reviews and illustrates some of the more recent findings about bias in ecologic estimates. Special attention is given to problems of ecologic confounder control when individual risks follow a nonlinear model, and to misconceptions about ecologic bias that have appeared in the literature.

Bias↗
PubMed1993

Casual inference.

Explore the source record for details and available documents.

Epidemiologic Methods↗
PubMed1991

Estimability and estimation of expected years of life lost due to a hazardous exposure.

Expected years of life lost is an important concept in public-health and legal issues. We describe conditions under which the expected years of life lost due to hazardous exposure is estimable (identifiable) from epidemiologic data. We show that, in general, the average years of life lost among exposed subjects dying at a given age (the age-specific expected years of life lost) is not identifiable, although the average years of life lost among all exposed subjects (the unconditional expected years of life lost) is identifiable from an unbiased epidemiologic study. We also show that the average years of life lost among all exposed subjects dying of a specific cause (the cause-specific expected years of life lost) is not identifiable. We discuss the implications of these results for compensation schemes based on years of life lost, and compare such schemes with those based on the probability of causation.

Accidents, Traffic↗
PubMed1990

The validity of case-control studies with nonrandom selection of controls.

An unbiased estimate of the rate ratio can be obtained using a case-control design in which each case is matched to one or more controls randomly selected from population members at risk and in the same stratum as the case at the time of disease onset. However, the nonrandom assignment of controls to cases is quite frequent in case-control research. It occurs, for example, in matched case-control studies using either friend controls or neighborhood controls. Many valid random designs, in contrast to most nonrandom designs, require enumeration of a substantial fraction of the study base. Therefore, there may be important cost and logistic advantages to using valid nonrandom designs. In this paper we determine those nonrandom case-control designs that can produce unbiased estimates of the rate ratio and discuss the implications of our findings for the design of case-control studies. We conclude, as did Flanders and Austin, that friend-case-control studies should generally be avoided. On the other hand, in a typical neighborhood-matched, case-control study, any bias attributable to nonrandom control selection is usually too small to affect substantive conclusions.

Bias↗
PubMed1989

The control of confounding by intermediate variables.

In epidemiologic studies of the effect of an exposure on disease, the crude association of exposure with disease may fail to reflect a causal association due to confounding by one or more covariates. Most previous discussions of confounding in the epidemiologic literature have considered only point exposure studies, that is, studies that measure exposure and covariate status only once, at start of follow-up. In this paper we offer definitions of confounding suitable for longitudinal studies that obtain data on exposure, covariate, and vital status at several points in time. An important difference between longitudinal studies and point exposure studies is that, in longitudinal studies, a time-dependent covariate can be simultaneously a confounder and an intermediate variable on the causal pathway from exposure to disease. In this paper I propose an estimator, the extended standardized risk difference, that provides control for confounding by a covariate that is simultaneously a confounder and an intermediate variable.

Data Interpretation, Statistical↗
PubMed1989

The probability of causation under a stochastic model for individual risk.

In this paper we offer a mathematical definition for the probability of causation that formalizes the legal and ordinary-language meaning of the term. We show that, under this definition, even the average probability of causation among exposed cases is not identifiable from epidemiologic data. This is because the probability of causation depends both on the unknown mechanisms by which exposure affects disease risk and competing risks, and on the unknown degree of heterogeneity in the background disease risk of the exposed population. We derive the maximum and minimum values for the probability of causation consistent with the observable population quantities. We also derive the relationship of the "assigned share" (excess incidence rate as a proportion of total incidence rate) to the probability of causation.

Biometry↗
PubMed1988

A study of childhood scalds.

A retrospective review of admissions to the Wessex Regional Burn Centre was made to determine the incidence and causes of childhood scalds during the periods 1960-65 and 1979-84. No reduction in numbers injured or significant change in causes were observed. Children aged 1-2 years old are still the most prone to scalding injury.

Accidents, Home↗
PubMed1987

A graphical approach to the identification and estimation of causal parameters in mortality studies with sustained exposure periods.

