Bespoke instrumental variables with non-ideal reference populations.

Recently, a bespoke instrumental variable method was proposed, which, under certain assumptions, can eliminate bias due to unmeasured confounding when estimating the causal exposure effect among the exposed. This method uses data from both the study population of interest, and a reference population in which the exposure is completely absent. In this paper, we extend the bespoke instrumental variable method to allow for a non-ideal reference population that may include exposed subjects. Such an extension is particularly important in randomized trials with nonadherence, where even subjects in the control arm may have access to the treatment under investigation. We further scrutinize the assumptions underlying the bespoke instrumental method, and caution the reader about the potential non-robustness of the method to these assumptions.

Causal Inference in Oncology: Why, What, How and When.

Oncologists are faced with choosing the best treatment for each patient, based on the available evidence from randomized controlled trials (RCTs) and observational studies. RCTs provide estimates of the average effects of treatments on groups of patients, but they may not apply in many real-world scenarios where for example patients have different characteristics than the RCT participants, or where different treatment variants are considered. Causal inference defines what a treatment effect is and how it may be estimated with RCTs or outside of RCTs with observational - or 'real-world' - data. In this review, we introduce the field of causal inference, explain what a treatment effect is and what important challenges are with treatment effect estimation with observational data. We then provide a framework for conducting causal inference studies and describe when in oncology causal inference from observational data may be particularly valuable. Recognizing the strengths and limitations of both RCTs and observational causal inference provides a way for more informed and individualized treatment decision-making in oncology.

Assessment of unmeasured confounding in the association between perceived discrimination and mental health in a predominantly African American cohort using g-estimation.

BACKGROUND: Perceived discrimination in health care settings can have adverse consequences on mental health in minority groups. However, the association between perceived discrimination and mental health is prone to unmeasured confounding. The study aims to quantitatively evaluate the influence of unmeasured confounding in this association, using g-estimation. METHODS: In a predominantly African American cohort, we applied g-estimation to estimate the association between perceived discrimination and mental health, adjusted and unadjusted for measured confounders. Mental health was measured using clinical diagnoses of anxiety, depression and bipolar disorder. Perceived discrimination was measured as the number of patient-reported discrimination events in health care settings. Measured confounders included demographic, socioeconomic, residential and health characteristics. The influence of confounding was denoted as α1 from g-estimation. We compared α1 for measured and unmeasured confounding. RESULTS: Strong associations between perceived discrimination in health care settings and mental health outcomes were observed. For anxiety, the odds ratio (95% confidence interval) unadjusted and adjusted for measured confounders were 1.30 (1.21, 1.39) and 1.26 (1.17, 1.36), respectively. The α1 for measured confounding was -0.066. Unmeasured confounding with α1=0.200, which was over three times that of measured confounding, corresponds to an odds ratio of 1.12 (1.01, 1.24). Similar results were observed for other mental health outcomes. CONCLUSION: Compared with measured confounding, unmeasured that was three times measured confounding was not enough to explain away the association between perceived discrimination and mental health, suggesting that this association is robust to unmeasured confounding. This study provides a novel framework to quantitatively evaluate unmeasured confounding.

Parametric g-formula for Testing Time-Varying Causal Effects: What It Is, Why It Matters, and How to Implement It in Lavaan.

Psychologists leverage longitudinal designs to examine the causal effects of a focal predictor (i.e., treatment or exposure) over time. But causal inference of naturally observed time-varying treatments is complicated by treatment-dependent confounding in which earlier treatments affect confounders of later treatments. In this tutorial article, we introduce psychologists to an established solution to this problem from the causal inference literature: the parametric g-computation formula. We explain why the g-formula is effective at handling treatment-dependent confounding. We demonstrate that the parametric g-formula is conceptually intuitive, easy to implement, and well-suited for psychological research. We first clarify that the parametric g-formula essentially utilizes a series of statistical models to estimate the joint distribution of all post-treatment variables. These statistical models can be readily specified as standard multiple linear regression functions. We leverage this insight to implement the parametric g-formula using lavaan, a widely adopted R package for structural equation modeling. Moreover, we describe how the parametric g-formula may be used to estimate a marginal structural model whose causal parameters parsimoniously encode time-varying treatment effects. We hope this accessible introduction to the parametric g-formula will equip psychologists with an analytic tool to address their causal inquiries using longitudinal data.

Comparison of associations between alcohol consumption and metabolic syndrome according to three definitions: The Swedish INTERGENE study.

