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Many examples of the use of real-world data in the area of pharmacoepidemiology include "big data," such as insurance claims, medical records, or hospital discharge databases. However, "big" is not always better, particularly when studying outcomes with narrow windows of etiologic relevance. Birth defects are such an outcome, for which specificity of exposure timing is critical. Studies with primary data collection can be designed to query details about the timing of medication use, as well as type, dose, frequency, duration, and indication, that can better characterize the "real world." Because birth defects are rare, etiologic studies are typically case‑control in design, like the National Birth Defects Prevention Study, Birth Defects Study to Evaluate Pregnancy Exposures, and Slone Birth Defects Study. Recall bias can be a concern, but the ability to collect detailed information about both prescription and over-the-counter medication use and other exposures such as diet, family history, and sociodemographic factors is a distinct advantage over claims and medical record data sources. Case‑control studies with primary data collection are essential to advancing the pharmacoepidemiology of birth defects. This article is part of a Special Collection on Pharmacoepidemiology. [ABSTRACT FROM AUTHOR]

Difference-in-differences and synthetic control methods have become common study designs for evaluating the effects of changes in policies, including health policies. They also have potential for providing real-world effectiveness and safety evidence in pharmacoepidemiology. To effectively add to the toolkit of the field, however, designs—including both their benefits and drawbacks—must be well understood. Quasi-experimental designs provide an opportunity to estimate the average treatment effect on the treated without requiring the measurement of all possible confounding factors, and to assess population-level effects. This requires, however, other key assumptions, including the parallel trends or stable weighting assumptions, a lack of other concurrent events that could alter time trends, and an absence of contamination between exposed and unexposed units. The targeted estimands are also highly specific to the settings of the study, and combining across units or time periods can be challenging. Case studies are presented for 3 vaccine evaluation studies, showcasing some of these challenges and opportunities in a specific field of pharmacoepidemiology. These methods provide feasible and valuable sources of evidence in various pharmacoepidemiologic settings and can be improved through research to identify and weigh the advantages and disadvantages in those settings. This article is part of a Special Collection on Pharmacoepidemiology. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has highlighted the need to use infection testing databases to rapidly estimate effectiveness of prior infection in preventing reinfection (⁠|$P{E}_S$|⁠) by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Mathematical modeling was used to demonstrate a theoretical foundation for applicability of the test-negative, case–control study design to derive |$P{E}_S$|⁠. Apart from the very early phase of an epidemic, the difference between the test-negative estimate for |$P{E}_S$| and true value of |$P{E}_S$| was minimal and became negligible as the epidemic progressed. The test-negative design provided robust estimation of |$P{E}_S$| and its waning. Assuming that only 25% of prior infections are documented, misclassification of prior infection status underestimated |$P{E}_S$|⁠ , but the underestimate was considerable only when > 50% of the population was ever infected. Misclassification of latent infection, misclassification of current active infection, and scale-up of vaccination all resulted in negligible bias in estimated |$P{E}_S$|⁠. The test-negative design was applied to national-level testing data in Qatar to estimate |$P{E}_S$| for SARS-CoV-2. |$P{E}_S$| against SARS-CoV-2 Alpha and Beta variants was estimated at 97.0% (95% CI, 93.6-98.6) and 85.5% (95% CI, 82.4-88.1), respectively. These estimates were validated using a cohort study design. The test-negative design offers a feasible, robust method to estimate protection from prior infection in preventing reinfection. [ABSTRACT FROM AUTHOR]

Epidemiologists are attempting to address research questions of increasing complexity by developing novel methods for combining information from diverse sources. Cole et al. (Am J Epidemiol. 2023;192(3)467–474) provide 2 examples of the process of combining information to draw inferences about a population proportion. In this commentary, we consider combining information to learn about a target population as an epidemiologic activity and distinguish it from more conventional meta-analyses. We examine possible rationales for combining information and discuss broad methodological considerations, with an emphasis on study design, assumptions, and sources of uncertainty. [ABSTRACT FROM AUTHOR]

We pay tribute to Marshall Joffe, PhD, and his substantial contributions to the field of causal inference with focus in biostatistics and epidemiology. By compiling narratives written by us, his colleagues, we not only present highlights of Marshall's research and their significance for causal inference but also offer a portrayal of Marshall's personal accomplishments and character. Our discussion of Marshall's research notably includes (but is not limited to) handling of posttreatment variables such as noncompliance, employing G-estimation for treatment effects on failure-time outcomes, estimating effects of time-varying exposures subject to time-dependent confounding, and developing a causal framework for case-control studies. We also provide a description of some of Marshall's unpublished work, which is accompanied by a bonus anecdote. We discuss future research directions related to Marshall's research. While Marshall's impact in causal inference and the world outside of it cannot be wholly captured by our words, we hope nonetheless to present some of what he has done for our field and what he has meant to us and to his loved ones. [ABSTRACT FROM AUTHOR]

