Effects In Observational Studies Research Articles

Semiparametric estimation of average treatment effects in observational studies

AbstractWe propose a semiparametric method to estimate average treatment effects in observational studies based on the assumption of unconfoundedness. Assume that the propensity score model and outcome model are a general single index model, which are estimated by the kernel method and the unknown index parameter is estimated via linearized maximum rank correlation method. The proposed estimator is computationally tractable, allows for large dimension covariates and not involves the approximation of link functions. We showed that the proposed estimator is consistent and asymptotically normally distributed. In general, the proposed estimator is superior to existing methods when the model is incorrectly specified. We also provide an empirical analysis on the average treatment effect and average treatment effect on the treated of 401(k) eligibility on net financial assets.

Asymptotic properties of resampling‐based processes for the average treatment effect in observational studies with competing risks

AbstractIn observational studies with time‐to‐event outcomes, the g‐formula can be used to estimate a treatment effect in the presence of confounding factors. However, the asymptotic distribution of the corresponding stochastic process is complicated and thus not suitable for deriving confidence intervals or time‐simultaneous confidence bands for the average treatment effect. A common remedy are resampling‐based approximations, with Efron's nonparametric bootstrap being the standard tool in practice. We investigate the large sample properties of three different resampling approaches and prove their asymptotic validity in a setting with time‐to‐event data subject to competing risks. The usage of these approaches is demonstrated by an analysis of the effect of physical activity on the risk of knee replacement among patients with advanced knee osteoarthritis.

Household Air Pollution Interventions to Improve Health in Low- and Middle-Income Countries: An Official American Thoracic Society Research Statement.

Background: An estimated 3 billion people, largely in low- and middle-income countries, rely on unclean fuels for cooking, heating, and lighting to meet household energy needs. The resulting exposure to household air pollution (HAP) is a leading cause of pneumonia, chronic lung disease, and other adverse health effects. In the last decade, randomized controlled trials of clean cooking interventions to reduce HAP have been conducted. We aim to provide guidance on how to interpret the findings of these trials and how they should inform policy makers and practitioners.Methods: We assembled a multidisciplinary working group of international researchers, public health practitioners, and policymakers with expertise in household air pollution from within academia, the American Thoracic Society, funders, nongovernmental organizations, and global organizations, including the World Bank and the World Health Organization. We performed a literature search, convened four sessions via web conference, and developed consensus conclusions and recommendations via the Delphi method.Results: The committee reached consensus on 14 conclusions and recommendations. Although some trials using cleaner-burning biomass stoves or cleaner-cooking fuels have reduced HAP exposure, the committee was divided (with 55% saying no and 45% saying yes) on whether the studied interventions improved measured health outcomes.Conclusions: HAP is associated with adverse health effects in observational studies. However, it remains unclear which household energy interventions reduce exposure, improve health, can be scaled, and are sustainable. Researchers should engage with policy makers and practitioners working to scale cleaner energy solutions to understand and address their information needs.

Resampling-based confidence intervals and bands for the average treatment effect in observational studies with competing risks

The g-formula can be used to estimate the treatment effect while accounting for confounding bias in observational studies. With regard to time-to-event endpoints, possibly subject to competing risks, the construction of valid pointwise confidence intervals and time-simultaneous confidence bands for the causal risk difference is complicated, however. A convenient solution is to approximate the asymptotic distribution of the corresponding stochastic process by means of resampling approaches. In this paper, we consider three different resampling methods, namely the classical nonparametric bootstrap, the influence function equipped with a resampling approach as well as a martingale-based bootstrap version, the so-called wild bootstrap. For the latter, three sub-versions based on differing distributions of the underlying random multipliers are examined. We set up a simulation study to compare the accuracy of the different techniques, which reveals that the wild bootstrap should in general be preferred if the sample size is moderate and sufficient data on the event of interest have been accrued. For illustration, the resampling methods are further applied to data on the long-term survival in patients with early-stage Hodgkin’s disease.

A Capture-Recapture-based Ascertainment Probability Weighting Method for Effect Estimation With Under-ascertained Outcomes.