Background: While prevalence estimates differ by definition of metabolic syndrome (MetS), it is less clear how different definitions affect associations with alcohol consumption. Methods: We included 3051 adults aged 25-77 from the baseline examination of the Swedish INTERGENE cohort (2001-2004). Using multiple logistic regression, we investigated cross-sectional associations between ethanol intake and MetS defined according to the Adult Treatment Panel III (ATP III), the International Diabetes Federation (IDF), and the Joint Interim Statement (JIS). Alcohol exposure categories comprised abstinence, and low, medium, and high consumption defined via sex-specific tertiles of ethanol intake among current consumers. Covariates included sociodemographics, health, and lifestyle factors. Results: MetS prevalence estimates varied between 13.9 % (ATP III) and 25.3 % (JIS), with higher prevalence in men than women. Adjusted for age and sex, medium-high alcohol consumption was associated with lower odds of MetS compared to low consumption, while no difference was observed for abstainers. Only the most specific (and thus severe) definition of MetS (ATP III) showed decreasing odds for ethanol intake when adjusted for all covariates. Conclusion: Our study shows that alcohol-related associations differ by definition of MetS. The finding that individuals with the most stringently defined MetS may benefit from alcohol consumption calls for further well-controlled studies.

Assessing Etiologic Heterogeneity for Multinomial Outcome with Two-Phase Outcome-Dependent Sampling Design.

Etiologic heterogeneity occurs when distinct sets of events or exposures give rise to different subtypes of disease. Inference about subtype-specific exposure effects from two-phase outcome-dependent sampling data requires adjustment for both confounding and the sampling design. Common approaches to inference for these effects do not necessarily appropriately adjust for these sources of bias, or allow for formal comparisons of effects across different subtypes. Herein, using inverse probability weighting (IPW) to fit a multinomial model is shown to yield valid inference with this sampling design for subtype-specific exposure effects and contrasts thereof. The IPW approach is compared to common regression-based methods for assessing exposure effect heterogeneity using simulations. The methods are applied to estimate subtype-specific effects of various exposures on breast cancer risk in the Carolina Breast Cancer Study.

Multiply robust estimation of marginal structural models in observational studies subject to covariate-driven observations.

Electronic health records and other sources of observational data are increasingly used for drawing causal inferences. The estimation of a causal effect using these data not meant for research purposes is subject to confounding and irregularly-spaced covariate-driven observation times affecting the inference. A doubly-weighted estimator accounting for these features has previously been proposed that relies on the correct specification of two nuisance models used for the weights. In this work, we propose a novel consistent multiply robust estimator and demonstrate analytically and in comprehensive simulation studies that it is more flexible and more efficient than the only alternative estimator proposed for the same setting. It is further applied to data from the Add Health study in the United States to estimate the causal effect of therapy counseling on alcohol consumption in American adolescents.

Application of high-dimensional propensity score methods to the National Health and Aging Trends Study.

BACKGROUND: High-dimensional propensity scoring (HDPS) is a method for empirically identifying potential confounders within large healthcare databases such as administrative claims data. However, this method has not yet been applied to large national health surveys such as the National Health and Aging Trends Study (NHATS), an ongoing nationally representative survey of older adults in the U.S and important resource in gerontology research. METHODS: In this Research Practice article, we present an overview of HDPS and describe the specific data transformation steps and analytic considerations needed to apply it to national health surveys. We applied HDPS within NHATS to investigate the association between self-reported visual difficulty and incident dementia, comparing HDPS to conventional confounder selection methods. RESULTS: Among 7,207 dementia-free NHATS wave 1 respondents, 528 (7.3%) had self-reported visual difficulty. In an unadjusted discrete time proportional hazards model accounting for the complex survey design of NHATS, self-reported visual difficulty was strongly associated with incident dementia (OR 2.34, 95% CI: 1.95-2.81). After adjustment for standard investigator-selected covariates via inverse probability weighting, the magnitude of this association decreased, but evidence of an association remained (OR 1.44, 95% CI: 1.11-1.85). Adding 75 HDPS-prioritized variables to the investigator-selected propensity score model resulted in further attenuation of the association between visual impairment and dementia (OR 0.94, 95% CI: 0.70-1.23). CONCLUSIONS: HDPS can be successfully applied to national health surveys such as NHATS and may improve confounder adjustment. We hope developing this framework will encourage future consideration of HDPS in this setting.

Identification and estimation of causal effects in the presence of confounded principal strata.

