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The article in the ILR Review honors George J. Borjas as a pioneer in the study of immigration economics and quantitative social science. Borjas' work is characterized by rigorous evidence and thoughtful analysis of migration decisions and their economic impacts. His research on immigrant selectivity, assimilation, and economic mobility across generations has had a profound influence on labor economics and empirical methodology. Borjas' approach combines deep reflection on economic theory with meticulous attention to data, making his work a valuable model for scholars in the field. [Extracted from the article]

Analyzing heterogeneous treatment effects (HTEs) plays a crucial role in understanding the impacts of educational interventions. A standard practice for HTE analysis is to examine interactions between treatment status and preintervention participant characteristics, such as pretest scores, to identify how different groups respond to treatment. This study demonstrates that the identical patterns of HTE on test score outcomes can emerge either from variation in treatment effects due to a preintervention participant characteristic or from correlations between treatment effects and item easiness parameters. We demonstrate analytically and through simulation that these two scenarios cannot be distinguished if analysis is based on summary scores alone. We then describe a novel approach that identifies the relevant data-generating process by leveraging item-level data. We apply our approach to a randomized trial of a reading intervention in second grade and show that any apparent HTE by pretest ability is driven by the correlation between treatment effect size and item easiness. Our results highlight the potential of employing measurement principles in causal analysis, beyond their common use in test construction. [ABSTRACT FROM AUTHOR]

The reintegration of former armed group affiliates into formal politics is a fundamental challenge for post-conflict societies. Arguably, this challenge is even more difficult in settings where ethnic discrimination is pervasive, since discrimination implies comparatively higher barriers to political reintegration for racialized ex-combatants. In this paper, we explore how ethnicity and armed group affiliation interact to shape electoral prospects. Specifically, we investigate whether participants in a conjoint experiment in Colombia discriminate between political candidates of European and African descent and whether politicians of different ethnic identities are differentially punished for ties to armed groups. We find that participants display a strong aversion toward politicians with armed group affiliations and that they discriminate against Afro-Colombian candidates. Furthermore, we demonstrate that biases are additive, in the sense that Caucasian and Afro-Colombian candidates are equally punished for associating with armed groups. Finally, we explore heterogeneous treatment effects to shed light on sources of ethnic discrimination and aversion toward former armed group affiliates. [ABSTRACT FROM AUTHOR]

Instrumental variables are commonly used to estimate treatment effects in cases of imperfect compliance. However, if participation in the program is misreported, standard techniques can yield severely biased results. We present a new command, ivreg2m, that implements the mismeasured robust local average treatment-effect estimator developed by Calvi, Lewbel, and Tommasi (2022, Journal of Business and Economic Statistics 40: 1701–1717) and Tommasi and Zhang (2024b, Journal of Applied Econometrics, https://doi.org/10.1002/jae.3079), to estimate the heterogeneous treatment effect of a program in the presence of treatment noncompliance and misreporting. The ivreg2m command can be used as the preferred strategy in cases of exogenous (nondifferential) misclassification. [ABSTRACT FROM AUTHOR]

Objective: To assess the efficacy of pelvic floor muscle training and physical therapy interventions in patients with low back pain. Data sources: The Ovid–Medline, Ovid–Embase, Cochrane Library, CINAHL, Web of Science, and PEDro databases were searched for randomised, controlled trials published in English or Korean between database inception and September 2024. Review Methods: Studies providing pelvic floor muscle training in individuals with low back pain were included. The risk of bias using the Cochrane Risk of Bias 2 tool and the grading of recommendation, assessment, development, and evaluation (GRADE) system was used to evaluate the quality of evidence. The meta-analysis was performed using Review Manager software 5.4. Results: Nineteen studies were included in this review. Pelvic floor muscle training showed low certainty evidence for improving pain (standardised mean difference = −0.73, 95% CI [−1.10, −0.36]) and reflected a clinically meaningful reduction in pain. The evidence for disability improvement had a low certainty (mean difference = −5.21, 95% CI [−7.15, −3.26]) due to high heterogeneity. Substantial improvements in pain and disability were observed when pelvic floor muscle training was added to standard physical therapy, with low certainty of evidence supporting these findings. Whereas pelvic floor muscle training substantially improved pain compared to other interventions, there was no marked improvement in disability. Conclusion: Pelvic floor muscle training is potentially beneficial in addition to physical therapy for reducing low back pain, particularly in pregnancy-related cases. However, the evidence should be interpreted considering the quality and risk of bias. [ABSTRACT FROM AUTHOR]

