Bayesian safety surveillance with adaptive bias correction.

Postmarket safety surveillance is an integral part of mass vaccination programs. Typically relying on sequential analysis of real-world health data as they accrue, safety surveillance is challenged by sequential multiple testing and by biases induced by residual confounding in observational data. The current standard approach based on the maximized sequential probability ratio test (MaxSPRT) fails to satisfactorily address these practical challenges and it remains a rigid framework that requires prespecification of the surveillance schedule. We develop an alternative Bayesian surveillance procedure that addresses both aforementioned challenges using a more flexible framework. To mitigate bias, we jointly analyze a large set of negative control outcomes that are adverse events with no known association with the vaccines in order to inform an empirical bias distribution, which we then incorporate into estimating the effect of vaccine exposure on the adverse event of interest through a Bayesian hierarchical model. To address multiple testing and improve on flexibility, at each analysis timepoint, we update a posterior probability in favor of the alternative hypothesis that vaccination induces higher risks of adverse events, and then use it for sequential detection of safety signals. Through an empirical evaluation using six US observational healthcare databases covering more than 360 million patients, we benchmark the proposed procedure against MaxSPRT on testing errors and estimation accuracy, under two epidemiological designs, the historical comparator and the self-controlled case series. We demonstrate that our procedure substantially reduces Type 1 error rates, maintains high statistical power and fast signal detection, and provides considerably more accurate estimation than MaxSPRT. Given the extensiveness of the empirical study which yields more than 7 million sets of results, we present all results in a public R ShinyApp. As an effort to promote open science, we provide full implementation of our method in the open-source R package EvidenceSynthesis.

Comparative Effectiveness and Safety of Seizure Prophylaxis Among Adults After Acute Ischemic Stroke.

BACKGROUND: Older adults occasionally receive seizure prophylaxis in an acute ischemic stroke (AIS) setting, despite safety concerns. There are no trial data available about the net impact of early seizure prophylaxis on post-AIS survival. METHODS: Using a stroke registry (American Heart Association's Get With The Guidelines) individually linked to electronic health records, we examined the effect of initiating seizure prophylaxis (ie, epilepsy-specific antiseizure drugs) within 7 days of an AIS admission versus not initiating in patients ≥65 years admitted for a new, nonsevere AIS (National Institutes of Health Stroke Severity score ≤20) between 2014 and 2021 with no recorded use of epilepsy-specific antiseizure drugs in the previous 3 months. We addressed confounding by using inverse-probability weights. We performed standardization accounting for pertinent clinical and health care factors (eg, National Institutes of Health Stroke Severity scale, prescription counts, seizure-like events). RESULTS: The study sample included 151 patients who received antiseizure drugs and 3020 who did not. The crude 30-day mortality risks were 219 deaths per 1000 patients among epilepsy-specific antiseizure drugs initiators and 120 deaths per 1000 among noninitiators. After standardization, the estimated mortality was 251 (95% CI, 190-307) deaths per 1000 among initiators and 120 (95% CI, 86-144) deaths per 1000 among noninitiators, corresponding to a risk difference of 131 (95% CI, 65-200) excess deaths per 1000 patients. In the prespecified subgroup analyses, the risk difference was 52 (95% CI, 11-72) among patients with minor AIS and 138 (95% CI, 52-222) among moderate-to-severe AIS patients. Similarly, the risk differences were 86 (95% CI, 18-118) and 157 (95% CI, 57-219) among patients aged 65 to 74 years and ≥75 years, respectively. CONCLUSIONS: There was a higher risk of 30-day mortality associated with initiating versus not initiating seizure prophylaxis within 7 days post-AIS. This study does not support the role of seizure prophylaxis in reducing 30-day poststroke mortality.

Bias analyses to investigate the impact of differential participation: Application to a birth defects case-control study.

