American Journal of Epidemiology

ObjectiveFindings from family-based analyses, such as sibling comparisons, are often reported using only odds ratios or hazard ratios. We demonstrate how this can be improved upon by applying the marginalized between-within framework. Study Design and SettingWe provide an overview of sibling comparison methods and the marginalized between-within framework, which enables estimation of absolute risks and clinically relevant metrics while accounting for shared familial confounding. We illustrate t...

BackgroundElectronic health record (EHR)-based observational studies can rapidly provide real-world data on vaccine effectiveness (VE), particularly key during the COVID-19 pandemic. However, EHR data may be prone to misclassification and unmeasured confounding, requiring systematic mitigation to ensure robust findings. MethodsIn VEBIS-EHR, a retrospective multi-country COVID-19 VE cohort study, we examined unmeasured confounding using a negative control outcome (death not related to COVID-19) ...

AO_SCPLOWBSTRACTC_SCPLOWThe test-negative design has become a standard approach for vaccine effectiveness studies. However, previous studies suggested that it may be more sensitive than other designs to misclassification of disease outcome caused by imperfect diagnostic tests. This could be a particular limitation in vaccine effectiveness studies where simple tests (e.g. rapid influenza diagnostic tests) are used for logistical convenience. To address this issue, we derived a mathematical repres...

Variation in binary outcomes over time by cluster size arises across various biomedical disciplines, including reproductive health, dental medicine, and psychiatric epidemiology. This study formally integrates modified Poisson regression with cluster-weighted generalized estimating equations (MP-CWGEE) for computing risk ratios in longitudinal studies with informative cluster sizes. Using a comprehensive Monte-Carlo simulation study, we empirically evaluated MP-CWGEEs statistical properties agai...

Confounding is one of the most important concerns for randomized or nonrandomized intervention or exposure studies. This manuscript describes several metrics intended to provide quantitative approximations of confounding under certain conditions. Each metric quantifies differences in risk between intervention arms during time periods when the intervention (or exposure of interest) is not occurring. Because exposure is absent, these metrics have the potential to summarize the effects of other mea...

Ratio measures of effect, such as the odds ratio (OR), are consistent, but the presumption of their unbiasedness is founded on a false premise: The equality of the expected value of a ratio and the ratio of expected values. We show that the invalidity of this assumptions is an important source of empirical bias in ratio measures of effect, which is due to properties of the expectation of ratios of count random variables. We investigate ORs (unconfounded, no effect modification), proposing a corr...

BackgroundUnmeasured confounding is a persistent concern in observational studies. We can quantitatively assess the impact of unmeasured confounding using a quantitative bias analysis (QBA). A QBA specifies the relationship between the unmeasured confounder(s), U, and study data via its bias parameters. There are two broad classes of QBA methods: deterministic and probabilistic. We focus on a probabilistic QBA which incorporates external information about U via prior distribution(s) placed on t...

BackgroundIt is critical public health concern to identify safety signals originating from wide-scale immunization efforts. Such safety signals may be identified from spontaneous reports and other data sources. Although some work has been done on the best methods for vaccine safety surveillance, there is a scarcity of information on how these perform in analyses of real-world data. MethodsWe use four administrative claims databases and one electronic health record (EHR) database to evaluate the...

Epidemiologists are careful to describe their findings as "associations", and to avoid any causal language or claims. Arguably, this attempt to avoid reference to causal processes has become counterproductive. Explicitly stated or not, assumptions about causal processes are inherent in the formulation and interpretation of any statistical study. This article offers a bridge between established, extensively developed proportional hazard methods that are used to study longitudinal observational co...

Epidemiological studies often have missing data, which are commonly handled by multiple imputation (MI). In MI, in addition to those required for the substantive analysis, imputation models often include other variables ("auxiliary variables"). Auxiliary variables that predict the partially observed variables can reduce the standard error (SE) of the MI estimator and, if they also predict the probability that data are missing, reduce bias due to data being missing not at random. However, guidanc...