Uses of real-world data in drug safety and effectiveness studies are often challenged by various sources of bias. We undertook a systematic search of the published literature through September 2020 to evaluate the state of use and utility of negative controls to address bias in pharmacoepidemiologic studies. Two reviewers independently evaluated study eligibility and abstracted data. Our search identified 184 eligible studies for inclusion. Cohort studies (115, 63%) and administrative data (114, 62%) were, respectively, the most common study design and data type used. Most studies used negative control outcomes (91, 50%), and for most studies the target source of bias was unmeasured confounding (93, 51%). We identified 4 utility domains of negative controls: 1) bias detection (149, 81%), 2) bias correction (16, 9%), 3) P -value calibration (8, 4%), and 4) performance assessment of different methods used in drug safety studies (31, 17%). The most popular methodologies used were the 95% confidence interval and P -value calibration. In addition, we identified 2 reference sets with structured steps to check the causality assumption of the negative control. While negative controls are powerful tools in bias detection, we found many studies lacked checking the underlying assumptions. This article is part of a Special Collection on Pharmacoepidemiology. [ABSTRACT FROM AUTHOR]

Methods for extending—generalizing or transporting—inferences from a randomized trial to a target population involve conditioning on a large set of covariates that is sufficient for rendering the randomized and nonrandomized groups exchangeable. Yet, decision makers are often interested in examining treatment effects in subgroups of the target population defined in terms of only a few discrete covariates. Here, we propose methods for estimating subgroup-specific potential outcome means and average treatment effects in generalizability and transportability analyses, using outcome model--based (g-formula), weighting, and augmented weighting estimators. We consider estimating subgroup-specific average treatment effects in the target population and its nonrandomized subset, and we provide methods that are appropriate both for nested and non-nested trial designs. As an illustration, we apply the methods to data from the Coronary Artery Surgery Study (North America, 1975–1996) to compare the effect of surgery plus medical therapy versus medical therapy alone for chronic coronary artery disease in subgroups defined by history of myocardial infarction. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) vaccines are highly efficacious at preventing symptomatic infection, severe disease, and death. Most of the evidence that COVID-19 vaccines also reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is based on retrospective, observational studies. Specifically, an increasing number of studies are evaluating vaccine effectiveness against the secondary attack rate of SARS-CoV-2 using data available in existing health-care databases or contact-tracing databases. Since these types of databases were designed for clinical diagnosis or management of COVID-19, they are limited in their ability to provide accurate information on infection, infection timing, and transmission events. We highlight challenges with using existing databases to identify transmission units and confirm potential SARS-CoV-2 transmission events. We discuss the impact of common diagnostic testing strategies, including event-prompted and infrequent testing, and illustrate their potential biases in estimating vaccine effectiveness against the secondary attack rate of SARS-CoV-2. We articulate the need for prospective observational studies of vaccine effectiveness against the SARS-CoV-2 secondary attack rate, and we provide design and reporting considerations for studies using retrospective databases. [ABSTRACT FROM AUTHOR]

Qualitative research methods, while rising in popularity, are still a relatively underutilized tool in public health research. Usually reserved for small samples, qualitative research techniques have the potential to enhance insights gained from large questionnaires and cohort studies, both deepening the interpretation of quantitative data and generating novel hypotheses that might otherwise be missed by standard approaches; this is especially true where exposures and outcomes are new, understudied, or rapidly changing, as in a pandemic. However, methods for the conduct of qualitative research within large samples are underdeveloped. Here, we describe a novel method of applying qualitative research methods to free-text comments collected in a large epidemiologic questionnaire. Specifically, this method includes: 1) a hierarchical system of coding through content analysis; 2) a qualitative data management application; and 3) an adaptation of Cohen's κ and percent agreement statistics for use by a team of coders, applying multiple codes per record from a large codebook. The methods outlined in this paper may help direct future applications of qualitative and mixed methods within large cohort studies. [ABSTRACT FROM AUTHOR]

We consider the problem of designing a prospective randomized trial in which the outcome data will be self-reported and will involve sensitive topics. Our interest is in how a researcher can adequately power her study when some respondents misreport the binary outcome of interest. To correct the power calculations, we first obtain expressions for the bias and variance induced by misreporting. We model the problem by assuming each individual in our study is a member of one "reporting class": a true-reporter, false-reporter, never-reporter, or always-reporter. We show that the joint distribution of reporting classes and "response classes" (characterizing individuals' response to the treatment) will exactly define the error terms for our causal estimate. We propose a novel procedure for determining adequate sample sizes under the worst-case power corresponding to a given level of misreporting. Our problem is motivated by prior experience implementing a randomized controlled trial of a sexual-violence prevention program among adolescent girls in Kenya. [ABSTRACT FROM AUTHOR]

Starting in the 2010s, researchers in the experimental social sciences rapidly began to adopt increasingly open and reproducible scientific practices. These practices include publicly sharing deidentified data when possible, sharing analytical code, and preregistering study protocols. Empirical evidence from the social sciences suggests such practices are feasible, can improve analytical reproducibility, and can reduce selective reporting. In academic epidemiology, adoption of open-science practices has been slower than in the social sciences (with some notable exceptions, such as registering clinical trials). Epidemiologic studies are often large, complex, conceived after data have already been collected, and difficult to replicate directly by collecting new data. These characteristics make it especially important to ensure their integrity and analytical reproducibility. Open-science practices can also pay immediate dividends to researchers' own work by clarifying scientific reasoning and encouraging well-documented, organized workflows. We consider how established epidemiologists and early-career researchers alike can help midwife a culture of open science in epidemiology through their research practices, mentorship, and editorial activities. [ABSTRACT FROM AUTHOR]