Outcome under-ascertainment, characterized by the incomplete identification or reporting of cases, poses a substantial challenge in epidemiologic research. While capture-recapture methods can estimate unknown case numbers, their role in estimating exposure effects in observational studies is not well established. This paper presents an ascertainment probability weighting framework that integrates capture-recapture and propensity score weighting. We propose a nonparametric estimator of effects on binary outcomes that combines exposure propensity scores with data from two conditionally independent outcome measurements to simultaneously adjust for confounding and under-ascertainment. Demonstrating its practical application, we apply the method to estimate the relationship between health care work and coronavirus disease 2019 testing in a Swedish region. We find that ascertainment probability weighting greatly influences the estimated association compared to conventional inverse probability weighting, underscoring the importance of accounting for under-ascertainment in studies with limited outcome data coverage. We conclude with practical guidelines for the method's implementation, discussing its strengths, limitations, and suitable scenarios for application.

Handling time-varying treatments in observational studies: A scoping review and recommendations.

Time-varying treatments are common in observational studies. However, when assessing treatment effects, the methodological framework has not been systematically established for handling time-varying treatments. This study aimed to examine the current methods for dealing with time-varying treatments in observational studies and developed practical recommendations. We searched PubMed from 2000 to 2021 for methodological articles about time-varying treatments, and qualitatively summarized the current methods for handling time-varying treatments. Subsequently, we developed practical recommendations through interactive internal group discussions and consensus by a panel of external experts. Of the 36 eligible reports (22 methodological reviews, 10 original studies, 2 tutorials and 2 commentaries), most examined statistical methods for time-varying treatments, and only a few discussed the overarching methodological process. Generally, there were three methodological components to handle time-varying treatments. These included the specification of treatment which may be categorized as three scenarios (i.e., time-independent treatment, static treatment regime, or dynamic treatment regime); definition of treatment status which could involve three approaches (i.e., intention-to-treat, per-protocol, or as-treated approach); and selection of analytic methods. Based on the review results, a methodological workflow and a set of practical recommendations were proposed through two consensus meetings. There is no consensus process for assessing treatment effects in observational studies with time-varying treatments. Previous efforts were dedicated to developing statistical methods. Our study proposed a stepwise workflow with practical recommendations to assist the practice.

Greater use of antihypertensive medications explains lower blood pressures and better control in statin-treated than statin-eligible untreated adults.

Statins appear to have greater antihypertensive effects in observational studies than in randomized controlled trials. This study assessed whether more frequent treatment of hypertension contributed to better blood pressure (BP, mmHg) control in statin-treated than statin-eligible untreated adults in observational studies. National Health and Nutrition Examination Surveys 2009-2020 data were analyzed for adults 21-75 years ( N = 3814) with hypertension (BP ≥140/≥90 or treatment). The 2013 American College of Cardiology/American Heart Association Cholesterol Guideline defined statin eligibility. The main analysis compared BP values and hypertension awareness, treatment, and control in statin-treated and statin-eligible but untreated adults. Multivariable logistic regression was used to assess the association of statin therapy to hypertension control and the contribution of antihypertensive therapy to that relationship. Among adults with hypertension in 2009-2020, 30.3% were not statin-eligible, 36.9% were on statins, and 32.8% were statin-eligible but not on statins. Statin-treated adults were more likely to be aware of (93.4 vs. 80.6%) and treated (91.4 vs. 70.7%) for hypertension than statin-eligible adults not on statins. The statin-treated group had 8.3 mmHg lower SBP (130.3 vs. 138.6), and 22.8% greater control (<140/<90: 69.0 vs. 46.2%; all P values <0.001). The association between statin therapy and hypertension control [odds ratio 1.94 (95% confidence interval 1.53-2.47)] in multivariable logistic regression was not significant after also controlling for antihypertensive therapy [1.29 (0.96-1.73)]. Among adults with hypertension, statin-treated adults have lower BP and better control than statin-eligible untreated adults, which largely reflects differences in antihypertensive therapy.

Mendelian randomization study using paired SNPs as instruments supports casual protective effect of overweight on Alzheimer’s disease