Mental health conditions such as anxiety, depression, aggression, and inattention are common in autistic youth and are challenging to treat by community providers. We aim to parse the heterogeneity of autism based on dimensions of executive function and determine whether specific executive function profiles are differentially related to psychiatric symptoms. Parents of 397 well-characterized 8–14-year-old autistic children without an intellectual disability reported on their child's executive function skills and psychiatric symptoms. We applied a data-driven, graph theory-based, community-detection approach to a common executive function measure, revealing three distinct executive function profile subgroups. Despite having similar social challenges, the executive function subgroups differed on anxiety, aggression, affect, and inattention symptoms. Results support the need for more intensive subtyping with autistic youth to develop appropriate, individualized mental health treatments and supports. Characterizing youth through neurocognitive strengths and challenges can guide the development of precision medicine, allowing for more meaningful, specialized treatment. Many autistic people struggle with mental health problems like anxiety, depression, inattention, and aggression, which can be challenging to treat. Executive function challenges, which impact many autistic individuals, may serve as a risk factor for mental health problems or make treating mental health conditions more difficult. While some people respond well to medication or therapy, others do not. This study tried to understand if there are different subgroups of autistic young people who may have similar patterns of executive function strengths and challenges—like flexibility, planning, self-monitoring, and emotion regulation. Then, we investigated whether executive function subgroups were related to mental health problems in autistic youth. We found three different types of executive function subgroups in autistic youth, each with different patterns of mental health problems. This helps us identify specific profiles of executive function strengths and challenges that may be helpful with identifying personalized supports, services, and treatment strategies for mental health conditions. [ABSTRACT FROM AUTHOR]

We are addressing the problem of estimating conditional average treatment effects with a continuous treatment and a continuous response, using random forests. We explore two general approaches: building trees with a split rule that seeks to increase the heterogeneity of the treatment effect estimation and building trees to predict Y as a proxy target variable. We conduct a simulation study to investigate several aspects including the presence or absence of confounding and colliding effects and the merits of locally centering the treatment and/or the response. Our study incorporates both existing and new implementations of random forests. The results indicate that locally centering both the response and treatment variables is generally the best strategy, and both general approaches are viable. Additionally, we provide an illustration using data from the 1987 National Medical Expenditure Survey. [ABSTRACT FROM AUTHOR]

To improve the validity of self-report measures, researchers should control for response style (RS) effects, which can be achieved with IRTree models. A traditional IRTree model considers a response as a combination of distinct decision-making processes, where the substantive trait affects the decision on response direction, while decisions about choosing the middle category or extreme categories are largely determined by midpoint RS (MRS) and extreme RS (ERS). One limitation of traditional IRTree models is the assumption that all respondents utilize the same set of RS in their response strategies, whereas it can be assumed that the nature and the strength of RS effects can differ between individuals. To address this limitation, we propose a mixture multidimensional IRTree (MM-IRTree) model that detects heterogeneity in response strategies. The MM-IRTree model comprises four latent classes of respondents, each associated with a different set of RS traits in addition to the substantive trait. More specifically, the class-specific response strategies involve (1) only ERS in the "ERS only" class, (2) only MRS in the "MRS only" class, (3) both ERS and MRS in the "2RS" class, and (4) neither ERS nor MRS in the "0RS" class. In a simulation study, we showed that the MM-IRTree model performed well in recovering model parameters and class memberships, whereas the traditional IRTree approach showed poor performance if the population includes a mixture of response strategies. In an application to empirical data, the MM-IRTree model revealed distinct classes with noticeable class sizes, suggesting that respondents indeed utilize different response strategies. [ABSTRACT FROM AUTHOR]

In a recent 12-week smoking cessation trial, varenicline tartrate failed to show significant improvements in enhancing end-of-treatment abstinence when compared with placebo among adolescents and young adults. The original analysis aimed to assess the average effect across the entire population using timeline followback methods, which typically involve overdispersed binomial counts. We instead propose to investigate treatment effect heterogeneity among latent classes of participants using a Bayesian beta-binomial piecewise linear growth mixture model specifically designed to address longitudinal overdispersed binomial responses. Within each class, we fit a piecewise linear beta-binomial mixed model with random changepoints for each study group to detect critical windows of treatment efficacy. Using this model, we can cluster subjects who share similar characteristics, estimate the class-specific mean abstinence trends for each study group, and quantify the treatment effect over time within each class. Our analysis identified two classes of subjects: one comprising high-abstinent individuals, typically young adults and light smokers, in which varenicline led to improved abstinence; and another comprising low-abstinent individuals for whom varenicline showed no discernible effect. These findings highlight the importance of tailoring varenicline to specific participant subgroups, thereby advancing precision medicine in smoking cessation studies. [ABSTRACT FROM AUTHOR]