BACKGROUND: Certain associations observed in the National Birth Defects Prevention Study (NBDPS) contrasted with other research or were from areas with mixed findings, including no decrease in odds of spina bifida with periconceptional folic acid supplementation, moderately increased cleft palate odds with ondansetron use and reduced hypospadias odds with maternal smoking. OBJECTIVES: To investigate the plausibility and extent of differential participation to produce effect estimates observed in NBDPS. METHODS: We searched the literature for factors related to these exposures and participation and conducted deterministic quantitative bias analyses. We estimated case-control participation and expected exposure prevalence based on internal and external reports, respectively. For the folic acid-spina bifida and ondansetron-cleft palate analyses, we hypothesized the true odds ratio (OR) based on prior studies and quantified the degree of exposure over- (or under-) representation to produce the crude OR (cOR) in NBDPS. For the smoking-hypospadias analysis, we estimated the extent of selection bias needed to nullify the association as well as the maximum potential harmful OR. RESULTS: Under our assumptions (participation, exposure prevalence, true OR), there was overrepresentation of folic acid use and underrepresentation of ondansetron use and smoking among participants. Folic acid-exposed spina bifida cases would need to have been ≥1.2× more likely to participate than exposed controls to yield the observed null cOR. Ondansetron-exposed cleft palate cases would need to have been 1.6× more likely to participate than exposed controls if the true OR is null. Smoking-exposed hypospadias cases would need to have been ≥1.2 times less likely to participate than exposed controls for the association to falsely appear protective (upper bound of selection bias adjusted smoking-hypospadias OR = 2.02). CONCLUSIONS: Differential participation could partly explain certain associations observed in NBDPS, but questions remain about why. Potential impacts of other systematic errors (e.g. exposure misclassification) could be informed by additional research.

International Registry of Coronavirus Exposure in Pregnancy (IRCEP): Cohort Description and Methodological Considerations.

Limited data are available about the potential health effects of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on pregnant women and their developing offspring. We established the International Registry of Coronavirus Exposure in Pregnancy (IRCEP) to provide data on the risk of major adverse obstetric and neonatal outcomes among women with varying degrees of severity and timing of coronavirus disease 2019 (COVID-19) during pregnancy. We describe here the cohort and share the lessons learned. The IRCEP enrolls women tested for SARS-CoV-2 or with a clinical diagnosis of COVID-19 during pregnancy and obtains information using an online data collection system. By March 2021, 17,532 participants from 77 countries had enrolled; 54% enrolled during pregnancy and 46% afterward. Among women with symptomatic COVID-19 with a positive SARS-CoV-2 test (n = 4,934), symptoms were mild in 41%, moderate in 52%, and severe in 7%; 7.7% were hospitalized for COVID-19 and 1.7% were admitted to an intensive care unit. The biggest challenges were retention of participants enrolled during pregnancy and the potential bias introduced when participants enroll after pregnancy outcomes are known. Multiple biases need to be considered and addressed when estimating and interpreting the effects of COVID-19 in pregnancy in these types of cohorts.

Multifoetal gestations mediate the effect of in vitro fertilisation (IVF) on ischaemic placental disease in autologous oocyte IVF more than donor oocyte IVF.