Because dementia is progressive, incident cases are on average milder than prevalent cases, affecting the performance of cognitive tests and questions on functional limitations (i.e. cognition/functional limitation items) used for dementia assessment. Longitudinal studies assess incident cases, while cross-sectional studies assess prevalent cases, but differences are not typically considered when researchers select items to include in studies. We used longitudinal data from the Religious Orders Study and Memory and Aging Project (ROSMAP) (n  = 3,446) collected between 1994 and 2021 to characterize differences in associations between items (cognition: 35 items; functional limitations: 14 items) and incident or prevalent dementia using multinomial regression models with generalized estimating equations, controlling for ROSMAP cohort (Religious Orders Study or Memory and Aging Project), age, sex, race, and education. The association between a given item and incident dementia was significantly weaker than the association between the same item and prevalent dementia for 46 of 49 items. However, there was variability, with larger differences for some items, including naming a pencil (prevalence odds ratio = 0.02 (95% confidence interval: 0.02, 0.03); incidence odds ratio = 0.10 (95% confidence interval: 0.06, 0.17); P for difference < 0.001). Important differences exist in the performance of cognition/functional limitation items for measurement of incident versus prevalent dementia. Differences can inform the choice of items for cross-sectional studies of prevalent cases or longitudinal studies of incident cases, leading to reduced misclassification and increased statistical power. [ABSTRACT FROM AUTHOR]

When individuals self-select (or are selected) into a study based on factors that influence the outcome, conclusions may not generalize to the full population. To compensate for this, results may be adjusted, for example, by standardization on the set of common causes of participation and outcome. Although such standardization is useful in some contexts, the common causes of participation and outcome may in practice not be fully observed. Instead, the researcher may have access to one or several variables related to the common causes, that is, to proxies for the common causes. This article defines and examines different types of proxy variables and shows how these can be used to obtain generalizable study results. First of all, the researcher may exploit proxies that influence only participation or outcome but which still allow for perfect generalizability by rendering participation and outcome conditionally independent. Further, generalizability can be achieved by leveraging 2 proxies, one of which is allowed to influence participation and one of which is allowed to influence the outcome, even if participation and outcome do not become independent conditional on these. Finally, approximate generalizability may be obtained by exploiting a single proxy that does not itself influence participation or outcome. [ABSTRACT FROM AUTHOR]

"Fusion" study designs combine data from different sources to answer questions that could not be answered (as well) by subsets of the data. Studies that augment main study data with validation data, as in measurement-error correction studies or generalizability studies, are examples of fusion designs. Fusion estimators, here solutions to stacked estimating functions, produce consistent answers to identified research questions using data from fusion designs. In this paper, we describe a pair of examples of fusion designs and estimators, one where we generalize a proportion to a target population and one where we correct measurement error in a proportion. For each case, we present an example motivated by human immunodeficiency virus research and summarize results from simulation studies. Simulations demonstrate that the fusion estimators provide approximately unbiased results with appropriate 95% confidence interval coverage. Fusion estimators can be used to appropriately combine data in answering important questions that benefit from multiple sources of information. [ABSTRACT FROM AUTHOR]

There is a compelling need to evaluate the real-world health effects of medical products outside of tightly controlled preapproval clinical trials. This is done through pharmacoepidemiology, which is the study of the health effects of medical products (including drugs, biologicals, and medical devices and diagnostics) in populations, often using nonrandomized designs. Recent developments in pharmacoepidemiology span changes in the focus of research questions, research designs, data used, and statistical analysis methods. Developments in these areas are thought to improve the value of the evidence produced by such studies, and are prompting greater use of real-world evidence to inform clinical, regulatory, and reimbursement decisions. [ABSTRACT FROM AUTHOR]

The article discusses the importance of testing Cox proportional hazards model assumptions in medical studies. Topics mentioned include the assumption of the absence of statistical interaction between the treatment and time scale in the model, the analogy of proportional hazards with logistic regression, the presence of variations in disease susceptibility, and the use of machine learning methods if the Cox proportional hazards model is inappropriate.

The authors offer a response to the article "Invited Commentary: Combining Information to Answer Epidemiologic Questions about a Target Population" by I. J. Dahabreh. They talk about the data combined in fusion study designs, case on estimating a misclassified and transported proportion, and introduction of the oncept of learning functions.

In this paper, we propose a framework for thinking through the design and conduct of descriptive epidemiologic studies. A well-defined descriptive question aims to quantify and characterize some feature of the health of a population and must clearly state: 1) the target population, characterized by person and place, and anchored in time; 2) the outcome, event, or health state or characteristic; and 3) the measure of occurrence that will be used to summarize the outcome (e.g. incidence, prevalence, average time to event, etc.). Additionally, 4) any auxiliary variables will be prespecified and their roles as stratification factors (to characterize the outcome distribution) or nuisance variables (to be standardized over) will be stated. We illustrate application of this framework to describe the prevalence of viral suppression on December 31, 2019, among people living with human immunodeficiency virus (HIV) who had been linked to HIV care in the United States. Application of this framework highlights biases that may arise from missing data, especially 1) differences between the target population and the analytical sample; 2) measurement error; 3) competing events, late entries, loss to follow-up, and inappropriate interpretation of the chosen measure of outcome occurrence; and 4) inappropriate adjustment. [ABSTRACT FROM AUTHOR]