How does living on property taken from others affect voting behavior? Recent studies have argued that benefiting from historical violence leads to support for the far right. We extend this fledgling literature with new theoretical insights and original data from Israel, using case-specific variation in the nature of displacement to uncover heterogeneous treatment effects. Exploiting the coercion during the settlement of Jewish migrants on rural lands following the 1948 war, we show that living on lands taken from Palestinians consistently led to hawkish right-wing voting--even 70 years after the violence occurred and despite the widespread rejection of guilt over that violence. We also show that exposure to the ruins of the displaced villages increased right-wing voting and that the impact of intergroup contact is divergent: it decreased intolerant voting in most villages but increased it among Jewish communities that reside on violently taken land. Our results are robust when matching is used to account for several controls and spatiotemporal dependencies. [ABSTRACT FROM AUTHOR]

The cluster randomized crossover design has been proposed to improve efficiency over the traditional parallel-arm cluster randomized design. While statistical methods have been developed for designing cluster randomized crossover trials, they have exclusively focused on testing the overall average treatment effect, with little attention to differential treatment effects across subpopulations. Recently, interest has grown in understanding whether treatment effects may vary across pre-specified patient subpopulations, such as those defined by demographic or clinical characteristics. In this article, we consider the two-treatment two-period cluster randomized crossover design under either a cross-sectional or closed-cohort sampling scheme, where it is of interest to detect the heterogeneity of treatment effect via an interaction test. Assuming a patterned correlation structure for both the covariate and the outcome, we derive new sample size formulas for testing the heterogeneity of treatment effect with continuous outcomes based on linear mixed models. Our formulas also address unequal cluster sizes and therefore allow us to analytically assess the impact of unequal cluster sizes on the power of the interaction test in cluster randomized crossover designs. We conduct simulations to confirm the accuracy of the proposed methods, and illustrate their application in two real cluster randomized crossover trials. [ABSTRACT FROM AUTHOR]

When the primary endpoints in randomized clinical trials require long term follow-up or are costly to measure, it is often desirable to assess treatment effects on surrogate instead of clinical endpoints. Prior to adopting a surrogate endpoint for such purposes, the extent of its surrogacy on the primary endpoint must be assessed. There is a rich statistical literature on assessing surrogacy in the overall population, much of which is based on quantifying the proportion of treatment effect on the primary endpoint that is explained by the treatment effect on the surrogate endpoint. However, the surrogacy of an endpoint may vary across different patient subgroups according to baseline demographic characteristics, and limited methods are currently available to assess overall surrogacy in the presence of potential surrogacy heterogeneity. In this paper, we propose methods that incorporate covariates for baseline information, such as age, to improve overall surrogacy assessment. We use flexible semi-non-parametric modeling strategies to adjust for covariate effects and derive a robust estimate for the proportion of treatment effect of the covariate-adjusted surrogate endpoint. Simulation results suggest that the adjusted surrogate endpoint has greater proportion of treatment effect compared to the unadjusted surrogate endpoint. We apply the proposed method to data from a clinical trial of infliximab and assess the adequacy of the surrogate endpoint in the presence of age heterogeneity. [ABSTRACT FROM AUTHOR]

Instrumental-variables estimation is an approach commonly used to evaluate the effect of a program in case of noncompliance. However, when the binary treatment status is misreported, standard techniques are not sufficient to point identify and consistently estimate the effect of interest. We present a new command, ivbounds, that implements three partial identification strategies developed by Tommasi and Zhang (2024, Journal of Econometrics 238: 105556) to bound the heterogeneous treatment effect when both noncompliance and misreporting of treatment status are present. We illustrate the use of the command by reassessing the benefits of participating in the 401(k) pension plan on savings in the United States. [ABSTRACT FROM AUTHOR]