In observational cohort mortality studies with prolonged periods of exposure to the agent under study, independent risk factors for death commonly determine subsequent exposure to the study agent. For example, in occupational mortality studies, date of termination of employment is both a determinant of subsequent exposure to the chemical agent under study (since terminated individuals receive no further exposure) and an independent risk factor for death (since disabled individuals tend to leave employment). When a risk factor determines subsequent exposure and is determined by previous exposure, standard analyses that estimate age-specific mortality rates as a function of cumulative exposure can underestimate the true effect of exposure on mortality, whether or not one adjusts for the risk factor in the analysis. This observation raises the question, "Which, if any, empirical population parameter can be causally interpreted as the true effect of exposure in observational mortality studies?" In answer, we offer a graphical approach to the identification and estimation of causal parameters in mortality studies with sustained exposure periods. We reanalyze the mortality experience of a cohort of arsenic-exposed copper smelter workers using our approach and compare our results with those obtained using standard methods. We find an adverse effect of arsenic exposure on all cause and lung cancer mortality, which standard methods failed to detect. The analytic approach introduced in this paper may be necessary to control bias in any epidemiologic study in which there exists a risk factor which both determines subsequent exposure and is determined by previous exposure to the agent under study.

Epidemiologic Methods↗
PubMed1986

Cardiac involvement in congenital acquired immunodeficiency syndrome.

Cardiac abnormalities have been reported in 25% to 73% of adult patients with acquired immunodeficiency syndrome (AIDS). We are reporting the clinical course of a child with congenital AIDS who developed similar cardiac complications. He presented with congestive heart failure three months after the diagnosis of AIDS. He had cardiomegaly demonstrated on chest roentgenogram, which was previously normal. He had left ventricular hypertrophy and T-wave abnormalities on electrocardiography and left ventricular dysfunction and dilatation on echocardiography. His subsequent echocardiogram continued to show poor contractility, although his congestive symptoms were stabilized with digitalis therapy and diuresis. After a year of maintenance therapy with digitalis, he developed right ventricular and right atrial enlargement and tricuspid valve thickening and nodularity, similar to the valvular changes reported in adults. Thus, children with AIDS should be monitored for cardiac complications.

Acquired Immunodeficiency Syndrome↗
PubMed1986

Explaining discrepancies between longitudinal and cross-sectional models.

Data from longitudinal studies may be analyzed both cross-sectionally and longitudinally. Discrepancies between estimates obtained from these analyses pose questions about the validity of cross-sectional estimates of change. In some cases these discrepancies are the result of period effects, cohort effects, or selective dropout. In others, they are the result of incomplete modeling of the process and are spurious rather than substantive. In this report, we show that when the true relation between a dependent variable and age is non-linear (e.g. quadratic), but is modeled as linear, the estimated age effect will be a function of the age distribution. In a continuous-time idealization, if the age distribution is Gaussian, the estimated age effects agree. If the age distribution is symmetric and the non-linearity is quadratic, cross-sectional and longitudinal results agree. Otherwise they do not. We illustrate these points by analysis of the relation between aging and pulmonary function in middle and old age using data from a large, prospective, longitudinal study.

Adult↗
PubMed1985

The problem of multiple inference in studies designed to generate hypotheses.

Epidemiologic research often involves the simultaneous assessment of associations between many risk factors and several disease outcomes. In such situations, often designed to generate hypotheses, multiple univariate hypothesis-testing is not an appropriate basis for inference. The number of true positive associations in a collection of many associations can be estimated by comparing the observed distribution of p values for the positive associations to a theoretical uniform distribution, or to the observed distribution of negative associations, or to an empiric randomization distribution. None of these approaches, however, will distinguish the true from the false positive associations. Various criteria for selecting a subset of associations to report are considered by the authors, including Bonferoni adjustment of p values, splitting the sample for searching and testing, Bayesian inference, and decision theory. The authors prefer an approach in which all associations in the data are reported, whether significant or not, followed by a ranking in order of priority for investigation using empirical Bayes techniques. Methods are illustrated by application to preliminary data from a study aimed at identifying hitherto unsuspected occupational carcinogens.

Bayes Theorem↗
PubMed1985

Acupuncture for the induction of cervical dilatation in preparation for first-trimester abortion and its influence on HCG.

Cervical dilatation in preparation for first-trimester abortion using acupuncture at loci SP6 and LI4 was studied in 20 patients, who were compared to a control group in whom no preparation was used. Ninety percent of the patients had successful acupuncture procedures. As judged by Hegar dilators, the increase in cervical dilatation in those 18 patients was significantly greater than in the controls. No significant side effects were observed. The effect of acupuncture on serum human chorionic gonadotropin (HCG) as a pregnancy marker was evaluated in 12 patients. No statistically significant difference in the change in HCG was noted in terms of the controls, indicating an absence of abortifacient activity with acupuncture.

Abortion, Induced↗