In multiply matched case-control studies, a number of cases and controls may be included in each matched set. However, when per-participant costs between cases and controls differ, investigators should be aware of how the numbers of cases and controls per matched set affect the overall total study cost. Traditional statistical approaches to designing case-control studies do not account for study costs. Given an effect size, the power to detect differences is typically a function of the numbers of cases and controls within each matched set. Therefore, the same level of statistical power will be achieved based on various combinations of the numbers of cases and controls. Typical matched case-control studies match a case to a number of controls by levels of 1 or more known factors. Several authors have shown that for study designs with 1 case per matched set, the optimal number of controls within each matched set that minimizes the total study cost is the square root of the ratio of the cost of a case to the cost of a control. Herein, we extend this result to the setting of a multiply matched case-control study design, when 1 or more cases are matched to controls within each matched set. A Shiny web application implementation of the proposed methods is presented. [ABSTRACT FROM AUTHOR]

Active comparator studies are increasingly common, particularly in pharmacoepidemiology. In such studies, the parameter of interest is a contrast (difference or ratio) in the outcome risks between the treatment of interest and the selected active comparator. While it may appear treatment is dichotomous, treatment is actually polytomous as there are at least 3 levels: no treatment, the treatment of interest, and the active comparator. Because misclassification may occur between any of these groups, independent nondifferential treatment misclassification may not be toward the null (as expected with a dichotomous treatment). In this work, we describe bias from independent nondifferential treatment misclassification in active comparator studies with a focus on misclassification that occurs between each active treatment and no treatment. We derive equations for bias in the estimated outcome risks, risk difference, and risk ratio, and we provide bias correction equations that produce unbiased estimates, in expectation. Using data obtained from US insurance claims data, we present a hypothetical comparative safety study of antibiotic treatment to illustrate factors that influence bias and provide an example probabilistic bias analysis using our derived bias correction equations. [ABSTRACT FROM AUTHOR]

In this issue of the Journal , Savitz and Wellenius (Am J Epidemiol. 2023;192(4):514–516) discuss the contribution of cross-sectional studies to causal inference when the data are used to address etiological research questions. We elaborate on their thoughts with a discussion of the conditions needed for addressing etiology with the cross-sectional design, using a modern causal inference lens. [ABSTRACT FROM AUTHOR]

Cross-sectional studies—often defined as those in which exposure and outcome are assessed at the same point in time—are frequently viewed as minimally informative for causal inference. While cross-sectional studies may be susceptible to reverse causality, may be limited to assessment of disease prevalence rather than incidence, or may only provide estimates of current rather than past exposures, not all cross-sectional studies suffer these limitations. Moreover, none of these concerns are unique to or inherent in the structure of a cross-sectional study. Regardless of when exposure and disease were ascertained relative to one another, a cross-sectional study may provide insights into the causal effects of exposure on disease incidence. Simply labeling a study as "cross-sectional" and assuming that 1 or more of these limitations exist and are materially important fails to recognize the need for a more nuanced assessment and risks discarding evidence that may be useful in assessing causal relationships. [ABSTRACT FROM AUTHOR]

The Collaborative Cohort of Cohorts for COVID-19 Research (C4R) is a national prospective study of adults comprising 14 established US prospective cohort studies. Starting as early as 1971, investigators in the C4R cohort studies have collected data on clinical and subclinical diseases and their risk factors, including behavior, cognition, biomarkers, and social determinants of health. C4R links this pre–coronavirus disease 2019 (COVID-19) phenotyping to information on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and acute and postacute COVID-related illness. C4R is largely population-based, has an age range of 18–108 years, and reflects the racial, ethnic, socioeconomic, and geographic diversity of the United States. C4R ascertains SARS-CoV-2 infection and COVID-19 illness using standardized questionnaires, ascertainment of COVID-related hospitalizations and deaths, and a SARS-CoV-2 serosurvey conducted via dried blood spots. Master protocols leverage existing robust retention rates for telephone and in-person examinations and high-quality event surveillance. Extensive prepandemic data minimize referral, survival, and recall bias. Data are harmonized with research-quality phenotyping unmatched by clinical and survey-based studies; these data will be pooled and shared widely to expedite collaboration and scientific findings. This resource will allow evaluation of risk and resilience factors for COVID-19 severity and outcomes, including postacute sequelae, and assessment of the social and behavioral impact of the pandemic on long-term health trajectories. [ABSTRACT FROM AUTHOR]

In this paper, we consider methods for generating draws of a binary random variable whose expectation conditional on covariates follows a logistic regression model with known covariate coefficients. We examine approximations for finding a "balancing intercept," that is, a value for the intercept of the logistic model that leads to a desired marginal expectation for the binary random variable. We show that a recently proposed analytical approximation can produce inaccurate results, especially when targeting more extreme marginal expectations or when the linear predictor of the regression model has high variance. We then formulate the balancing intercept as a solution to an integral equation, implement a numerical approximation for solving the equation based on Monte Carlo methods, and show that the approximation works well in practice. Our approach to the basic problem of the balancing intercept provides an example of a broadly applicable strategy for formulating and solving problems that arise in the design of simulation studies used to evaluate or teach epidemiologic methods. [ABSTRACT FROM AUTHOR]

Data collected from a validation substudy permit calculation of a bias-adjusted estimate of effect that is expected to equal the estimate that would have been observed had the gold standard measurement been available for the entire study population. In this paper, we develop and apply a framework for adaptive validation to determine when sufficient validation data have been collected to yield a bias-adjusted effect estimate with a prespecified level of precision. Prespecified levels of precision are decided a priori by the investigator, based on the precision of the conventional estimate and allowing for wider confidence intervals that would still be substantively meaningful. We further present an applied example of the use of this method to address exposure misclassification in a study of transmasculine/transfeminine youth and self-harm. Our method provides a novel approach to effective and efficient estimation of classification parameters as validation data accrue, with emphasis on the precision of the bias-adjusted estimate. This method can be applied within the context of any parent epidemiologic study design in which validation data will be collected and modified to meet alternative criteria given specific study or validation study objectives. [ABSTRACT FROM AUTHOR]