We propose a novel framework based on the RuleFit method to estimate heterogeneous treatment effect in randomized clinical trials. The proposed method estimates a rule ensemble comprising a set of prognostic rules, a set of prescriptive rules, as well as the linear effects of the original predictor variables. The prescriptive rules provide an interpretable description of the heterogeneous treatment effect. By including a prognostic term in the proposed model, the selected rule is represented as an heterogeneous treatment effect that excludes other effects. We confirmed that the performance of the proposed method was equivalent to that of other ensemble learning methods through numerical simulations and demonstrated the interpretation of the proposed method using a real data application. [ABSTRACT FROM AUTHOR]

Background: Chronic pain is one of the most common drivers of healthcare utilization and a marked domain for health disparities, as African American/Black populations experience high rates of chronic pain. Integrative Medical Group Visits (IMGV) combine mindfulness techniques, evidence-based integrative medicine, and medical group visits. In a parent randomized controlled trial, this approach was tested as an adjunct treatment in a diverse, medically underserved population with chronic pain and depression. Objective: To determine race-based heterogeneity in the effects of a mindfulness based treatment for chronic pain. Methods: This secondary analysis of the parent trial assessed heterogeneity of treatment effects along racialized identity in terms of primary patient-reported pain outcomes in a racially diverse sample suffering from chronic pain and depression. The analytic approach examined comorbidities and sociodemographics between racialized groups. RMANOVAs examined trajectories in pain outcomes (average pain, pain severity, and pain interference) over three timepoints (baseline, 9, and 21 weeks) between participants identifying as African American/Black (n = 90) vs White (n = 29) across both intervention and control conditions. Results: At baseline, African American/Black participants had higher pain severity and had significantly different age, work status, and comorbidity profiles. RMANOVA models also identified significant race-based differences in the response to the parent IMGV intervention. There was reduced pain severity in African American/Black subjects in the IMGV condition from baseline to 9 weeks. This change was not observed in White participants over this time period. However, there was a reduction in pain severity in White participants over the subsequent interval from 9 to 21 week where IMGV had no significant effect in African American/Black subjects during this latter time period. Conclusion: Interactions between pain and racialization require further investigation to understand how race-based heterogeneity in the response to integrative medicine treatments for chronic pain contribute to the broader landscape of health inequity. [ABSTRACT FROM AUTHOR]

Existing methods that use propensity scores for heterogeneous treatment effect estimation on non-experimental data do not readily extend to the case of more than two treatment options. In this work, we develop a new propensity score-based method for heterogeneous treatment effect estimation when there are three or more treatment options, and prove that it generates unbiased estimates. We demonstrate our method on a real patient registry of patients in Singapore with diabetic dyslipidemia. On this dataset, our method generates heterogeneous treatment recommendations for patients among three options: Statins, fibrates, and non-pharmacological treatment to control patients' lipid ratios (total cholesterol divided by high-density lipoprotein level). In our numerical study, our proposed method generated more stable estimates compared to a benchmark method based on a multi-dimensional propensity score. [ABSTRACT FROM AUTHOR]

Understanding whether and how treatment effects vary across subgroups is crucial to inform clinical practice and recommendations. Accordingly, the assessment of heterogeneous treatment effects based on pre-specified potential effect modifiers has become a common goal in modern randomized trials. However, when one or more potential effect modifiers are missing, complete-case analysis may lead to bias and under-coverage. While statistical methods for handling missing data have been proposed and compared for individually randomized trials with missing effect modifier data, few guidelines exist for the cluster-randomized setting, where intracluster correlations in the effect modifiers, outcomes, or even missingness mechanisms may introduce further threats to accurate assessment of heterogeneous treatment effect. In this article, the performance of several missing data methods are compared through a simulation study of cluster-randomized trials with continuous outcome and missing binary effect modifier data, and further illustrated using real data from the Work, Family, and Health Study. Our results suggest that multilevel multiple imputation and Bayesian multilevel multiple imputation have better performance than other available methods, and that Bayesian multilevel multiple imputation has lower bias and closer to nominal coverage than standard multilevel multiple imputation when there are model specification or compatibility issues. [ABSTRACT FROM AUTHOR]