BACKGROUND: Ischaemic placental disease (IPD) affects 16%-23% of pregnancies in the United States. In vitro fertilisation (IVF) is a risk factor for IPD, and the magnitude of increase in risk differs for individuals using donor oocytes (donor IVF) versus their own oocytes (autologous IVF). In addition, multifoetal gestations, which are more common in IVF than non-IVF pregnancies, also are a risk factor for IPD. OBJECTIVE: To quantify the contribution of multifoetal gestations to the association between IVF and IPD. METHODS: We conducted a retrospective cohort study at a tertiary hospital from 1 January, 2000 to 1 August 2018 using electronic medical records and state vital statistics data. IPD was defined as preeclampsia, placental abruption, small for gestational age (SGA) birth or an intrauterine foetal demise due to placental insufficiency. We used mediation analysis to decompose the total effect of IVF on IPD into a natural direct effect and an indirect effect through multifoetal gestations. We repeated the analyses separately for donor and autologous IVF. All models were adjusted for maternal age, race, parity, insurance, year of delivery and account for multiple pregnancies per person. RESULTS: We identified 86,514 deliveries, of which 281 resulted from donor IVF and 4173 resulted from autologous IVF. IVF pregnancies had 1.99 (95% CI 1.88, 2.10) times the risk of IPD compared to non-IVF pregnancies, and 75.5% of this increased risk was mediated by multifoetal gestations. Autologous IVF pregnancies had 1.95 (95% CI 1.84, 2.07) times the risk of IPD compared to non-IVF pregnancies, and the per cent mediated was 78.8%. Donor IVF pregnancies had 2.50 (95% CI 2.09, 2.92) times the risk of IPD, but the per cent mediated was 37.5%. CONCLUSION: The majority of the association between autologous IVF and IPD was mediated through multifoetal gestations; however, this was not the case for donor IVF pregnancies.

Emulation of a target trial with sustained treatment strategies: an application to prostate cancer using both inverse probability weighting and the g-formula.

As with many chronic illnesses, recurrent prostate cancer generally requires sustained treatment to prolong survival. However, initiating treatment immediately after recurrence may negatively impact quality of life without any survival gains. Therefore, we consider sustained strategies for initiating treatment based on specific characteristics of prostate-specific antigen (PSA), which can indicate disease progression. We define the protocol for a target trial comparing treatment strategies based on PSA doubling time, in which androgen deprivation therapy is initiated only after doubling time decreases below a certain threshold. Such a treatment strategy means the timing of treatment initiation (if ever) is not known at baseline, and the target trial protocol must explicitly specify the frequency of PSA monitoring until the threshold is met, as well as the duration of treatment. We describe these and other components of a target trial that need to be specified in order for such a trial to be emulated in observational data. We then use the parametric g-formula and inverse-probability weighted dynamic marginal structural models to emulate our target trial in a cohort of prostate cancer patients from clinics across the United States.

COVID-19 pharmacotherapy utilization patterns during pregnancy: International Registry of Coronavirus Exposure in Pregnancy.

PURPOSE: Women infected with SARS-CoV-2 during pregnancy are at increased risk of developing severe illness and experience a higher rate of preterm births than pregnant women who are not infected. The use of innovative or repurposed therapies to treat COVID-19 patients is widespread; however, there are very limited data regarding the patterns of use and safety profile of most of these therapeutics in pregnant women. We assessed the patterns of use of COVID-19 therapeutics during pregnancy using data from the International Registry of Coronavirus in Pregnancy (IRCEP). METHODS: The IRCEP is an international observational cohort study intended to assess the risk of major obstetric and neonatal outcomes among pregnant women with COVID-19. Women enrolled while pregnant or within 6 months after end of pregnancy. Follow-up for women enrolled while pregnant includes monthly online questionnaires throughout the pregnancy and, for live births, through the infant's first 90 days of life. Participants provide information on demographic characteristics, health history, COVID-19 tests and symptoms, medications, and obstetric and neonatal outcomes. RESULTS: A total of 5780 women with COVID-19 during pregnancy were identified from the IRCEP. Severity of COVID-19 was classified in 372 of them as severe, 3053 moderate, and 2355 mild. The most frequently reported COVID-19 therapies, other than analgesics, included azithromycin (12.8%), steroids (3.5%), interferon (2.4%), oseltamivir (2.1%), chloroquine/hydroxychloroquine (1.7%), anticoagulants (2.0%), antibodies (0.9%), and remdesivir (0.3%). Most drugs were preferentially used for severe cases. Patterns of use varied by country. CONCLUSIONS: IRCEP participants reported use of therapeutics for COVID-19 during pregnancy for which there is little safety information. Findings on COVID-19 pharmacotherapy utilization patterns can guide future studies examining the safety of COVID-19 therapies during pregnancy.