Principal stratification has become a popular tool to address a broad class of causal inference questions, particularly in dealing with non-compliance and truncation by death problems. The causal effects within principal strata, which are determined by joint potential values of the intermediate variable, also known as the principal causal effects, are often of interest in these studies. The analysis of principal causal effects from observational studies mostly relies on the ignorability assumption of treatment assignment, which requires practitioners to accurately measure as many covariates as possible so that all potential sources of confounders are captured. However, in practice, collecting all potential confounding factors can be challenging and costly, rendering the ignorability assumption questionable. In this paper, we consider the identification and estimation of causal effects when treatment and principal stratification are confounded by unmeasured confounding. Specifically, we establish the nonparametric identification of principal causal effects using a pair of negative controls to mitigate unmeasured confounding, requiring they have no direct effect on the outcome variable. We also provide an estimation method for principal causal effects. Extensive simulations and a leukemia study are employed for illustration.

Evaluation and comparison of covariate balance metrics in studies with time-dependent confounding.

Marginal structural models have been increasingly used by analysts in recent years to account for confounding bias in studies with time-varying treatments. The parameters of these models are often estimated using inverse probability of treatment weighting. To ensure that the estimated weights adequately control confounding, it is possible to check for residual imbalance between treatment groups in the weighted data. Several balance metrics have been developed and compared in the cross-sectional case but have not yet been evaluated and compared in longitudinal studies with time-varying treatment. We have first extended the definition of several balance metrics to the case of a time-varying treatment, with or without censoring. We then compared the performance of these balance metrics in a simulation study by assessing the strength of the association between their estimated level of imbalance and bias. We found that the Mahalanobis balance performed best. Finally, the method was illustrated for estimating the cumulative effect of statins exposure over one year on the risk of cardiovascular disease or death in people aged 65 and over in population-wide administrative data. This illustration confirms the feasibility of employing our proposed metrics in large databases with multiple time-points.

Preventable sources of bias in subgroup analyses and secondary outcomes of randomized trials.

BACKGROUND: Randomized controlled trials are the gold standard for determining treatment efficacy in medicine. To deter harmful practices such as p-hacking and hypothesizing after the results are known, any analysis of subgroups and secondary outcomes must be documented and pre-specified. However, they can still introduce bias (and routinely do) if they are not treated with the same consideration as the primary analysis. METHODS: We describe several sources of bias that affect subgroup and secondary outcome analyses using published randomized trials and causal directed acyclic graphs (DAGs). RESULTS: We use the RECOVERY and START trials to elucidate sources of bias in analyses of subgroups and secondary outcomes. Chance imbalance can occur if the distribution of prognostic variables is not sought for any given subgroup analysis as for the main analysis. This differential distribution of prognostic variables can also occur in analyses of secondary outcomes. Selection bias can occur if the subgroup variable is causally related to staying in the trial. Given loss to follow up is not normally addressed in subgroups, attrition bias can pass unnoticed in these cases. In every case, the solution is to take the same considerations for these analyses as we do for primary analyses. CONCLUSIONS: Approval of treatments and clinical decisions can occur based on results from subgroup or secondary outcome analyses. Thus, it is important to give them the same treatment as primary analyses to avoid preventable biases.

Confounding associations between green space and outdoor artificial light at night: Systematic investigations and implications for urban health.

Excessive urbanization leads to considerable nature deficiency and abundant artificial infrastructure in urban areas, which triggered intensive discussions on people's exposure to green space and outdoor artificial light at night (ALAN). Recent academic progress highlights that people's exposure to green space and outdoor ALAN may be confounders of each other but lacks systematic investigations. This study investigates the associations between people's exposure to green space and outdoor ALAN by adopting the three most used research paradigms: population-level residence-based, individual-level residence-based, and individual-level mobility-oriented paradigms. We employed the green space and outdoor ALAN data of 291 Tertiary Planning Units in Hong Kong for population-level analysis. We also used data from 940 participants in six representative communities for individual-level analyses. Hong Kong green space and outdoor ALAN were derived from high-resolution remote sensing data. The total exposures were derived using the spatiotemporally weighted approaches. Our results confirm that the negative associations between people's exposure to green space and outdoor ALAN are universal across different research paradigms, spatially non-stationary, and consistent among different socio-demographic groups. We also observed that mobility-oriented measures may lead to stronger negative associations than residence-based measures by mitigating the contextual errors of residence-based measures. Our results highlight the potential confounding associations between people's exposure to green space and outdoor ALAN, and we strongly recommend relevant studies to consider both of them in modeling people's health outcomes, especially for those health outcomes impacted by the co-exposure to them.

