On the use of propensity scores in principal causal effect estimation

Cognitive benefits of hearing intervention vary by risk of cognitive decline: A secondary analysis of the ACHIEVE trial

INTRODUCTION

Results from the Aging and Cognitive Health Evaluation in Elders (ACHIEVE) trial suggest hearing intervention may not reduce 3-year cognitive decline in all older adults with hearing loss but may be beneficial in certain groups. This secondary analysis investigated if participants with multiple risk factors for cognitive decline received greater benefits.

METHODS

We used a sample of dementia-free participants (N = 2692) from the Atherosclerosis Risk in Communities (ARIC) cohort to develop a predictive model for cognitive decline. The model was applied to baseline measures of ACHIEVE participants (N = 977) to estimate predicted risk. We tested an interaction between predicted risk and randomization to hearing intervention or health education control.

RESULTS

Among ACHIEVE participants in the top quartile of predicted risk, 3-year cognitive decline in the hearing intervention was 61.6% (95% confidence interval [CI]: 33.7%-94.1%) slower than the control.

DISCUSSION

The effect of hearing intervention on reducing 3-year cognitive decline was greatest among individuals with multiple baseline risk factors associated with faster cognitive decline.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03243422 HIGHLIGHTS: The Aging and Cognitive Health Evaluation in Elders (ACHIEVE) trial tested the effect of hearing intervention on cognitive decline. Participants were recruited from the Atherosclerosis Risk in Communities (ARIC) cohort or de novo from the local community. A 48% reduction in cognitive decline was observed in ARIC cohort participants. In this secondary analysis, there was an interaction between hearing intervention and predicted risk of cognitive decline. Among participants in the top quartile of predicted risk of cognitive decline, hearing intervention slowed cognitive decline by 62%.

Causal Inference Over a Subpopulation: The Effect of Malaria Vaccine in Women During Pregnancy

Preventing malaria during pregnancy is of critical importance, yet there are no approved malaria vaccines for pregnant women due to lack of efficacy results within this population. Conducting a randomized trial in pregnant women throughout the entire duration of pregnancy is impractical. Instead, a randomized trial was conducted among women of childbearing potential (WOCBP), and some participants became pregnant during the 2-year study. We explore a statistical method for estimating vaccine effect within the target subpopulation-women who can naturally become pregnant, namely, women who can become pregnant under a placebo condition-within the causal inference framework. Two vaccine effect estimators are employed to effectively utilize baseline characteristics and account for the fact that certain baseline characteristics were only available from pregnant participants. The first estimator considers all participants but can only utilize baseline variables collected from the entire participant pool. In contrast, the second estimator, which includes only pregnant participants, utilizes all available baseline information. Both estimators are evaluated numerically through simulation studies and applied to the WOCBP trial to assess vaccine effect against pregnancy malaria.

Principal quantile treatment effect estimation using principal scores

Intercurrent events and estimands play a key role in defining the treatment effects of interest precisely. Sometimes the median or other quantiles of outcomes in a principal stratum according to potential occurrence of intercurrent events are of interest in randomized clinical trials. Naïve analyses such as those based on the observed occurrence of the intercurrent events lead to biased results. Therefore, we propose principal quantile treatment effect estimators that can nonparametrically estimate the distribution of potential outcomes by principal score weighting without relying on the exclusion restriction assumption. Our simulation studies show that the proposed method works in situations where the median or quantiles may be regarded as the preferred population-level summary over the mean. We illustrate our proposed method by using data from a randomized controlled trial conducted on patients with nonerosive reflux disease.

Multiply robust estimation of principal causal effects with noncompliance and survival outcomes

Treatment noncompliance and censoring are two common complications in clinical trials. Motivated by the ADAPTABLE pragmatic clinical trial, we develop methods for assessing treatment effects in the presence of treatment noncompliance with a right-censored survival outcome. We classify the participants into principal strata, defined by their joint potential compliance status under treatment and control. We propose a multiply robust estimator for the causal effects on the survival probability scale within each principal stratum. This estimator is consistent even if one, sometimes two, of the four working models-on the treatment assignment, the principal strata, censoring, and the outcome-is misspecified. A sensitivity analysis strategy is developed to address violations of key identification assumptions, the principal ignorability and monotonicity. We apply the proposed approach to the ADAPTABLE trial to study the causal effect of taking low- versus high-dosage aspirin on all-cause mortality and hospitalization from cardiovascular diseases.