Objective: To systematically review the effectiveness of guided telerehabilitation on improving functional performance in community-dwelling older adults. Data sources: Articles published in PubMed, Cochrane Library and Embase (Ovid) from 01 January 2010 up to 17 October 2023. Review Methods: Included studies had (1) a randomised controlled trial design, (2) an average population age of 65 years or older, (3) a home-based setting and (4) evaluated the effectiveness of functional performance outcome measures. The intervention was considered telerehabilitation when guided by a healthcare professional using video, audio and/or text communication technologies with a minimum frequency of once per week. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2020 statement guideline was followed. Methodological quality was appraised using the revised Cochrane Risk of Bias tool. Results: A total of 26 randomised controlled trials were included. Telerehabilitation had superior (N = 15), non-superior (N = 16) or non-inferior (N = 11) effectiveness for improving functional performance outcome measures compared to control interventions. No studies found the control intervention to be superior over telerehabilitation. Between study differences in intervention characteristics contributed to significant clinical heterogeneity. Five studies were found to present an overall 'low' risk of bias, 12 studies to present 'some' risk of bias and 9 studies to present an overall 'high' risk of bias. Conclusion: The findings suggest that telerehabilitation could be a promising alternative to in-person rehabilitation for improving functional performance in community-dwelling older adults. Additional well-designed studies with minimised bias are needed for a better understanding of effective telerehabilitation intervention strategies. [ABSTRACT FROM AUTHOR]

Given an exogenous treatment d and covariates x, an ordinary least-squares (OLS) estimator is often applied with a noncontinuous outcome y to find the effect of d, despite the fact that the OLS linear model is invalid. Also, when d is endogenous with an instrument z, an instrumental-variables estimator (IVE) is often applied, again despite the invalid linear model. Furthermore, the treatment effect is likely to be heterogeneous, say, µ 1(x), not a constant as assumed in most linear models. Given these problems, the question is then what kind of effect the OLS and IVE actually estimate. Under some restrictive conditions such as a "saturated model", the estimated effect is known to be a weighted average, say, E { ω (x) µ 1(x)}, but in general, OLS and the IVE applied to linear models with a noncontinuous outcome or heterogeneous effect fail to yield a weighted average of heterogeneous treatment effects. Recently, however, it has been found that E { ω (x) µ 1(x)} can be estimated by OLS and the IVE without those restrictive conditions if the "propensity-score residual" d − E (d| x) or the "instrument-score residual" z−E (z| x) is used. In this article, we review this recent development and provide a command for OLS and the IVE with the propensity- and instrument-score residuals, which are applicable to any outcome and any heterogeneous effect. [ABSTRACT FROM AUTHOR]

The estimation of heterogeneous treatment effects has attracted considerable interest in many disciplines, most prominently in medicine and economics. Contemporary research has so far primarily focused on continuous and binary responses where heterogeneous treatment effects are traditionally estimated by a linear model, which allows the estimation of constant or heterogeneous effects even under certain model misspecifications. More complex models for survival, count, or ordinal outcomes require stricter assumptions to reliably estimate the treatment effect. Most importantly, the noncollapsibility issue necessitates the joint estimation of treatment and prognostic effects. Model-based forests allow simultaneous estimation of covariate-dependent treatment and prognostic effects, but only for randomized trials. In this paper, we propose modifications to model-based forests to address the confounding issue in observational data. In particular, we evaluate an orthogonalization strategy originally proposed by Robinson (1988, Econometrica) in the context of model-based forests targeting heterogeneous treatment effect estimation in generalized linear models and transformation models. We found that this strategy reduces confounding effects in a simulated study with various outcome distributions. We demonstrate the practical aspects of heterogeneous treatment effect estimation for survival and ordinal outcomes by an assessment of the potentially heterogeneous effect of Riluzole on the progress of Amyotrophic Lateral Sclerosis. [ABSTRACT FROM AUTHOR]

College education features prominently in research on determinants of deaths from substance use disorders and self-harm—outcomes collectively referred to as “deaths of despair” (DoD). Limited attention has been given to whether the protective effects of college education on indicators of despair vary by individuals’ likelihood of college completion. We use data from the National Longitudinal Study of Adolescent to Adult Health for 6,145 individuals to test whether the protective effects of college completion on precursors to DoD vary according to individuals’ propensity to attain a college degree. Understanding whether the benefits of college education differ depending on the propensity to complete it is important for designing effective educational policies. Using the heterogeneous treatment effects approach, we find that individuals with a relatively low propensity for graduating from college but who complete it have a lower likelihood of depressive symptoms, binge drinking, prescription drug abuse, and hard drug use. [ABSTRACT FROM AUTHOR]