This historical note focuses on contributions to the concepts, design, methods, and conduct of clinical trials from the 1960s planning and findings of a feasibility study that was little reported and largely unrecognized: the Pilot Study of Prescribed Physical Activity for the Primary Prevention of Ischemic Heart Disease (1963–1968). In contrast, complementary findings from the 1960s National Diet-Heart Feasibility Study (1962–1968) were fully reported, widely recognized, and well-analyzed by historians in context of the rapid evolution in chronic disease epidemiology and clinical trials following World War II. The experience of these thoughtfully planned and rigorously conducted pilot studies changed the career paths of their planners and investigators and shaped the form and direction of subsequent research on the causes, treatment, and prevention of heart attack. The 2 studies were both fruit and seed of a consortium of experts that evolved in reaction to new questions and findings about the midcentury coronary disease epidemic. The contributions of each study, and of this broader academy of pioneer thinkers, are essential elements in the history of cardiovascular disease epidemiology, prevention research, and health policy. [ABSTRACT FROM AUTHOR]

Social distancing measures introduced on March 12, 2020, in Denmark during the COVID-19 pandemic may affect non–COVID-19 admissions for severe acute exacerbation of chronic obstructive pulmonary disease (s-AECOPD). We compared rates of s-AECOPD in a nationwide, observational, semi-experimental cohort study using data from all Danish inhabitants between calendar week 1 through 25 in 2019 and 2020. In a sub-cohort of patients with chronic obstructive pulmonary disease, we examined incidence of s-AECOPD, admissions to an intensive care unit, and all-cause mortality. A total of 3.0 million inhabitants aged ≥40 years, corresponding to 3.0 million person-years, were followed for s-AECOPD. In the social distancing period in 2020, there were 6,212 incidents of s-AECOPD, compared with 11,260 incidents in 2019, resulting in a 45% relative risk reduction. In the cohort with chronic obstructive pulmonary disease (n  = 16,675), we observed a lower risk of s-AECOPD in the social distancing period (subdistribution hazard ratio (HR) = 0.34, 95% confidence interval (CI): 0.33, 0.36; absolute risk: 25.4% in 2020 and 42.8% in 2019). The risk of admissions to an intensive care unit was reduced (subdistribution HR = 0.64, 95% CI: 0.47, 0.87), as was all-cause mortality (HR = 0.83, 95% CI: 0.76, 0.90). Overall, the social distancing period was associated with a significant risk reduction for hospital admittance with s-AECOPD. [ABSTRACT FROM AUTHOR]

In the article, the authors present their study on the effects of a patient's sex and race in the diagnosis and clinical decision making in patients with systemic lupus erythematosus (SLE). Topics include the symptoms of SLE like joint pain, chest pain, fever and proteinuria, as well as the key role played by physicians in the identification of SLE and starting the appropriate medication or treatment.

An increasing number of recent studies have suggested that doubly robust estimators with cross-fitting should be used when estimating causal effects with machine learning methods. However, not all existing programs that implement doubly robust estimators support machine learning methods and cross-fitting, or provide estimates on multiplicative scales. To address these needs, we developed AIPW , a software package implementing augmented inverse probability weighting (AIPW) estimation of average causal effects in R (R Foundation for Statistical Computing, Vienna, Austria). Key features of the AIPW package include cross-fitting and flexible covariate adjustment for observational studies and randomized controlled trials (RCTs). In this paper, we use a simulated RCT to illustrate implementation of the AIPW estimator. We also perform a simulation study to evaluate the performance of the AIPW package compared with other doubly robust implementations, including CausalGAM , npcausal , tmle , and tmle3. Our simulation showed that the AIPW package yields performance comparable to that of other programs. Furthermore, we also found that cross-fitting substantively decreases the bias and improves the confidence interval coverage for doubly robust estimators fitted with machine learning algorithms. Our findings suggest that the AIPW package can be a useful tool for estimating average causal effects with machine learning methods in RCTs and observational studies. [ABSTRACT FROM AUTHOR]

Epidemiologists sometimes use external sources of variation to explore highly confounded exposure-outcome relationships or exposures that cannot be randomized. These exogenous sources of variation, or natural experiments, are sometimes proposed as instrumental variables to examine the effects of given exposures on given outcomes. Previous epidemiologic studies have applied this technique using famines, earthquakes, weather events, and previous pandemics as exogenous sources of variation for other exposures; interest in applying this technique using the current severe acute respiratory system coronavirus 2 (SARS-CoV-2) pandemic is already documented. Yet large-scale events like these likely have broad and complicated impacts on human health, which almost certainly violates the exclusion restriction assumption of instrumental variable analyses. We review the assumptions of instrumental variable analyses, highlight previous applications of this method with respect to natural experiments with broad impacts or "shocks," and discuss how these relate to our current observations of the SARS-CoV-2 pandemic. While we encourage thorough investigation of the broad impacts of the SARS-CoV-2 pandemic on human health, we caution against its widespread use as an instrumental variable to study other exposures of interest. [ABSTRACT FROM AUTHOR]