AbstractBackgroundMendelian Randomization (MR) is a powerful method of detecting causal effects in observational studies. However, choosing an appropriate SNP as an instrumental variable (IVar) is challenging because of difficulties in satisfying all assumption criteria. We proposed a novel strategy of creating IVar using paired SNPs between genes of interest, and tested this strategy in MR study examining causal relationships between being overweight and onset of Alzheimer’s disease (AD).MethodWe performed MR in the Health and Retirement Study (HRS) subsample of participants aged 75+, using the following binary variables: (1) ‘overweight’ BMI 25‐30 vs. (0) ‘normal’ BMI 18.5‐25, at ages 65‐75, as exposure; and (1) no AD vs. (0) AD onset at ages 75+, as outcome. We found significant association between ‘overweight’ BMI and lower odds of AD in logistic regression, and used MR to test causality. For IVar, we selected eight obesity‐related genes from the literature (ADIPOQ, FTO, LEP, LEPR, INSIG2, MC4R, PCSK1, PPARG), cross‐paired their SNPs, and used counts of minor alleles of the paired SNPs to create new ‘composite SNP’s. Analyses were stratified by sex and race, and non‐stratified. Initial models were estimated in SAS. Resulting summary statistics were used in R‐package MendelianRandomization. F‐value&gt;10 criteria was applied to ensure the IVar strength. The causal effect was evaluated using weighted median, Inverse‐Variance Weighted (IVW), and maximum likelihood (ML).ResultBeing overweight at ages 65‐75 had significant causal protective effect on AD onset after age 75 in White females (logistic regression: OR = 1.99 [higher odds for ‘overweight’ individuals to be in ‘no AD’ group], p‐value = 8.26E‐5; MR: number of instrument ‘SNP’s = 13, weighted median causal estimate = 1.27, p‐value = 1.91E‐5; IVW causal estimate = 1.14, p‐value = 2.30E‐08; ML causal estimate = 1.20, p‐value = 7.67E‐7). No causal effects were detected using single SNPs in the same genes.ConclusionThe proposed novel strategy of creating IVar for MR analysis, based on pairing SNPs between candidate genes, substantially improves the critical step of MR analysis: choosing efficient instrument SNPs. We successfully tested this new strategy in HRS data, and confirmed causal relationship between BMI and AD, specifically, significant causal protective effect of being overweight on AD onset later in life in white females.

2D score-based estimation of heterogeneous treatment effects

Abstract Statisticians show growing interest in estimating and analyzing heterogeneity in causal effects in observational studies. However, there usually exists a trade-off between accuracy and interpretability for developing a desirable estimator for treatment effects, especially in the case when there are a large number of features in estimation. To make efforts to address the issue, we propose a score-based framework for estimating the conditional average treatment effect (CATE) function in this article. The framework integrates two components: (i) leverage the joint use of propensity and prognostic scores in a matching algorithm to obtain a proxy of the heterogeneous treatment effects for each observation and (ii) utilize nonparametric regression trees to construct an estimator for the CATE function conditioning on the two scores. The method naturally stratifies treatment effects into subgroups over a 2d grid whose axis are the propensity and prognostic scores. We conduct benchmark experiments on multiple simulated data and demonstrate clear advantages of the proposed estimator over state-of-the-art methods. We also evaluate empirical performance in real-life settings, using two observational data from a clinical trial and a complex social survey, and interpret policy implications following the numerical results.

A Design-Based Perspective on Synthetic Control Methods

Since their introduction by Abadie and Gardeazabal, Synthetic Control (SC) methods have quickly become one of the leading methods for estimating causal effects in observational studies in settings with panel data. Formal discussions often motivate SC methods by the assumption that the potential outcomes were generated by a factor model. Here we study SC methods from a design-based perspective, assuming a model for the selection of the treated unit(s) and period(s). We show that the standard SC estimator is generally biased under random assignment. We propose a Modified Unbiased Synthetic Control (MUSC) estimator that guarantees unbiasedness under random assignment and derive its exact, randomization-based, finite-sample variance. We also propose an unbiased estimator for this variance. We document in settings with real data that under random assignment, SC-type estimators can have root mean-squared errors that are substantially lower than that of other common estimators. We show that such an improvement is weakly guaranteed if the treated period is similar to the other periods, for example, if the treated period was randomly selected. While our results only directly apply in settings where treatment is assigned randomly, we believe that they can complement model-based approaches even for observational studies.

Estimating causal effects in observational studies for survival data with a cure fraction using propensity score adjustment.

In observational studies, covariates are often confounding factors for treatment assignment. Such covariates need to be adjusted to estimate the causal treatment effect. For observational studies with survival outcomes, it is usually more challenging to adjust for the confounding covariates for causal effect estimation because of censoring. The challenge becomes even thornier when there exists a nonignorable cure fraction in the population. In this paper, we propose a causal effect estimation approach in observational studies for survival data with a cure fraction. We extend the absolute treatment effects on survival outcomes-including the restricted average causal effect and SPCE-to survival outcomes with cure fractions, and construct the corresponding causal effect estimators based on propensity score stratification. We prove the asymptotic properties of the proposed estimators and conduct simulation studies to evaluate their performances. As an illustration, the method is applied to a stomach cancerstudy.

