The role of counterfactual theory in causal reasoning.

In this commentary I review the fundamentals of counterfactual theory and its role in causal reasoning in epidemiology. I consider if counterfactual theory dictates that causal questions must be framed in terms of well-defined interventions. I conclude that it does not.

Quantitative bias analysis in an asthma study of rescue-recovery workers and volunteers from the 9/11 World Trade Center attacks.

Purpose: When learning bias analysis, epidemiologists are taught to quantitatively adjust for multiple biases by correcting study results in the reverse order of the error sequence. To understand the error sequence for a particular study, one must carefully examine the health study's epidemiologic data-generating process. In this article, we describe the unique data-generating process of a man-made disaster epidemiologic study.

Quantifying and Adjusting for Disease Misclassification Due to Loss to Follow-Up in Historical Cohort Mortality Studies.

The purpose of this analysis was to quantify and adjust for disease misclassification from loss to follow-up in a historical cohort mortality study of workers where exposure was categorized as a multi-level variable. Disease classification parameters were defined using 2008 mortality data for the New Zealand population and the proportions of known deaths observed for the cohort. The probability distributions for each classification parameter were constructed to account for potential differences in mortality due to exposure status, gender, and ethnicity.

Good practices for quantitative bias analysis.

Quantitative bias analysis serves several objectives in epidemiological research. First, it provides a quantitative estimate of the direction, magnitude and uncertainty arising from systematic errors. Second, the acts of identifying sources of systematic error, writing down models to quantify them, assigning values to the bias parameters and interpreting the results combat the human tendency towards overconfidence in research results, syntheses and critiques and the inferences that rest upon them.

Toward a clearer understanding of causal concepts in epidemiology.

Purpose: In this manuscript, I share insights into causal concepts that emerged from creating and refining a simple example originally designed for teaching causal epidemiologic concepts.

Methods: The insights that emerged are primarily related to the difference between how a causal effect occurs in an individual and what our methods assume about how a causal effect occurs when we estimate its effect in a population. In an individual, the causal effect of exposure on disease occurrence results from the interaction of several causal factors in that individual, not from a single factor in isolation.

Adjusting for outcome misclassification: the importance of accounting for case-control sampling and other forms of outcome-related selection.

Purpose: Special care must be taken when adjusting for outcome misclassification in case-control data. Basic adjustment formulas using either sensitivity and specificity or predictive values (as with external validation data) do not account for the fact that controls are sampled from a much larger pool of potential controls. A parallel problem arises in surveys and cohort studies in which participation or loss is outcome related.

The effect of uncertainty in exposure estimation on the exposure-response relation between 1,3-butadiene and leukemia.

In a follow-up study of mortality among North American synthetic rubber industry workers, cumulative exposure to 1,3-butadiene was positively associated with leukemia. Problems with historical exposure estimation, however, may have distorted the association. To evaluate the impact of potential inaccuracies in exposure estimation, we conducted uncertainty analyses of the relation between cumulative exposure to butadiene and leukemia.

Update: Greenland and Robins (1986). Identifiability, exchangeability and epidemiological confounding.

We are pleased to publish an update to "Identifiabiliity, exchangeability and epidemiological confounding" (IEEC) by Sander Greenland and James Robins, originally published in 1986 in the International Journal of Epidemiology. This is the first in a series of updates to classic epidemiologic-methods papers that EP&I has commissioned.

Specifying exposure classification parameters for sensitivity analysis: family breast cancer history.

One of the challenges to implementing sensitivity analysis for exposure misclassification is the process of specifying the classification proportions (eg, sensitivity and specificity). The specification of these assignments is guided by three sources of information: estimates from validation studies, expert judgment, and numerical constraints given the data. The purpose of this teaching paper is to describe the process of using validation data and expert judgment to adjust a breast cancer odds ratio for misclassification of family breast cancer history.

How far from non-differential does exposure or disease misclassification have to be to bias measures of association away from the null?