Overcoming Time-Varying Confounding in Self-Controlled Case Series with Active Comparators: Application and Recommendations.

Confounding by indication is a key challenge for pharmacoepidemiologists. Although self-controlled study designs address time-invariant confounding, indications sometimes vary over time. For example, infection might act as a time-varying confounder in a study of antibiotics and uveitis, because it is time-limited and a direct cause both of receiving antibiotics and uveitis. Methods for incorporating active comparators in self-controlled studies to address such time-varying confounding by indication have only recently been developed. In this paper we formalize these methods, and provide a detailed description for how the active comparator rate ratio can be derived in a self-controlled case series (SCCS): either by explicitly comparing the regression coefficients for a drug of interest and an active comparator under certain circumstances using a simple ratio approach, or through the use of a nested regression model. The approaches are compared in two case studies, one examining the association between thiazolidinediones and fractures, and one examining the association between fluoroquinolones and uveitis using the UK Clinical Practice Research DataLink. Finally, we provide recommendations for the use of these methods, which we hope will support the design, execution and interpretation of SCCS using active comparators and thereby increase the robustness of pharmacoepidemiological studies.

Higher-Order Least Squares: Assessing Partial Goodness of Fit of Linear Causal Models.

We introduce a simple diagnostic test for assessing the overall or partial goodness of fit of a linear causal model with errors being independent of the covariates. In particular, we consider situations where hidden confounding is potentially present. We develop a method and discuss its capability to distinguish between covariates that are confounded with the response by latent variables and those that are not. Thus, we provide a test and methodology for partial goodness of fit. The test is based on comparing a novel higher-order least squares principle with ordinary least squares. In spite of its simplicity, the proposed method is extremely general and is also proven to be valid for high-dimensional settings. Supplementary materials for this article are available online.

Intention to treat and per protocol analyses: differences and similarities.

Randomized trials can take more explanatory or more pragmatic approaches. Pragmatic studies, conducted closer to real-world conditions, assess treatment effectiveness while considering factors like protocol adherence. In these studies, intention-to-treat (ITT) analysis is fundamental, comparing outcomes regardless of the actual treatment received. Explanatory trials, conducted closer to optimal conditions, evaluate treatment efficacy, commonly with a per protocol (PP) analysis, which includes only outcomes from adherent participants. ITT and PP are strategies used in the conception, design, conduct (protocol execution), analysis, and interpretation of trials. Each serves distinct objectives. While both can be valid, when bias is controlled, and complementary, each has its own limitations. By excluding nonadherent participants, PP analyses can lose the benefits of randomization, resulting in group differences in factors (influencing adherence and outcomes) that were present at baseline. Additionally, clinical and social factors affecting adherence can also operate during follow-up, that is, after randomization. Therefore, incomplete adherence may introduce postrandomization confounding. Conversely, ITT analysis, including all participants regardless of adherence, may dilute treatment effects. Moreover, varying adherence levels could limit the applicability of ITT findings in settings with diverse adherence patterns. Both ITT and PP analyses can be affected by selection bias due to differential losses and nonresponse (ie, missing data) during follow-up. Combining high-quality and comprehensive data with advanced statistical methods, known as g-methods, like inverse probability weighting, may help address postrandomization confounding in PP analysis as well as selection bias in both ITT and PP analyses.

Confounding and Negative Control Methods in Observational Study of SARS-CoV-2 Vaccine Effectiveness: A Nationwide, Population-Based Danish Health Registry Study.

Background: Observational studies of SARS-CoV-2 vaccine effectiveness are prone to confounding, which can be illustrated using negative control methods. Methods: Nationwide population-based cohort study including two cohorts of Danish residents 60-90 years of age matched 1:1 on age and sex: A vaccinated and a non-vaccinated cohort, including 61052 SARS-CoV-2 vaccinated individuals between 1 March and 1 July 2021 and 61052 individuals not vaccinated preceding 1 July 2021. From these two cohorts, we constructed negative control cohorts of individuals diagnosed with SARS-CoV-2 infection or acute myocardial infarction, stroke, cancer, low energy fracture, or head-trauma. Outcomes were SARS-CoV-2 infection, negative control outcomes (eg, mammography, prostate biopsy, operation for cataract, malignant melanoma, examination of eye and ear), and death. We used Cox regression to calculate adjusted incidence and mortality rate ratios (aIRR and aMRR). Results: Risks of SARS-CoV2 infection and all negative control outcomes were elevated in the vaccinated population, ranging from an aIRR of 1.15 (95% CI: 1.09-1.21) for eye examinations to 3.05 (95% CI: 2.24-4.14) for malignant melanoma. Conversely, the risk of death in the SARS-CoV-2 infected cohort and in all negative control cohorts was lower in vaccinated individuals, ranging from an aMRR of 0.23 (95% CI: 0.19-0.26) after SARS-CoV-2 infection to 0.50 (95% CI: 0.37-0.67) after stroke. Conclusion: Our findings indicate that observational studies of SARS-CoV-2 vaccine effectiveness may be subject to substantial confounding. Therefore, randomized trials are essential to establish vaccine efficacy after the emergence of new SARS-CoV-2 variants and the rollout of multiple booster vaccines.