BackgroundStepped-wedge designs (SWDs) are currently being used to investigate interventions to reduce opioid overdose deaths in communities located in several states. However, these interventions are competing with external factors such as newly initiated public policies limiting opioid prescriptions, media awareness campaigns, and social distancing orders due to the COVID-19 pandemic. Furthermore, control communities may prematurely adopt components of the proposed intervention as they become ...

BackgroundVaccine safety surveillance commonly includes a serial testing approach with a sensitive method for "signal generation" and specific method for "signal validation." Whether serially combining epidemiological designs improves both sensitivity and specificity is unknown. MethodsWe assessed the overall performance of serial testing using three administrative claims and one electronic health record database. We compared Type I and II errors before and after empirical calibration for histo...

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSAn internal validation substudy compares an imperfect measurement of a variable with a gold standard measurement in a subset of the study population. Validation data permit calculation of a bias-adjusted estimate, expected to equal the association that would have been observed had the gold standard measurement been available for the entire study population. Guidance on optimal sampling of participants to include in validation substudies has not c...

In many laboratory assays that measure immunological quantities, a portion of the measured values fall below a limit of detection (LOD). This is also the case for the hemagglutination inhibition assay (HAI), a common method used to quantify antibodies in influenza research. The conventional approach is to treat values below the LOD as either equal to the LOD or LOD/2, which can introduce potential biases. These biases can become more pronounced when calculating compound measures such as the diff...

Our objective was to assess associations between 8 adverse childhood experiences (ACEs; physical, emotional, and sexual abuse as a child, or living in household with substance abuse, mental health problems, divorce, intimate partner violence, or incarceration) and subjective cognitive impairment (SCI: the disability question from the 2019 Behavioral Risk Factor Surveillance Surveys) and compare with cardiovascular disease (CVD) results. MethodsAdults (N=149,801) from 21 states with data on ACEs...

We present our considerations for using multiple imputation to account for missing data in propensity score-weighted analysis with bootstrap percentile confidence interval. We outline the assumptions underlying each of the methods and discuss the methodological and practical implications of our choices and briefly point to alternatives. We made a number of choices a priori for example to use logistic regression-based propensity scores to produce "standardized mortality ratio"-weights and Substan...

Reducing population levels of frailty is an important goal and preventing its development in mid-adulthood could be pivotal. Childhood socioeconomic position (SEP) is associated with a myriad of adult health outcomes but evidence is limited on associations with frailty. Using 1958 British birth cohort data (N=8711), we aimed to: (i) establish the utility of measuring frailty in mid-life, by examining associations between a 34-item frailty index at 50y (FI50y) and mortality over an eight-year fol...

Epidemiological studies often have missing data, which are commonly handled by multiple imputation (MI). MI is valid (given correctly-specified models) if data are missing at random, conditional on the observed data, but not (unless additional information is available) if data are missing not at random (MNAR). In this paper we explore a previously-suggested strategy, namely, including an auxiliary variable predictive of missingness but not the missing data in the imputation model, when data are ...

Neighbourhood disadvantage may be associated with brain health but the importance at different stages of the life course is poorly understood. Utilizing the Lothian Birth Cohort 1936, we explored the relationship between residential neighbourhood deprivation from birth to late adulthood, and global and regional neuroimaging measures at age 73. We found that residing in disadvantaged neighbourhoods in mid- to late adulthood was associated with smaller total brain ({beta}=-0.06; SE=0.02; n=390) an...

Observational analyses of electronic health record (EHR) data using databases such as the National Clinical Cohort Collaborative include unique challenges for researchers seeking causal inferences, particularly when evaluating subjectively-defined outcomes like Long COVID. We explore several challenges and describe potential solutions. 1. Lack of true negatives: Many diagnoses and conditions either have a positive indicator or a missing status, requiring investigators to carefully consider which...