A well-known heuristic in epidemiology is that non-differential exposure or disease misclassification biases the expected values of an estimator toward the null value. This heuristic works correctly only when additional conditions are met, such as independence of classification errors. We present examples to show that, even when the additional conditions are met, if the misclassification is only approximately non-differential, then bias is not guaranteed to be toward the null.

Uncertainty analysis: an example of its application to estimating a survey proportion.

Uncertainty analysis is a method, established in engineering and policy analysis but relatively new to epidemiology, for the quantitative assessment of biases in the results of epidemiological studies. Each uncertainty analysis is situation specific, but usually involves four main steps: (1) specify the target parameter of interest and an equation for its estimator; (2) specify the equation for random and bias effects on the estimator; (3) specify prior probability distributions for the bias parameters; and (4) use Monte-Carlo or analytic techniques to propagate the uncertainty about the bias parameters through the equation, to obtain an approximate posterior probability distribution for the parameter of interest. A basic example is presented illustrating uncertainty analyses for four proportions estimated from a survey of the epidemiological literature.

An updated study of mortality among North American synthetic rubber industry workers.

This study evaluated mortality rates from leukemia and other diseases during the time period 1944 through 1998 among 17,924 men employed in the synthetic rubber industry. In this group, there were 6237 deaths, which is 14% fewer than the 7242 deaths expected based on general population rates. Numbers of observed versus expected deaths (shown hereafter as observed/expected) were 1608/1741 for all cancers combined, including 71/61 for leukemia, 53/53 for non-Hodgkin lymphoma (NHL*), and 26/27 for multiple myeloma.

Exposure-measurement error is frequently ignored when interpreting epidemiologic study results.

Introduction: One important source of error in study results is error in measuring exposures. When interpreting study results, one should consider the impact that exposure-measurement error (EME) might have had on study results.

Methods: To assess how often this consideration is made and the form it takes, journal articles were randomly sampled from original articles appearing in the American Journal of Epidemiology and Epidemiology in 2001, and the International Journal of Epidemiology between December 2000 and October 2001.

Can landscape ecology untangle the complexity of antibiotic resistance?

Bacterial resistance to antibiotics continues to pose a serious threat to human and animal health. Given the considerable spatial and temporal heterogeneity in the distribution of resistance and the factors that affect its evolution, dissemination and persistence, we argue that antibiotic resistance must be viewed as an ecological problem. A fundamental difficulty in assessing the causal relationship between antibiotic use and resistance is the confounding influence of geography: the co-localization of resistant bacterial species with antibiotic use does not necessarily imply causation and could represent the presence of environmental conditions and factors that have independently contributed to the occurrence of resistance.

Chemical exposures in the synthetic rubber industry and lymphohematopoietic cancer mortality.

Objective: This study evaluated the association between exposure to several chemicals and mortality from lymphohematopoietic cancer (LHC) among 16,579 synthetic rubber industry workers who were followed up from 1943 to 1998.

Methods: Poisson regression analyses examined LHC rates in relation to butadiene, styrene, and DMDTC exposure. Models provided maximum likelihood estimates of the relative rate for the contrast between categories of one agent, adjusting for other agents and for additional potential confounders.

Proper interpretation of non-differential misclassification effects: expectations vs observations.

Background: Many investigators write as if non-differential exposure misclassification inevitably leads to a reduction in the strength of an estimated exposure-disease association. Unfortunately, non-differentiality alone is insufficient to guarantee bias towards the null. Furthermore, because bias refers to the average estimate across study repetitions rather than the result of a single study, bias towards the null is insufficient to guarantee that an observed estimate will be an underestimate.

Editorial: Wishful thinking.

As a supplement to our lead editorial, the editors of the new journal, Epidemiologic Perspectives & Innovations, provide a partial list of specific analyses and topic areas they would like to see submitted to the journal.

Occupational exposure to glycol ethers and human congenital malformations.

Objectives: This commentary reviews toxicological information and critically evaluates epidemiological information on the relationship between glycol ethers and congenital malformations.

Methods: The authors identified and assessed toxicological and epidemiological research on glycol ethers used in occupational settings and congenital malformations. Sensitivity analyses evaluated the possible role of methodological problems in explaining the findings of the epidemiological studies.