Complementary features of randomized controlled trials (RCTs) and observational studies (OSs) can be used jointly to estimate the average treatment effect of a target population. We propose a calibration weighting estimator that enforces the covariate balance between the RCT and OS, therefore improving the trial-based estimator's generalizability. Exploiting semiparametric efficiency theory, we propose a doubly robust augmented calibration weighting estimator that achieves the efficiency bound derived under the identification assumptions. A nonparametric sieve method is provided as an alternative to the parametric approach, which enables the robust approximation of the nuisance functions and data-adaptive selection of outcome predictors for calibration. We establish asymptotic results and confirm the finite sample performances of the proposed estimators by simulation experiments and an application on the estimation of the treatment effect of adjuvant chemotherapy for early-stage non-small cell lung patients after surgery.
Methods for extending -- generalizing or transporting -- inferences from a randomized trial to a target population involve conditioning on a large set of covariates that is sufficient for rendering the randomized and non-randomized groups exchangeable. Yet, decision-makers are often interested in examining treatment effects in subgroups of the target population defined in terms of only a few discrete covariates. Here, we propose methods for estimating subgroup-specific potential outcome means and average treatment effects in generalizability and transportability analyses, using outcome model-b…
Finite-sample bias-variance tradeoff with variables related to trial participation inserted into causal forest models for ensuring generalizability
Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School（预防医学系，内科，布莱根妇女医院和哈佛医学院）；Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University（流行病学科，医药齿学研究院，冈山大学）；Harris School of Public Policy, University of Chicago（哈里斯公共政策学院，芝加哥大学）
A randomized trial and an analysis of observational data designed to emulate the trial sample observations separately, but have the same eligibility criteria, collect information on some shared baseline covariates, and compare the effects of the same treatments on the same outcomes. Treatment effect estimates from the trial and its emulation can be compared to benchmark observational analysis methods. In a simplified setting with complete adherence to the assigned treatment strategy and no loss-to-follow-up, we show that benchmarking relies on an exchangeability condition between the populatio…
Longitudinal observational patient data can be used to investigate the causal effects of time-varying treatments on time-to-event outcomes. Several methods have been developed for controlling for the time-dependent confounding that typically occurs. The most commonly used is inverse probability weighted estimation of marginal structural models (MSM-IPTW). An alternative, the sequential trials approach, is increasingly popular, in particular in combination with the target trial emulation framework. This approach involves creating a sequence of `trials' from new time origins, restricting to indi…
With increasing data availability, causal effects can be evaluated across different data sets, both randomized controlled trials (RCTs) and observational studies. RCTs isolate the effect of the treatment from that of unwanted (confounding) co-occurring effects but they may suffer from unrepresentativeness, and thus lack external validity. On the other hand, large observational samples are often more representative of the target population but can conflate confounding effects with the treatment of interest. In this paper, we review the growing literature on methods for causal inference on combi…
Causal inference from observational data can be viewed as a missing data problem arising from a hypothetical population-scale randomized trial matched to the observational study. This links a target trial protocol with a corresponding generative predictive model for inference, providing a complete framework for transparent communication of causal assumptions and statistical uncertainty on treatment effects, without the need for counterfactuals. The intuitive foundation for the work is that a whole population randomized trial would provide answers to any observable causal question with certaint…
Generalizing conditional average treatment effects from nested randomized trials to all trial-eligible individuals
University of Waterloo（滑铁卢大学）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Brown University School of Public Health（布朗大学公共卫生学院）；Center for Gerontology & Healthcare Research, Brown University School of Public Health（布朗大学公共卫生学院老年学与医疗保健研究中心）
Transporting Trial Evidence Under Posterior Drift and Possible Hidden Confounding
Qiuzhen College, Tsinghua University（清华大学邱氏书院）；Yau Mathematical Sciences Center, Tsinghua University（清华大学丘成桐数学科学中心）；Beijing Institute of Mathematical Sciences and Applications（北京数学科学与应用研究院）
Assessing Vaccine Effectiveness in Observational Studies via Nested Trial Emulation
Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校吉林斯全球公共卫生学院生物统计系）；Nuffield Department of Population Health, University of Oxford（牛津大学努菲尔德人口健康系）
Investigators are increasingly using novel methods for extending (generalizing or transporting) causal inferences from a trial to a target population. In many generalizability and transportability analyses, the trial and the observational data from the target population are separately sampled, following a non-nested trial design. In practical implementations of this design, non-randomized individuals from the target population are often identified by conditioning on the use of a particular treatment, while individuals who used other candidate treatments for the same indication or individuals w…
Methods for extending -- generalizing or transporting -- inferences from a randomized trial to a target population involve conditioning on a large set of covariates that is sufficient for rendering the randomized and non-randomized groups exchangeable. Yet, decision-makers are often interested in examining treatment effects in subgroups of the target population defined in terms of only a few discrete covariates. Here, we propose methods for estimating subgroup-specific potential outcome means and average treatment effects in generalizability and transportability analyses, using outcome model-b…
Item-Level Heterogeneous Treatment Effects of Selective Serotonin Reuptake Inhibitors (SSRIs) on Depression: Implications for Inference, Generalizability, and Identification
Approximate Bayesian Bootstrap Procedures to Estimate Multilevel Treatment Effects in Observational Studies with Application to Type 2 Diabetes Treatment Regimens