We developed and validated a synthetic cohort approach to examine numbers of cardiovascular risk factors (CRFs) and adverse clinical events, including incident cardiovascular disease and all-cause mortality, across the life span from ages 20 years to 90 years. The current analysis included 40,875 participants from 7 large, population-based longitudinal epidemiologic studies (1948–2016). On the basis of a joint multilevel imputation model, we multiply imputed each participant's life-span numbers of CRFs and events using available records. To validate the imputed values, we partially removed the observed data and then compared the imputed and observed values. The complete life-span synthetic data set reflected the original observed data trends well. In our validation sample, the distributions of imputed CRFs and events were close to the observed distributions but with less variability. Bland-Altman plots indicated that there was a slightly negative trend in general, and the agreement bias was relatively small for the continuous CRFs. The hypothetical linear regression model suggested that the relationships between the CRFs and events were preserved in the imputed data set. This approach generated valid estimates of CRFs and events across the life span for African-American and White participants. The synthetic cohort may be sufficiently accurate to be useful in assessing the origins and timing of accumulating cardiovascular risk that can inform efforts to avoid cardiovascular disease development. [ABSTRACT FROM AUTHOR]

Serological surveys can provide evidence of cases that were not previously detected, depict the spectrum of disease severity, and estimate the proportion of asymptomatic infections. To capture these parameters, survey sample sizes may need to be very large, especially when the overall infection rate is still low. Therefore, we propose the use of "snowball sampling" to enrich serological surveys by testing contacts of infected persons identified in the early stages of an outbreak. For future emerging pandemics, this observational study sampling design can answer many key questions, such as estimation of the asymptomatic proportion of all infected cases, the probability of a given clinical presentation for a seropositive individual, or the association between characteristics of either the host or the infection and seropositivity among contacts of index individuals. We provide examples, in the context of the coronavirus disease 2019 (COVID-19) pandemic, of studies and analysis methods that use a snowball sample and perform a simulation study that demonstrates scenarios where snowball sampling can answer these questions more efficiently than other sampling schemes. We hope such study designs can be applied to provide valuable information to slow the present pandemic as it enters its next stage and in early stages of future pandemics. [ABSTRACT FROM AUTHOR]

The test-negative study design is often used to estimate vaccine effectiveness in influenza studies, but it has also been proposed in the context of other infectious diseases, such as cholera, dengue, or Ebola. It was introduced as a variation of the case-control design, in an attempt to reduce confounding bias due to health-care–seeking behavior, and has quickly gained popularity because of its logistic advantages. However, examination of the directed acyclic graphs that describe the test-negative design reveals that without strong assumptions, the estimated odds ratio derived under this sampling mechanism is not collapsible over the selection variable, such that the results obtained for the sampled individuals cannot be generalized to the whole population. In this paper, we show that adjustment for severity of disease can reduce this bias and, under certain assumptions, makes it possible to unbiasedly estimate a causal odds ratio. We support our findings with extensive simulations and discuss them in the context of recently published cholera test-negative studies of the effectiveness of cholera vaccines. [ABSTRACT FROM AUTHOR]

In this article, we examine study designs for extending (generalizing or transporting) causal inferences from a randomized trial to a target population. Specifically, we consider nested trial designs, where randomized individuals are nested within a sample from the target population, and nonnested trial designs, including composite data-set designs, where observations from a randomized trial are combined with those from a separately obtained sample of nonrandomized individuals from the target population. We show that the counterfactual quantities that can be identified in each study design depend on what is known about the probability of sampling nonrandomized individuals. For each study design, we examine identification of counterfactual outcome means via the g-formula and inverse probability weighting. Last, we explore the implications of the sampling properties underlying the designs for the identification and estimation of the probability of trial participation. [ABSTRACT FROM AUTHOR]

Subgroup analyses of randomized controlled trials guide resource allocation and implementation of new interventions by identifying groups of individuals who are likely to benefit most from the intervention. Unfortunately, trial populations are rarely representative of the target populations of public health or clinical interest. Unless the relevant differences between trial and target populations are accounted for, subgroup results from trials might not reflect which groups in the target population will benefit most from the intervention. Transportability provides a rigorous framework for applying results derived in potentially highly selected study populations to external target populations. The method requires that researchers measure and adjust for all variables that 1) modify the effect of interest and 2) differ between the target and trial populations. To date, applications of transportability have focused on the external validity of overall study results and understanding within-trial heterogeneity; however, this approach has not yet been used for subgroup analyses of trials. Through an example from the Iniciativa Profilaxis Pre-Exposición (iPrEx) study (multiple countries, 2007–2010) of preexposure prophylaxis for human immunodeficiency virus, we illustrate how transporting subgroup analyses can produce target-specific subgroup effect estimates and numbers needed to treat. This approach could lead to more tailored and accurate guidance for resource allocation and cost-effectiveness analyses. [ABSTRACT FROM AUTHOR]

The prevalent new-user design includes a broader study population than the traditional new-user approach that is frequently used in pharmacoepidemiologic research. In an article appearing in this issue (Am J Epidemiol. 2021;190(7):1341–1348) , Webster-Clark et al. describe the treatment initiator types included in the prevalent new-user design and contrast the causal questions assessed using a prevalent new-user design versus a new-user design. They further applied a series of simulation studies showing the importance of accounting for treatment history in addition to time since initiation of the comparator in the prevalent new-user design. In this commentary, we put their findings in the broader context with a discussion of the strengths and limitations of the prevalent new-user design and settings where it would be most useful. The prevalent new-user design and new-user design both address unique questions of clinical and public health importance. Real-world evidence generated by pharmacoepidemiologic research is increasingly being used by regulators and other knowledge users to inform their decision-making. Understanding the causal questions addressed by different designs is crucial in this process; the study by Webster-Clark et al. represents an important step in addressing this issue. [ABSTRACT FROM AUTHOR]