Treatment effect heterogeneity occurs when individual characteristics influence the effect of a treatment. We propose a novel approach that combines prognostic score matching and conditional inference trees to characterize effect heterogeneity of a randomized binary treatment. One key feature that distinguishes our method from alternative approaches is that it controls the Type I error rate, that is, the probability of identifying effect heterogeneity if none exists and retains the underlying subgroups. This feature makes our technique particularly appealing in the context of clinical trials, where there may be significant costs associated with erroneously declaring that effects differ across population subgroups. Treatment effect heterogeneity trees are able to identify heterogeneous subgroups, characterize the relevant subgroups and estimate the associated treatment effects. We demonstrate the efficacy of the proposed method using a comprehensive simulation study and illustrate our method using a nutrition trial dataset to evaluate effect heterogeneity within a patient population. [ABSTRACT FROM AUTHOR]

In cluster-randomized trials, sometimes the effect of the intervention being studied differs between clusters, commonly referred to as treatment effect heterogeneity. In the analysis of stepped wedge and cluster-randomized crossover trials, it is possible to include terms in outcome regression models to allow for such treatment effect heterogeneity yet this is not frequently considered. Outside of some simulation studies of specific cases where the outcome is binary, the impact of failing to include terms for treatment effect heterogeneity on the variance of the treatment effect estimator is unknown. We analytically examine the impact of failing to include terms for treatment effect heterogeneity on the variance of the treatment effect estimator, when outcomes are continuous. Using analysis of variance and feasible generalized least squares we provide expressions for this variance. For both the cluster-randomized crossover design and the stepped wedge design, our analytic derivations indicate that failing to include treatment effect heterogeneity results in the estimates for variance of the treatment effect that are too small, leading to inflation of type I error rates. We therefore recommend assessing the sensitivity of sample size calculations and conclusions drawn from the analysis of cluster randomized trials to the inclusion of treatment effect heterogeneity. [ABSTRACT FROM AUTHOR]

This primer systematizes the emerging literature on causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction to building and optimizing custom deep learning models and shows how to adapt them to estimate/predict heterogeneous treatment effects. It also discusses ongoing work to extend causal inference to settings where confounding is nonlinear, time-varying, or encoded in text, networks, and images. To maximize accessibility, we also introduce prerequisite concepts from causal inference and deep learning. The primer differs from other treatments of deep learning and causal inference in its sharp focus on observational causal estimation, its extended exposition of key algorithms, and its detailed tutorials for implementing, training, and selecting among deep estimators in TensorFlow 2 and PyTorch. [ABSTRACT FROM AUTHOR]

Causal analyses typically focus on average treatment effects. Yet for substantive research on topics like inequality, interest extends to treatments' distributional consequences. When individuals differ in their responses to treatment, three types of inequality may result. Treatment may shape inequalities between subgroups defined by pretreatment covariates, it may induce more inequality in one subgroup than another, or it may polarize people across multiple dimensions of well-being. We introduce a model, called a covariance regression, that captures all three types of inequality via the means, variances, and correlations between multiple outcomes. The model can test for heterogeneous treatment effects, quantify the heterogeneity, and explain its structure using covariates. Finding that a treatment creates inequalities could drive theoretical refinement and inform policy decisions (targeting groups where payoffs will be most predictable). We illustrate the utility of covariance regressions by analyzing the effects of sharing information about income inequality on redistributive preferences. [ABSTRACT FROM AUTHOR]

Treatment effect heterogeneity is commonly investigated in meta-analyses to identify if treatment effects vary across studies. When conducting an aggregate level data meta-analysis it is common to describe the magnitude of any treatment effect heterogeneity using the I-squared statistic, which is an intuitive and easily understood concept. The effect of a treatment might also vary across clusters in a cluster randomized trial, or across centres in multi-centre randomized trial, and it can be of interest to explore this at the analysis stage. In cross-over trials and other randomized designs, in which clusters or centres are exposed to both treatment and control conditions, this treatment effect heterogeneity can be identified. Here we derive and evaluate a comparable I-squared measure to describe the magnitude of heterogeneity in treatment effects across clusters or centres in randomized trials. We further show how this methodology can be used to estimate treatment effect heterogeneity in an individual patient data meta-analysis. [ABSTRACT FROM AUTHOR]