When assessing causal effects, determining the target population to which the results are intended to generalize is a critical decision. Randomized and observational studies each have strengths and limitations for estimating causal effects in a target population. Estimates from randomized data may have internal validity but are often not representative of the target population. Observational data may better reflect the target population, and hence be more likely to have external validity, but are subject to potential bias due to unmeasured confounding. While much of the causal inference litera…
We consider how increasingly available observational data can be used to improve the design of randomized controlled trials (RCTs). We seek to design a prospective RCT, with the intent of using an Empirical Bayes estimator to shrink the causal estimates from our trial toward causal estimates obtained from an observational study. We ask: how might we design the experiment to better complement the observational study in this setting? We propose using an estimator that shrinks each component of the RCT causal estimator toward its observational counterpart by a factor proportional to its variance.…
Debiasing hazard-based, time-varying vaccine effects using vaccine-irrelevant infections: An observational extension of a pivotal Phase 3 COVID-19 vaccine efficacy trial
Recent work has made important contributions in the development of causally-interpretable meta-analysis. These methods transport treatment effects estimated in a collection of randomized trials to a target population of interest. Ideally, estimates targeted toward a specific population are more interpretable and relevant to policy-makers and clinicians. However, between-study heterogeneity not arising from differences in the distribution of treatment effect modifiers can raise difficulties in synthesizing estimates across trials. The existence of such heterogeneity, including variations in tre…
In the presence of heterogeneity between the randomized controlled trial (RCT) participants and the target population, evaluating the treatment effect solely based on the RCT often leads to biased quantification of the real-world treatment effect. To address the problem of lack of generalizability for the treatment effect estimated by the RCT sample, we leverage observational studies with large samples that are representative of the target population. This paper concerns evaluating treatment effects on survival outcomes for a target population and considers a broad class of estimands that are …
Dynamic models for estimating the effect of HAART on CD4 in observational studies: application to the Aquitaine Cohort study and the Swiss HIV Cohort Study
在许多随机临床试验中，高效抗逆转录病毒治疗（HAART）已被证明在增加CD 4计数方面有效。因为随机试验有一些局限性（例如，持续时间短，高度选择的受试者），使用观察性研究来评估它是很有趣的。这是具有挑战性的，因为治疗优先在患有重度疾病的受试者中开始，特别是在具有低CD 4计数的受试者中。这个一般性问题已经用依赖于反事实表述的边际结构模型（MSM）来处理。因果关系的另一种方法是基于动态模型。首先，我们提出了三个离散时间动态模型的基础上线性增量（LIM）：最简单的模型是由一个差分方程CD 4计数;第二个有一个平衡点;第三个模型是基于一个系统的两个差分方程，允许联合建模CD 4计数和病毒载量。然后，我们考虑连续时间模型的基础上常微分方程的随机效应（ODE-NLME）。这些机制模型允许在可用时并入生物学知识，这导致用于检测处理效应的能力增加。然而，在ODE-NLME模型中的推断从数值的角度来看是具有挑战性的，并且需要特定的方法和软件。LIM在一致性、精确性和复杂性方面是一个有价值的中间选择。不同的方法进行了比较，在模拟和适用于艾滋病毒队列（ANRS CO 3阿基坦队列和瑞士艾滋病毒队列研究）。
The ability to generalize experimental results from randomized control trials (RCTs) across locations is crucial for informing policy decisions in targeted regions. Such generalization is often hindered by the lack of identifiability due to unmeasured effect modifiers that compromise direct transport of treatment effect estimates from one location to another. We build upon sensitivity analysis in observational studies and propose an optimization procedure that allows us to get bounds on the treatment effects in targeted regions. Furthermore, we construct more informative bounds by balancing on…
Methods that infer causal dependence from observational data are central to many areas of science, including medicine, economics, and the social sciences. A variety of theoretical properties of these methods have been proven, but empirical evaluation remains a challenge, largely due to the lack of observational data sets for which treatment effect is known. We describe and analyze observational sampling from randomized controlled trials (OSRCT), a method for evaluating causal inference methods using data from randomized controlled trials (RCTs). This method can be used to create constructed ob…
Estimating treatment effects from observational data provides insights about causality guiding many real-world applications such as different clinical study designs, which are the formulations of trials, experiments, and observational studies in medical, clinical, and other types of research. In this paper, we describe causal inference for application in a novel clinical design called basket trial that tests how well a new drug works in patients who have different types of cancer that all have the same mutation. We propose a multi-task adversarial learning (MTAL) method, which incorporates fea…
Budgeted Active Experimentation for Treatment Effect Estimation from Observational and Randomized Data
Didi Chuxing（滴滴出行）；School of Statistics, East China Normal University（华东师范大学统计学院）；School of Mathematics and Statistics, Beijing Jiaotong University（北京交通大学数学与统计学院）；School of Computing and Information Technology, Great Bay University（大湾区大学计算与信息技术学院）；School of Statistics and Data Science, Shanghai University of Finance and Economics（上海财经大学统计与数据科学学院）；Institute of Data Science and Statistics, Shanghai University of Finance and Economics（上海财经大学数据科学与统计研究院）
Observing the unobserved confounding through its effects: toward randomized trial-like estimates from real-world survival data
Operations Research Center, Massachusetts Institute of Technology（麻省理工学院运营研究中心）；Sloan School of Management, Massachusetts Institute of Technology（麻省理工学院斯隆管理学院）；Department of Surgery, Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心外科部）；Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin（柏林夏里特医学院，柏林自由大学和洪堡大学联合成员）
Estimating marginal treatment effects from observational studies and indirect treatment comparisons: When are standardization-based methods preferable to those based on propensity score weighting?
Data from both a randomized trial and an observational study are sometimes simultaneously available for evaluating the effect of an intervention. The randomized data typically allows for reliable estimation of average treatment effects but may be limited in sample size and patient heterogeneity for estimating conditional average treatment effects for a broad range of patients. Estimates from the observational study can potentially compensate for these limitations, but there may be concerns about whether confounding and treatment effect heterogeneity have been adequately addressed. We propose a…