Timing and severity of COVID-19 during pregnancy and risk of preterm birth in the International Registry of Coronavirus Exposure in Pregnancy.

BACKGROUND: Studies of preterm delivery after COVID-19 are often subject to selection bias and do not distinguish between early vs. late infection in pregnancy, nor between spontaneous vs. medically indicated preterm delivery. This study aimed to estimate the risk of preterm birth (overall, spontaneous, and indicated) after COVID-19 during pregnancy, while considering different levels of disease severity and timing. METHODS: Pregnant and recently pregnant people who were tested for or clinically diagnosed with COVID-19 during pregnancy enrolled in an international internet-based cohort study between June 2020 and July 2021. We used several analytic approaches to minimize confounding and immortal time bias, including multivariable regression, time-to-delivery models, and a case-time-control design. RESULTS: Among 14,264 eligible participants from 70 countries who did not report a pregnancy loss before 20 gestational weeks, 5893 had completed their pregnancies and reported delivery information; others were censored at time of their last follow-up. Participants with symptomatic COVID-19 before 20 weeks' gestation had no increased risk of preterm delivery compared to those testing negative, with adjusted risks of 10.0% (95% CI 7.8, 12.0) vs. 9.8% (9.1, 10.5). Mild COVID-19 later in pregnancy was not clearly associated with preterm delivery. In contrast, severe COVID-19 after 20 weeks' gestation led to an increase in preterm delivery compared to milder disease. For example, the risk ratio for preterm delivery comparing severe to mild/moderate COVID-19 at 35 weeks was 2.8 (2.0, 4.0); corresponding risk ratios for indicated and spontaneous preterm delivery were 3.7 (2.0, 7.0) and 2.3 (1.2, 3.9), respectively. CONCLUSIONS: Severe COVID-19 late in pregnancy sharply increased the risk of preterm delivery compared to no COVID-19. This elevated risk was primarily due to an increase in medically indicated preterm deliveries, included preterm cesarean sections, although an increase in spontaneous preterm delivery was also observed. In contrast, mild or moderate COVID-19 conferred minimal risk, as did severe disease early in pregnancy.

Multiple-bias Sensitivity Analysis Using Bounds.

Confounding, selection bias, and measurement error are well-known sources of bias in epidemiologic research. Methods for assessing these biases have their own limitations. Many quantitative sensitivity analysis approaches consider each type of bias individually, although more complex approaches are harder to implement or require numerous assumptions. By failing to consider multiple biases at once, researchers can underestimate-or overestimate-their joint impact. We show that it is possible to bound the total composite bias owing to these three sources and to use that bound to assess the sensitivity of a risk ratio to any combination of these biases. We derive bounds for the total composite bias under a variety of scenarios, providing researchers with tools to assess their total potential impact. We apply this technique to a study where unmeasured confounding and selection bias are both concerns and to another study in which possible differential exposure misclassification and confounding are concerns. The approach we describe, though conservative, is easier to implement and makes simpler assumptions than quantitative bias analysis. We provide R functions to aid implementation.

The importance of mediation in reproductive health studies.

A mediator is a factor that occurs after the exposure of interest, precedes the outcome of interest (i.e. between the exposure and the outcome) and is associated with both the exposure and the outcome of interest (i.e. is on the pathway between exposure and outcome). Mediation analyses can be valuable in many reproductive health contexts, as mediation analysis can help researchers to better identify, quantify and understand the underlying pathways of the association they are studying. The purpose of this commentary is to introduce the concept of mediation and provide examples that solidify understanding of mediation for valid discovery and interpretation in the field of reproductive medicine.

Mediational E-values: Approximate Sensitivity Analysis for Unmeasured Mediator-Outcome Confounding.