Background: Random design experiments are a powerful device for estimating average treatment effects, but evaluators sometimes seek to estimate the distribution of treatment effects. For example, an evaluator might seek to learn the proportion of treated units who benefit from treatment, the proportion who receive no benefit, and the proportion who are harmed by treatment. Method: Imbens and Rubin (I&R) recommend a Bayesian approach to drawing inferences about the distribution of treatment effects. Drawing on the I&R recommendations, this article explains the approach; provides computing algorithms for continuous, binary, ordered and countable outcomes; and offers simulated and real-world illustrations. Results: This article shows how the I&R approach leads to bounded uncertainty intervals for summary measures of the distribution of treatment effects. It clarifies the nature of those bounds and shows that they are typically informative. Conclusions: Despite identification issues, bounded solutions provide useful insight into the distribution of treatment effects. We recommend that evaluators incorporate analyses of the distribution of treatment effects into new studies and that evaluators revisit completed studies to estimate the distribution of treatment effects. [ABSTRACT FROM AUTHOR]

Modeling dose-response relationships of drugs is essential to understanding their safety effects on patients under realistic circumstances. While intention-to-treat analyses of clinical trials provide the effect of assignment to a particular drug and dose, they do not capture observed exposure after factoring in nonadherence and dropout. We develop a Bayesian method to flexibly model the dose-response relationships of binary outcomes with continuous treatment, permitting multiple evidence sources, treatment effect heterogeneity, and nonlinear dose-response curves. In an application, we examine the risk of excessive weight gain for patients with schizophrenia treated with the second-generation antipsychotics paliperidone, risperidone, or olanzapine in 14 clinical trials. We define exposure as total cumulative dose (daily dose × duration) and convert to units equivalent to 100 mg of olanzapine (OLZ doses). Averaging over the sample population of 5891 subjects, the median dose ranged from 0 (placebo randomized participants) to 6.4 OLZ doses (paliperidone randomized participants). We found paliperidone to be least likely to cause excessive weight gain across a range of doses. Compared with 0 OLZ doses, at 5.0 OLZ doses, olanzapine subjects had a 15.6% (95% credible interval: 6.7, 27.1) excess risk of weight gain; corresponding estimates for paliperidone and risperidone were 3.2% (1.5, 5.2) and 14.9% (0.0, 38.7), respectively. Moreover, compared with nonblack participants, black participants had a 6.8% (1.0, 12.4) greater risk of excessive weight gain at 10.0 OLZ doses of paliperidone. Nevertheless, our findings suggest that paliperidone is safer in terms of weight gain risk than risperidone or olanzapine for all participants at low to moderate cumulative OLZ doses. [ABSTRACT FROM AUTHOR]

A biomarker-adjusted treatment effect (BATE) model describes the effect of one treatment versus another on a subpopulation of patients defined by a biomarker. Such a model can be estimated from clinical trial data without relying on additional modeling assumptions, and the estimator can be made more efficient by incorporating information on the main effect of the biomarker on the outcome of interest. Motivated by an HIV trial known as THRIVE, we consider the use of auxiliary covariates, which are usually available in clinical trials and have been used in overall treatment comparisons, in estimating a BATE model. Such covariates can be incorporated using an existing augmentation technique. For a specific type of estimating functions for difference-based BATE models, the optimal augmentation depends only on the joint main effects of marker and covariates. For a ratio-based BATE model, this result holds in special cases but not in general; however, simulation results suggest that the augmentation based on the joint main effects of marker and covariates is virtually equivalent to the theoretically optimal augmentation, especially when the augmentation terms are estimated from data. Application of these methods and results to the THRIVE data yields new insights on the utility of baseline CD4 cell count and viral load as predictive or treatment selection markers. [ABSTRACT FROM AUTHOR]

Schools with very few and relatively low-performing marginalized students may be most likely to trigger social identity threats (including stereotype threats) that contribute to racial disparities. We test this hypothesis by assessing variation in the benefits of a self-affirmation intervention designed to counteract social identity threat in a randomized trial in all 11 middle schools in Madison, Wisconsin. We find that school context moderates the benefits of self-affirmation for black and Hispanic students’ grades, with partial support among standardized achievement outcomes. Self-affirmation reduced the very large racial achievement gap in overall grade point average by 12.5 percent in high-threat school contexts and had no effect in low-threat contexts. These self-affirmation activities have the potential to help close some of the largest racial/ethnic achievement gaps, though only in specific school contexts. [ABSTRACT FROM PUBLISHER]

The article presents information on a study conducted on stratified sampling of experiments by using cluster analysis, in order to improve the generalizations from experiments to a larger population. The method uses cluster analysis techniques to classify the given population into homogenous strata and then provides a distance-based approach for selecting units in each stratum of the experiment.