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In his 1976 paper "Estimability and Estimation in Case-Referent Studies" (Am J Epidemiol. 1976;103(2):226-235), Miettinen weaved together a patchwork of new ideas into a coherent view of case-control studies. His article spurred theoretical development in epidemiologic methods and became a platform for teaching about some key concepts in epidemiologic study design. [ABSTRACT FROM AUTHOR]

Laboratory-based animal research has revealed a number of exposures with multigenerational effects—ones that affect the children and grandchildren of those directly exposed. An important task for epidemiology is to investigate these relationships in human populations. Without the relative control achieved in laboratory settings, however, population-based studies of multigenerational associations have had to use a broader range of study designs. Current strategies to obtain multigenerational data include exploiting birth registries and existing cohort studies, ascertaining exposures within them, and measuring outcomes across multiple generations. In this paper, we describe the methodological challenges inherent to multigenerational studies in human populations. After outlining standard taxonomy to facilitate discussion of study designs and target exposure associations, we highlight the methodological issues, focusing on the interplay between study design, analysis strategy, and the fact that outcomes may be related to family size. In a simulation study, we show that different multigenerational designs lead to estimates of different exposure associations with distinct scientific interpretations. Nevertheless, target associations can be recovered by incorporating (possibly) auxiliary information, and we provide insights into choosing an appropriate target association. Finally, we identify areas requiring further methodological development. [ABSTRACT FROM AUTHOR]

The idea that epidemiologic studies should start from first exposure onward has been advocated in the past few years. The study of incident exposures is contrasted with studies of prevalent exposures in which follow-up may commence after first exposure. The former approach is seen as a hallmark of a good study and necessary for causal inference. We argue that studying incident exposures may be necessary in some situations, but it is not always necessary and is not the preferred option in many instances. Conducting a study involves decisions as to which person-time experience should be included. Although studies of prevalent exposures involve left truncation (missingness on the left), studies of incident exposures may involve right censoring (missingness on the right) and therefore may not be able to assess the long-term effects of exposure. These considerations have consequences for studies of dynamic (open) populations that involve a mixture of prevalent and incident exposures. We argue that studies with prevalent exposures will remain a necessity for epidemiology. The purpose of this paper is to restore the balance between the emphasis on first exposure cohorts and the richness of epidemiologic information obtained when studying prevalent exposures. [ABSTRACT FROM AUTHOR]

Researchers conducting observational studies need to consider 3 types of biases: selection bias, information bias, and confounding bias. A whole arsenal of statistical tools can be used to deal with information and confounding biases. However, methods for addressing selection bias and unmeasured confounding are less developed. In this paper, we propose general bounding formulas for bias, including selection bias and unmeasured confounding. This should help researchers make more prudent interpretations of their (potentially biased) results. [ABSTRACT FROM AUTHOR]

Over the past century, the field of epidemiology has evolved and adapted to changing public health needs. Challenges include newly emerging public health concerns across broad and diverse content areas, new methods, and vast data sources. We recognize the need to engage and educate the next generation of epidemiologists and prepare them to tackle these issues of the 21st century. In this commentary, we suggest a skeleton framework upon which departments of epidemiology should build their curriculum. We propose domains that include applied epidemiology, biological and social determinants of health, communication, creativity and ability to collaborate and lead, statistical methods, and study design. We believe all students should gain skills across these domains to tackle the challenges posed to us. The aim is to train smart thinkers, not technicians, to embrace challenges and move the expanding field of epidemiology forward. [ABSTRACT FROM AUTHOR]

Peer review is central to the process through which epidemiologists generate evidence to inform public health and medical interventions. Reviewers thereby act as critical gatekeepers to high-quality research. They are asked to carefully consider the validity of the proposed work or research findings by paying careful attention to the methodology and critiquing the importance of the insight gained. However, although many have noted problems with the peer-review system for both manuscripts and grant submissions, few solutions have been proposed to improve the process. Quantitative bias analysis encompasses all methods used to quantify the impact of systematic error on estimates of effect in epidemiologic research. Reviewers who insist that quantitative bias analysis be incorporated into the design, conduct, presentation, and interpretation of epidemiologic research could substantially strengthen the process. In the present commentary, we demonstrate how quantitative bias analysis can be used by investigators and authors, reviewers, funding agencies, and editors. By utilizing quantitative bias analysis in the peer-review process, editors can potentially avoid unnecessary rejections, identify key areas for improvement, and improve discussion sections by shifting from speculation on the impact of sources of error to quantification of the impact those sources of bias may have had. [ABSTRACT FROM AUTHOR]