BACKGROUND: Mediation analysis is a powerful tool for understanding mechanisms, but conclusions about direct and indirect effects will be invalid if there is unmeasured confounding of the mediator-outcome relationship. Sensitivity analysis methods allow researchers to assess the extent of this bias but are not always used. One particularly straightforward technique that requires minimal assumptions is nonetheless difficult to interpret, and so would benefit from a more intuitive parameterization. METHODS: We conducted an exhaustive numerical search over simulated mediation effects, calculating the proportion of scenarios in which a bound for unmeasured mediator-outcome confounding held under an alternative parameterization. RESULTS: In over 99% of cases, the bound for the bias held when we described the strength of confounding directly via the confounder-mediator relationship instead of via the conditional exposure-confounder relationship. CONCLUSIONS: Researchers can conduct sensitivity analysis using a method that describes the strength of the confounder-outcome relationship and the approximate strength of the confounder-mediator relationship that, together, would be required to explain away a direct or indirect effect.

Bounding Bias Due to Selection.

When epidemiologic studies are conducted in a subset of the population, selection bias can threaten the validity of causal inference. This bias can occur whether or not that selected population is the target population and can occur even in the absence of exposure-outcome confounding. However, it is often difficult to quantify the extent of selection bias, and sensitivity analysis can be challenging to undertake and to understand. In this article, we demonstrate that the magnitude of the bias due to selection can be bounded by simple expressions defined by parameters characterizing the relationships between unmeasured factor(s) responsible for the bias and the measured variables. No functional form assumptions are necessary about those unmeasured factors. Using knowledge about the selection mechanism, researchers can account for the possible extent of selection bias by specifying the size of the parameters in the bounds. We also show that the bounds, which differ depending on the target population, result in summary measures that can be used to calculate the minimum magnitude of the parameters required to shift a risk ratio to the null. The summary measure can be used to determine the overall strength of selection that would be necessary to explain away a result. We then show that the bounds and summary measures can be simplified in certain contexts or with certain assumptions. Using examples with varying selection mechanisms, we also demonstrate how researchers can implement these simple sensitivity analyses. See video abstract at, http://links.lww.com/EDE/B535.

Benzodiazepine Initiation and the Risk of Falls or Fall-Related Injuries in Older Adults Following Acute Ischemic Stroke.

Background: Benzodiazepine use in older adults following acute ischemic stroke (AIS) is common, yet short-term safety concerning falls or fall-related injuries remains unexplored. Methods: We emulated a hypothetical randomized trial of benzodiazepine use during the acute post stroke recovery period to assess incidence of falls or fall related injuries in older adults. Using linked data from the Get With the Guidelines Registry and Mass General Brigham's electronic health records, we selected patients aged 65 and older admitted for Acute Ischemic Stroke (AIS) between 2014 and 2021 with no documented prior stroke and no benzodiazepine prescriptions in the previous 3 months. Potential for immortal-time and confounding biases was addressed via separate inverse-probability weighting strategies. Results: The study included 495 patients who initiated inpatient benzodiazepines within three days of admission and 2,564 who did not. After standardization, the estimated 10-day risk of falls or fall-related injuries was 694 events per 1000 (95% confidence interval CI: 676-709) for the benzodiazepine initiation strategy and 584 events per 1000 (95% CI: 575-595) for the non-initiation strategy. Subgroup analyses showed risk differences of 142 events per 1000 (95% CI: 111-165) and 85 events per 1000 (95% CI: 64-107) for patients aged 65 to 74 years and for those aged 75 years or older, respectively. Risk differences were 187 events per 1000 (95% CI: 159-206) for patients with minor (NIHSS≤ 4) AIS and 32 events per 1000 (95% CI: 10-58) for those with moderate-to-severe AIS. Conclusions: Initiating inpatient benzodiazepines within three days of AIS is associated with an elevated 10-day risk of falls or fall-related injuries, particularly for patients aged 65 to 74 years and for those with minor strokes. This underscores the need for caution with benzodiazepines, especially among individuals likely to be ambulatory during the acute and sub-acute post-stroke period.