New-user designs restricting to treatment initiators have become the preferred design for studying drug comparative safety and effectiveness using nonexperimental data. This design reduces confounding by indication and healthy-adherer bias at the cost of smaller study sizes and reduced external validity, particularly when assessing a newly approved treatment compared with standard treatment. The prevalent new-user design includes adopters of a new treatment who switched from or previously used standard treatment (i.e. the comparator), expanding study sample size and potentially broadening the study population for inference. Previous work has suggested the use of time-conditional propensity-score matching to mitigate prevalent user bias. In this study, we describe 3 "types" of initiators of a treatment: new users, direct switchers, and delayed switchers. Using these initiator types, we articulate the causal questions answered by the prevalent new-user design and compare them with those answered by the new-user design. We then show, using simulation, how conditioning on time since initiating the comparator (rather than full treatment history) can still result in a biased estimate of the treatment effect. When implemented properly, the prevalent new-user design estimates new and important causal effects distinct from the new-user design. [ABSTRACT FROM AUTHOR]

It is of critical importance to estimate changing disease-transmission rates and their dependence on population mobility. A common approach to this problem involves fitting daily transmission rates using a susceptible-exposed-infected-recovered-(SEIR) model (regularizing to avoid overfitting) and then computing the relationship between the estimated transmission rate and mobility. Unfortunately, there are often several very different transmission-rate trajectories that can fit the reported cases well, meaning that the choice of regularization determines the final solution (and thus the mobility–transmission rate relationship) selected by the SEIR model. Moreover, the classical approaches to regularization—penalizing the derivative of the transmission rate trajectory—do not correspond to realistic properties of pandemic spread. Consequently, models fitted using derivative-based regularization are often biased toward underestimating the current transmission rate and future deaths. In this work, we propose mobility-driven regularization of the SEIR transmission rate trajectory. This method rectifies the artificial regularization problem, produces more accurate and unbiased forecasts of future deaths, and estimates a highly interpretable relationship between mobility and the transmission rate. For this analysis, mobility data related to the coronavirus disease 2019 pandemic was collected by Safegraph (San Francisco, California) from major US cities between March and August 2020. [ABSTRACT FROM AUTHOR]

Directed acyclic graphs (DAGs) have had a major impact on the field of epidemiology by providing straightforward graphical rules for determining when estimates are expected to lack causally interpretable internal validity. Much less attention has been paid, however, to what DAGs can tell researchers about effect measure modification and external validity. In this work, we describe 2 rules based on DAGs related to effect measure modification. Rule 1 states that if a variable, |$P$|⁠ , is conditionally independent of an outcome, |$Y$|⁠ , within levels of a treatment, |$X$|⁠ , then |$P$| is not an effect measure modifier for the effect of |$X$| on |$Y$| on any scale. Rule 2 states that if |$P$| is not conditionally independent of |$Y$| within levels of |$X$|⁠ , and there are open causal paths from |$X$| to |$Y$| within levels of |$P$|⁠ , then |$P$| is an effect measure modifier for the effect of |$X$| on |$Y$| on at least 1 scale (given no exact cancelation of associations). We then show how Rule 1 can be used to identify sufficient adjustment sets to generalize nested trials studying the effect of |$X$| on |$Y$| to the total source population or to those who did not participate in the trial. [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 coronavirus disease 2019 (COVID-19) pandemic revealed and exacerbated existing social and economic health disparities, and actionable epidemiologic evidence is needed to identify potential vulnerability factors to help inform targeted responses. In this commentary, methodological challenges and opportunities regarding the links between air pollution and COVID-19 are discussed with a focus on 2 factors: 1) the role of differential exposure to air pollution across populations as an explanation for spatiotemporal variability of the epidemic spread and resultant mortality; and 2) the indirect impacts of interventions to control COVID-19 person-to-person spread treated as natural experiments on air pollution and population health. I first discuss the potential mechanisms between exposure to air pollution and COVID-19 and the opportunity to clearly formulate causal questions of interest through the target trial framework. Then, I discuss challenges regarding the use of quasiexperimental designs that capitalize on the differential timing of COVID-19 policies including the selection of control groups and potential violations of the common shock assumption. Finally, I discuss environmental justice implications of this many-headed beast of a crisis. [ABSTRACT FROM AUTHOR]

Diet, lifestyle, and psychosocial factors might influence fertility for men and women, although evidence is mixed, and couple-based approaches are needed for assessing associations with reproductive outcomes. The Impact of Diet, Exercise, and Lifestyle (IDEAL) on Fertility Study is a prospective cohort with contemporaneous detailed follow-up of female partners of men enrolled in the Folic Acid and Zinc Supplementation Trial studying couples seeking infertility treatment (2016–2019). Follow-up of men continued for 6 months, while female partners were followed for 9 months while attempting pregnancy and throughout any resulting pregnancy (up to 18 months). Longitudinal data on diet, physical activity (including measurement via wearable device), sleep, and stress were captured at multiple study visits during this follow-up. A subset of women (IDEALplus) also completed daily journals and a body fat assessment via dual-energy x-ray absorptiometry. IDEAL enrolled 920 women, and IDEALPlus enrolled 218. We demonstrated the ability to enroll women in a prospective cohort study contemporaneous to a partner-enrolled randomized trial. In combination with data collected on male partners, IDEAL data facilitates a couple-based approach to understanding associations between lifestyle factors and infertility treatment outcomes. We describe in detail the study design, recruitment, data collection, lessons learned, and baseline characteristics. [ABSTRACT FROM AUTHOR]