Why was this study done: : After the emergence of new SARS-CoV-2 variants and the rollout of multiple booster SARS-CoV-2 vaccines, the impact of vaccination on risk of SARS-CoV-2 infection and death after the infection has mainly been explored in observational studies. We used negative control methods to investigate whether confounding affects the results of observational SARS-CoV-2 vaccine effectiveness studies. Findings: : We used Danish registry data obtained during the SARS-CoV-2 vaccine roll-out to conduct a nationwide, matched population-based cohort study of Danish residents 60­90 years in which we compared vaccinated individuals with non-vaccinated individuals. Compared with unvaccinated individuals, vaccinated individuals had increased risks of SARS-CoV2 infection but also had increased risks of all negative control outcomes (mammography, prostate biopsy, operation for cataract, malignant melanoma, examination of eye and ear). The risk of death after SARS-CoV2 infection was lower in the vaccinated cohort, as was the risk of death after acute myocardial infarction, stroke, cancer, low energy fracture, and head-trauma. Meaning: : The negative control methods indicate that observational studies of SARS-CoV-2 vaccine effectiveness may be prone to substantial confounding which may impact the observed associations. This bias may both lead to underestimation of vaccine effectiveness (increased risk of SARS-CoV2 infection among vaccinated individuals) and overestimation of the vaccine effectiveness (decreased risk of death after of SARS-CoV2 infection among vaccinated individuals). Our results highlight the need for randomized vaccine efficacy studies after the emergence of new SARS-CoV-2 variants and the rollout of multiple booster vaccines.

Benchmarking Mendelian randomization methods for causal inference using genome-wide association study summary statistics.

Mendelian randomization (MR), which utilizes genetic variants as instrumental variables (IVs), has gained popularity as a method for causal inference between phenotypes using genetic data. While efforts have been made to relax IV assumptions and develop new methods for causal inference in the presence of invalid IVs due to confounding, the reliability of MR methods in real-world applications remains uncertain. Instead of using simulated datasets, we conducted a benchmark study evaluating 16 two-sample summary-level MR methods using real-world genetic datasets to provide guidelines for the best practices. Our study focused on the following crucial aspects: type I error control in the presence of various confounding scenarios (e.g., population stratification, pleiotropy, and family-level confounders like assortative mating), the accuracy of causal effect estimates, replicability, and power. By comprehensively evaluating the performance of compared methods over one thousand exposure-outcome trait pairs, our study not only provides valuable insights into the performance and limitations of the compared methods but also offers practical guidance for researchers to choose appropriate MR methods for causal inference.

Broadcasters, receivers, functional groups of metabolites, and the link to heart failure by revealing metabolomic network connectivity.

BACKGROUND AND OBJECTIVE: Blood-based small molecule metabolites offer easy accessibility and hold significant potential for insights into health processes, the impact of lifestyle, and genetic variation on disease, enabling precise risk prevention. In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline. METHODS: We uncovered the interconnectivity of metabolites using data-driven and causal networks augmented with polygenic factors. Exploring the networks, we identified metabolite broadcasters, receivers, mediators, and subnetworks corresponding to functional classes of metabolites, and provided insights into the link between metabolomic architecture and regulation in health. We incorporated the network structure into the identification of metabolites associated with HF to control the effect of confounding metabolites. RESULTS: We identified metabolites associated with higher and lower risk of HF incidence, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. These associations were not confounded by the other metabolites due to uncovering the connectivity among metabolites and adjusting each association for the confounding metabolites. Examples of our findings include the direct influence of asparagine on glycine, both of which were inversely associated with HF. These two metabolites were influenced by polygenic factors and only essential amino acids, which are not synthesized in the human body and are obtained directly from the diet. CONCLUSION: Metabolites may play a critical role in linking genetic background and lifestyle factors to HF incidence. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates studying complex conditions like HF.