Data Missingness Reporting and Use of Methods to Address It in Critical Care Cohort Studies.

IMPORTANCE: Failure to recognize and address data missingness in cohort studies may lead to biased results. Although Strengthening the Reporting of Observational Studies in Epidemiology reporting guidelines advocate data missingness reporting, the degree to which missingness is reported and addressed in the critical care literature remains unclear. OBJECTIVES: To review published ICU cohort studies to characterize data missingness reporting and the use of methods to address it. DESIGN SETTING AND PARTICIPANTS: We searched the 2022 table of contents of 29 critical care/critical care subspecialty journals having a 2021 impact factor greater than or equal to 3 to identify published prospective clinical or retrospective database cohort studies enrolling greater than or equal to 100 patients. MAIN OUTCOMES AND MEASURES: In duplicate, two trained researchers conducted a manuscript/supplemental material PDF word search for "missing*" and extracted study type, patient age, ICU type, sample size, missingness reporting, and the use of methods to address it. RESULTS: A total of 656 studies were reviewed. Of the 334 of 656 (50.9%) studies mentioning missingness, missingness was reported for greater than or equal to 1 variable in 234 (70.1%) and it exceeded 5% for at least one variable in 160 (47.9%). Among the 334 studies mentioning missingness, 88 (26.3%) used exclusion criteria, 36 (10.8%) used complete-case analysis, and 164 (49.1%) used a formal method to avoid missingness. In these 164 studies, imputation only was used in 100 (61.0%), an analytic strategy only in 24 (14.6%), and both in 40 (24.4%). Only missingness greater than 5% (in ≥ 1 variable) was independently associated with greater use of a missingness method (adjusted odds ratio 2.91; 95% CI, 1.85-4.60). Among 140 studies using imputation, multiple imputation was used in 87 studies (62.1%) and simple imputation in 49 studies (35.0%). For the 64 studies using an analytic method, 12 studies (18.8%) assigned missingness as an unknown category, whereas sensitivity analysis was used in 47 studies (73.4%). CONCLUSIONS AND RELEVANCE: Among published critical care cohort studies, only half mentioned result missingness, one-third reported actual missingness and only one-quarter used a method to manage missingness. Educational strategies to promote missingness reporting and resolution methods are required.

No short-term mortality from benzodiazepine use post-acute ischemic stroke after accounting for bias.

BACKGROUND AND OBJECTIVES: Older adults receive benzodiazepines for agitation, anxiety, and insomnia after acute ischemic stroke (AIS). No trials have been conducted to determine if benzodiazepine use affects poststroke mortality in the elderly. METHODS: We examined the association between initiating benzodiazepines within 1 week after AIS and 30-day mortality. We included patients ≥65 years, admitted for new nonsevere AIS (NIH-Stroke-Severity[NIHSS]≤ 20), 2014-2020, with no recorded benzodiazepine use in the previous 3 months and no contraindication for use. We linked a stroke registry to electronic health records, used inverse-probability weighting to address confounding, and estimated the risk difference (RD). A process of cloning, weighting, and censoring was used to avoid immortal time bias. RESULTS: Among 2,584 patients, 389 received benzodiazepines. The crude 30-day mortality risk from treatment initiation was 212/1,000 among patients who received benzodiazepines, while the 30-day mortality was 34/1,000 among those who did not. When follow-up was aligned on day of AIS admission and immortal time was assigned to the two groups, the estimated risks were 27/1,000 and 22/1,000, respectively. Upon further adjustment for confounders, the RD was 5 (-12 to 19) deaths/1,000 patients. CONCLUSION: The observed higher 30-day mortality associated with benzodiazepine initiation within 7 days was largely due to bias.

Side effects of COVID-19 vaccinations in patients treated for breast cancer.