Studying the effect of chronic medication exposure by means of a case-crossover design may result in an upward-biased odds ratio. In this study, our aim was to assess the occurrence of this bias and to evaluate whether it is remedied by including a control group (the case-time-control design). Using Danish data resources from 1995-2012, we conducted case-crossover and case-time-control analyses for 3 medications (statins, insulin, and thyroxine) in relation to 3 outcomes (retinal detachment, wrist fracture, and ischemic stroke), all with assumed null associations. Controls were matched on age, sex, and index date, and exposure over the preceding 12 months was ascertained. For retinal detachment, the case-crossover odds ratio was 1.60 (95% confidence interval (CI): 1.42, 1.80) for statins, 1.40 (95% CI: 1.02, 1.92) for thyroxine, and 1.53 (95% CI: 1.04, 2.24) for insulin. Estimates for the retinal detachment controls were similar, leading to near-null case-time-control estimates for all 3 medication classes. For wrist fracture and stroke, the odds ratios were higher for cases than for controls, and case-time-control odds ratios were consistently above unity, thus implying significant residual bias. In case-crossover studies of medications, contamination by persistent users confers a moderate bias upward, which is partly remedied by using a control group. The optimal strategy for dealing with this problem is currently unknown. [ABSTRACT FROM AUTHOR]

For more than a century, epidemiology has seen major shifts in both focus and methodology. Taking into consideration the explosion of "big data," the advent of more sophisticated data collection and analytical tools, and the increased interest in evidence-based solutions, we present a framework that summarizes 3 fundamental domains of epidemiologic methods that are relevant for the understanding of both historical contributions and future directions in public health. First, the manner in which populations and their follow-up are defined is expanding, with greater interest in online populations whose definition does not fit the usual classification by person, place, and time. Second, traditional data collection methods, such as population-based surveillance and individual interviews, have been supplemented with advances in measurement. From biomarkers to mobile health, innovations in the measurement of exposures and diseases enable refined accuracy of data collection. Lastly, the comparison of populations is at the heart of epidemiologic methodology. Risk factor epidemiology, prediction methods, and causal inference strategies are areas in which the field is continuing to make significant contributions to public health. The framework presented herein articulates the multifaceted ways in which epidemiologic methods make such contributions and can continue to do so as we embark upon the next 100 years. [ABSTRACT FROM AUTHOR]

The comparative new-user design is a principled approach to learning about the relative risks and benefits of starting different treatments in patients who have no history of use of the treatments being studied. Vandenbroucke and Pearce (Am J Epidemiol. 2015;182(10):826-833) discuss some problems inherent in incident exposure designs and argue that epidemiology may be harmed by a rigid requirement that follow-up can only begin at first exposure. In the present counterpoint article, a range of problems in pharmacoepidemiology that do not necessarily require that observation begin at first exposure are discussed. For example, among patients who are past or current users of a medication, we might want to know whether treatment should be augmented, switched, restarted, or discontinued. To answer these questions, a generalization of the new-user design, the treatment decision design, which identifies cohorts anchored at times when treatment decisions are being made, such as the evaluation of laboratory parameters, is discussed. The design aims to provide estimates that are directly relevant to physicians and patients, helping them to better understand the risks and benefits of the different treatment choices that they are considering. [ABSTRACT FROM AUTHOR]

In this issue of the Journal, Keyes and Galea (Am J Epidemiol. 2014;180(7):661–668) propose “7 foundational steps” for introducing epidemiologic methods and concepts to beginners. Keyes and Galea's credo is that the methododological and conceptual components that comprise epidemiology, today scattered in textbook chapters, come together as an integrated and coherent methodological corpus in the process of designing studies. Thus, they expound, the process of designing studies should be the core of teaching epidemiology. Two aspects of their 7-steps-to-epidemiology, do-it-yourself user manual stand out as novel: 1) the approach, because of its emphasis on modern epidemiology's causal framework of a dynamic population in a steady state evolving across time, and 2) the ambition to teach modern epidemiology in introductory courses, instead of the popular mix of classical and modern epidemiology that is often used today to keep introductory courses simple. Both aspects are of potentially great significance for our discipline. [ABSTRACT FROM PUBLISHER]

The article presents the authors' response to the comments by MA Hernan and MA Brookhart regarding incident and prevalent exposures and causal inference where they argued that limiting studies to persons who are followed from first exposure onward may damage epidemiology. They cite their disagreements when Hernan sees the purpose of epidemiologic research as particularly to support practice and limit epidemiology to studies that resemble randomized trials.