In the test-negative design, routine testing at health-care facilities is leveraged to estimate the effectiveness of an intervention such as a vaccine. The odds of vaccination for individuals who test positive for a target pathogen is compared with the odds of vaccination for individuals who test negative for that pathogen, adjusting for key confounders. The design is rapidly growing in popularity, but many open questions remain about its properties. In this paper, we examine temporal confounding by generalizing derivations to allow for time-varying vaccine status, including out-of-season controls, and open populations. We confirm that calendar time is an important confounder when vaccine status varies during the study. We demonstrate that, where time is not a confounder, including out-of-season controls can improve precision. We generalize these results to open populations. We use our theoretical findings to interpret 3 recent papers utilizing the test-negative design. Through careful examination of the theoretical properties of this study design, we provide key insights that can directly inform the implementation and analysis of future test-negative studies. [ABSTRACT FROM AUTHOR]

Multilevel regression and poststratification (MRP) is a model-based approach for estimating a population parameter of interest, generally from large-scale surveys. It has been shown to be effective in highly selected samples, which is particularly relevant to investigators of large-scale population health and epidemiologic surveys facing increasing difficulties in recruiting representative samples of participants. We aimed to further examine the accuracy and precision of MRP in a context where census data provided reasonable proxies for true population quantities of interest. We considered 2 outcomes from the baseline wave of the Ten to Men study (Australia, 2013–2014) and obtained relevant population data from the 2011 Australian Census. MRP was found to achieve generally superior performance relative to conventional survey weighting methods for the population as a whole and for population subsets of varying sizes. MRP resulted in less variability among estimates across population subsets relative to sample weighting, and there was some evidence of small gains in precision when using MRP, particularly for smaller population subsets. These findings offer further support for MRP as a promising analytical approach for addressing participation bias in the estimation of population descriptive quantities from large-scale health surveys and cohort studies. [ABSTRACT FROM AUTHOR]

Difficulties inherent in the identification of immune correlates of protection or severe disease have challenged the development and evaluation of dengue vaccines. There persist substantial gaps in knowledge about the complex effects of age and sequential dengue virus (DENV) exposures on these correlations. To address these gaps, we were conducting a novel family-based cohort-cluster study for DENV transmission in Kamphaeng Phet, Thailand. The study began in 2015 and is funded until at least 2023. As of May 2019, 2,870 individuals in 485 families were actively enrolled. The families comprise at least 1 child born into the study as a newborn, 1 other child, a parent, and a grandparent. The median age of enrolled participants is 21 years (range 0–93 years). Active surveillance is performed to detect acute dengue illnesses, and annual blood testing identifies subclinical seroconversions. Extended follow-up of this cohort will detect sequential infections and correlate antibody kinetics and sequence of infections with disease outcomes. The central goal of this prospective study is to characterize how different DENV exposure histories within multigenerational family units, from DENV-naive infants to grandparents with multiple prior DENV exposures, affect transmission, disease, and protection at the level of the individual, household, and community. [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]

Sequences of treatments that adapt to a patient's changing condition over time are often needed for the management of chronic diseases. An adaptive treatment strategy (ATS) consists of personalized treatment rules to be applied through the course of a disease that input the patient's characteristics at the time of decision-making and output a recommended treatment. An optimal ATS is the sequence of tailored treatments that yields the best clinical outcome for patients sharing similar characteristics. Methods for estimating optimal adaptive treatment strategies, which must disentangle short- and long-term treatment effects, can be theoretically involved and hard to explain to clinicians, especially when the outcome to be optimized is a survival time subject to right-censoring. In this paper, we describe dynamic weighted survival modeling, a method for estimating an optimal ATS with survival outcomes. Using data from the Clinical Practice Research Datalink, a large primary-care database, we illustrate how it can answer an important clinical question about the treatment of type 2 diabetes. We identify an ATS pertaining to which drug add-ons to recommend when metformin in monotherapy does not achieve the therapeutic goals. [ABSTRACT FROM AUTHOR]

Algorithms are regularly used to identify persons living with diagnosed human immunodeficiency virus (HIV) (PLWDH) in Medicaid data. To our knowledge, there are no published reports of an HIV algorithm from Medicaid claims codes that have been compared with an HIV surveillance system to assess its sensitivity, specificity, positive predictive value, and negative predictive value in identifying PLWDH. Therefore, our aims in this study were to 1) develop an algorithm that could identify PLWDH in New York State Medicaid data from 2006–2014 and 2) validate this algorithm using the New York State HIV surveillance system. Classification and regression tree analysis identified 16 nodes that we combined to create a case-finding algorithm with 5 criteria. This algorithm identified 86,930 presumed PLWDH, 88.0% of which were verified by matching to the surveillance system. The algorithm yielded a sensitivity of 94.5%, a specificity of 94.4%, a positive predictive value of 88.0%, and a negative predictive value of 97.6%. This validated algorithm has the potential to improve the utility of Medicaid data for assessing health outcomes and programmatic interventions. [ABSTRACT FROM AUTHOR]