Lymph node swelling is a side effect of the mRNA COVID-19 vaccines, a distressing side effect for women treated for breast cancer. The purpose of this study is to present side effects reported by a cohort of patients treated for breast cancer. A survey link was sent to 4945 women who received breast cancer treatment and were prospectively screened for breast cancer-related lymphedema. In total, 621 patients who received an mRNA vaccine and responded to the survey were included in analysis. We assessed the frequency and predictors of side effects. The most frequent side effects reported were injection site soreness, fatigue, generalized muscle soreness, headache, and chills, with median duration ≤ 48 h. Lymph node swelling occurred most often in the axilla ipsilateral to the vaccine. The median duration was 1 week or less after all doses. These data will inform patient education regarding future vaccine doses, including reassurances about which side effects to expect, particularly lymph node swelling which may impact mammograms after vaccination. Type and duration of side effects were similar to that reported by the general population in Phase 3 testing trials of the mRNA vaccines. Clinical Trial Registration NCT04872738 posted May 4, 2021.

Frailty Among Sexual and Gender Minority Older Adults: The All of Us Database.

BACKGROUND: Despite known disparities in health status among older sexual and gender minority adults (OSGM), the prevalence of frailty is unknown. The aim of this study was to develop and validate a deficit-accumulation frailty index (AoU-FI) for the All of Us database to describe and compare frailty between OSGM and non-OSGM participants. METHODS: Developed using a standardized approach, the AoU-FI consists of 33 deficits from baseline survey responses of adults aged 50+. OSGM were self-reported as "not straight" or as having discordant gender and sex assigned at birth. Descriptive statistics characterized the AoU-FI. Regression was used to assess the association between frailty, age, and gender. Validation of the AoU-FI used Cox proportional hazard models to test the association between frailty categories (robust <0.15, 0.15 ≤ pre-frail ≤ 0.25, frail >0.25) and mortality. RESULTS: There were 9 110 OSGM and 67 420 non-OSGM with sufficient data to calculate AoU-FI; 41% OSGM versus 50% non-OSGM were robust, whereas 34% versus 32% were pre-frail, and 26% versus 19% were frail. Mean AoU-FI was 0.19 (95% confidence interval [CI]: 0.187, 0.191) for OSGM and 0.168 (95% CI: 0.167, 0.169) for non-OSGM. Compared to robust, odds of mortality were higher among frail OSGM (odds ratio [OR] 6.40; 95% CI: 1.84, 22.23) and non-OSGM (OR 3.96; 95% CI: 2.96, 5.29). CONCLUSIONS: The AoU-FI identified a higher burden of frailty, increased risk of mortality, and an attenuated impact of age on frailty among OSGM compared to non-OSGM. Future work is needed to understand how frailty affects the OSGM population.

E-values for effect heterogeneity and approximations for causal interaction.

BACKGROUND: Estimates of effect heterogeneity (i.e. the extent to which the causal effect of one exposure varies across strata of a second exposure) can be biased if the exposure-outcome relationship is subject to uncontrolled confounding whose severity differs across strata of the second exposure. METHODS: We propose methods, analogous to the E-value for total effects, that help to assess the sensitivity of effect heterogeneity estimates to possible uncontrolled confounding. These E-value analogues characterize the severity of uncontrolled confounding strengths that would be required, hypothetically, to 'explain away' an estimate of multiplicative or additive effect heterogeneity in the sense that appropriately controlling for those confounder(s) would have shifted the effect heterogeneity estimate to the null, or alternatively would have shifted its confidence interval to include the null. One can also consider shifting the estimate or confidence interval to an arbitrary non-null value. All of these E-values can be obtained using the R package EValue. RESULTS: We illustrate applying the proposed E-value analogues to studies on: (i) effect heterogeneity by sex of the effect of educational attainment on dementia incidence and (ii) effect heterogeneity by age on the effect of obesity on all-cause mortality. CONCLUSION: Reporting these proposed E-values could help characterize the robustness of effect heterogeneity estimates to potential uncontrolled confounding.