Place of metformin in modern clinical guidelines for the treatment of type 2 diabetes mellitus

Diabetes mellitus is one of the most common endocrine diseases. Type 2 diabetes mellitus is associated with high cardiovascular morbidity and mortality and is a leading cause of chronic kidney disease and end-stage kidney disease. To date, for the treatment of patients with type 2 diabetes, hypoglycemic drugs are used that affect different parts of the pathogenesis. Metformin is a biguanide that is used in the treatment of type 2 diabetes mellitus; it is effective as monotherapy and in combination with other glucose-lowering medications. In addition to the treatment of diabetes mellitus, metformin is recommended and widely used for the treatment of prediabetes. Metformin is generally well-tolerated with minimal side effects and is affordable. Metformin is very effective in HbA1c lowering associated with some weight loss, but does not increase risk for hypoglycemia. With the use of metformin in monotherapy, the decrease in HbA1c levels is 1–2%. Despite the appearance in practice of a fairly large number of new, effective glucose-lowering drugs, metformin is still the drug of choice in many cases. And most diabetic associations recommend metformin as a first-line drug. Metformin has demonstrated many positive effects in observational studies in patients with CAD, heart failure, and chronic kidney disease. This review presents data on the efficacy and, pleiotropic effects of metformin. Possible adverse events are also discussed, including the risk of low level and deficiency of vitamin B12, gastrointestinal tract adverse events.

Ensuring valid inference for Cox hazard ratios after variable selection.

The problem of how to best select variables for confounding adjustment forms one of the key challenges in the evaluation of exposure effects in observational studies, and has been the subject of vigorous recent activity in causal inference. A major drawback of routine procedures is that there is no finite sample size at which they are guaranteed to deliver exposure effect estimators and associated confidence intervals with adequate performance. In this work, we will consider this problem when inferring conditional causal hazard ratios from observational studies under the assumption of no unmeasured confounding. The major complication that we face with survival data is that the key confounding variables may not be those that explain the censoring mechanism. In this paper, we overcome this problem using a novel and simple procedure that can be implemented using off-the-shelf software for penalized Cox regression. In particular, we will propose tests of the null hypothesis that the exposure has no effect on the considered survival endpoint, which are uniformly valid under standard sparsity conditions. Simulation results show that the proposed methods yield valid inferences even when covariates arehigh-dimensional.

Using Balancing Weights to Target the Treatment Effect on the Treated when Overlap is Poor.

Inverse probability weights are commonly used in epidemiology to estimate causal effects in observational studies. Researchers often focus on either the average treatment effect or the average treatment effect on the treated with inverse probability weighting estimators. However, poor overlap in the baseline covariates between the treated and control groups can produce extreme weights that can result in biased treatment effect estimates. One alternative to inverse probability weights are overlap weights, which target the population with the most overlap on observed covariates. Although estimates based on overlap weights produce less bias in such contexts, the causal estimand can be difficult to interpret. An alternative to model-based inverse probability weights are balancing weights, which directly target imbalances during the estimation process, rather than model fit. Here, we explore whether balancing weights allow analysts to target the average treatment effect on the treated in cases where inverse probability weights lead to biased estimates due to poor overlap. We conduct three simulation studies and an empirical application. We find that balancing weights often allow the analyst to still target the average treatment effect on the treated even when overlap is poor. We show that although overlap weights remain a key tool, more familiar estimands can sometimes be targeted by using balancing weights.

Cardiovascular diseases and exposure to integrase inhibitors: causal interpretation of treatment effect in observational studies.

Cardiovascular diseases and exposure to integrase inhibitors: causal interpretation of treatment effect in observational studies.

Estimating heterogeneous causal effects in observational studies using small area predictors

The official statistics produced by National Statistical Institutes are mainly used by policy makers to take decisions. In particular, when policy makers and decision takers would like to know the impact of a given policy, it is important to acknowledge the heterogeneity of the treatment effects for different domains. If the domain of interest is small with regard to its sample size, then the evaluator has entered the small area estimation (SAE) dilemma. Based on the modification of the Inverse Propensity Weighting estimator and the traditional small area predictors, new estimators of area specific average treatment effects are proposed for unplanned domains. A robustified version of the predictor against presence of the outliers is also developed. Analytical Mean Squared Error (MSE) estimators of the proposed predictors are derived. These methods provide a tool to map the policy impacts that can help to better target the treatment group(s). The properties of these small area estimators are illustrated by means of a design-based simulation using a real data set where the aim is to study the effects of permanent versus temporary contracts on the economic insecurity of households in different regions of Italy.

Inference of subgroup-level treatment effects via generic causal tree in observational studies

Exploring heterogeneity in causal effects has wide applications in the field of policy evaluation and decision-making. In recent years, researchers have begun employing machine learning methods to study causality, among which the most popular methods generally estimate heterogeneous treatment effects at the individual level. However, we argue that in large sample cases, identifying specific subgroups with heterogeneity will be more intuitive and intelligible for decision-making. In this paper, we provide a tree-based method, called “Generic Causal Tree” (GCT), to identify the subgroup-level treatment effects in observational studies. The tree is designed to grow by maximizing the disparity of treatment effects between subgroups, embedding a semiparametric framework for the improvement of treatment effect estimation. To accomplish valid statistical inference of the tree-based estimators of treatment effects, we adopt the honest estimation to separate tree-building and inference. In the simulation, we show that the Generic Causal Tree algorithm has distinct advantages in subgroup identification and gives estimation with higher accuracy compared with the other two benchmark methods. Also, we verify the effectiveness of statistical inference by Generic Causal Tree.

Estimation of average treatment effect based on a multi-index propensity score

BackgroundEstimating the average effect of a treatment, exposure, or intervention on health outcomes is a primary aim of many medical studies. However, unbalanced covariates between groups can lead to confounding bias when using observational data to estimate the average treatment effect (ATE). In this study, we proposed an estimator to correct confounding bias and provide multiple protection for estimation consistency.MethodsWith reference to the kernel function-based double-index propensity score (Ker.DiPS) estimator, we proposed the artificial neural network-based multi-index propensity score (ANN.MiPS) estimator. The ANN.MiPS estimator employed the artificial neural network to estimate the MiPS that combines the information from multiple candidate models for propensity score and outcome regression. A Monte Carlo simulation study was designed to evaluate the performance of the proposed ANN.MiPS estimator. Furthermore, we applied our estimator to real data to discuss its practicability.ResultsThe simulation study showed the bias of the ANN.MiPS estimators is very small and the standard error is similar if any one of the candidate models is correctly specified under all evaluated sample sizes, treatment rates, and covariate types. Compared to the kernel function-based estimator, the ANN.MiPS estimator usually yields smaller standard error when the correct model is incorporated in the estimator. The empirical study indicated the point estimation for ATE and its bootstrap standard error of the ANN.MiPS estimator is stable under different model specifications.ConclusionsThe proposed estimator extended the combination of information from two models to multiple models and achieved multiply robust estimation for ATE. Extra efficiency was gained by our estimator compared to the kernel-based estimator. The proposed estimator provided a novel approach for estimating the causal effects in observational studies.

A latent class model to multiply impute missing treatment indicators in observational studies when inferences of the treatment effect are made using propensity score matching.

Analysts often estimate treatment effects in observational studies using propensity score matching techniques. When there are missing covariate values, analysts can multiply impute the missing data to create m completed data sets. Analysts can then estimate propensity scores on each of the completed data sets, and use these to estimate treatment effects. However, there has been relatively little attention on developing imputation models to deal with the additional problem of missing treatment indicators, perhaps due to the consequences of generating implausible imputations. However, simply ignoring the missing treatment values, akin to a complete case analysis, could also lead to problems when estimating treatment effects. We propose a latent class model to multiply impute missing treatment indicators. We illustrate its performance through simulations and with data taken from a study on determinants of children's cognitive development. This approach is seen to obtain treatment effect estimates closer to the true treatment effect than when employing conventional imputation procedures as well as compared to a complete caseanalysis.

Combining parametric and nonparametric models to estimate treatment effects in observational studies.

Performing causal inference in observational studies requires we assume confounding variables are correctly adjusted for. In settings with few discrete-valued confounders, standard models can be employed. However, as the number of confounders increases these models become less feasible as there are fewer observations available for each unique combination of confounding variables. In this paper, we propose a new model for estimating treatment effects in observational studies that incorporates both parametric and nonparametric outcome models. By conceptually splitting the data, we can combine these models while maintaining a conjugate framework, allowing us to avoid the use of Markov chain Monte Carlo (MCMC) methods. Approximations using the central limit theorem and random sampling allow our method to be scaled to high-dimensional confounders. Through simulation studies we show our method can be competitive with benchmark models while maintaining efficient computation, and illustrate the method on a large epidemiological health survey.