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.

Sensitivity analysis for principal ignorability violation in estimating complier and noncomplier average causal effects

An important strategy for identifying principal causal effects (popular estimands in settings with noncompliance) is to invoke the principal ignorability (PI) assumption. As PI is untestable, it is important to gauge how sensitive effect estimates are to its violation. We focus on this task for the common one-sided noncompliance setting where there are two principal strata, compliers and noncompliers. Under PI, compliers and noncompliers share the same outcome-mean-given-covariates function under the control condition. For sensitivity analysis, we allow this function to differ between compliers and noncompliers in several ways, indexed by an odds ratio, a generalized odds ratio, a mean ratio, or a standardized mean difference sensitivity parameter. We tailor sensitivity analysis techniques (with any sensitivity parameter choice) to several types of PI-based main analysis methods, including outcome regression, influence function (IF) based and weighting methods. We discuss range selection for the sensitivity parameter. We illustrate the sensitivity analyses with several outcome types from the JOBS II study. This application estimates nuisance functions parametrically - for simplicity and accessibility. In addition, we establish rate conditions on nonparametric nuisance estimation for IF-based estimators to be asymptotically normal - with a view to inform nonparametric inference.

Results of a cognitive behavior therapy-based intervention for antenatal anxiety on birth outcomes in Pakistan: a randomized control trial

Antenatal anxiety is among the risk factors for adverse birth outcomes, which are common in Pakistan. Between 2019 and 2022, we conducted a randomized controlled trial to evaluate the effects of the Happy Mother-Healthy Baby program, designed to reduce anxiety during pregnancy through use of Cognitive Behavior Therapy, on birth outcomes with 796 women in Rwalpindi, Pakistan. We performed intent-to-treat analysis and per protocol analyses. Intention-to-treat analyses showed no difference in the odds of low birthweight (LBW) (Adj. OR = 0.82, 95% CI 0.55-1.28 p = 0.37), preterm birth (PTB) (Adj. OR = 1.20 95% CI 0.83-1.71, p = 0.33) or small-for-gestational age (SGA) birth, (Adj. OR = 0.76, 95% CI 0.56-1.09, p = 0.16). Among completers who received ≥ 5 intervention sessions, the odds of LBW and SGA were 39% and 32% lower (Adj. OR = 0.61, 95% CI 0.43-0.87, p < 0.01; Adj. OR = 0.68, 95% CI 0.53-0.89, p < 0.01). The significant LBW and SGA results among the intervention completers suggest that the program may be effective when a sufficient dose is received. However, confirmation of these findings is needed due to the fact that randomization is not maintained in completer analyses.Clinical Trial Registration: ClinicalTrials.gov Identifier: NCT03880032, 19/03/2019.

Effects of Compliance to a Training Intervention for High School Students with ADHD

OBJECTIVE

We evaluated the effects of treatment compliance with the Challenging Horizons Program (CHP) for high school aged adolescents with attention-deficit hyperactivity disorder (ADHD).

METHOD

Participants were 185 high school aged adolescents (65% non-Hispanic White; 79% male) with a diagnosis of ADHD who were randomly assigned to either CHP or community control. Outcomes included parent-rated academic functioning, parent- and self-rated social-emotional functioning, and GPA. The complier average causal effect (CACE) was estimated using propensity-weighted models for youth engaging in ≥ 30 CHP individual sessions (15-20 min) across the academic year.

RESULTS

Most (78%) CHP participants engaged in≥30 CHP sessions. CACE analyses using latent growth curve modeling revealed significant treatment effects among treatment compliers across ratings of academic and social outcomes relative to similar control participants. For most outcomes, CACE estimates were larger than those found in intent-to-treat analyses, especially at 6-months follow-up.

CONCLUSIONS

Compliance with 30 or more individual CHP sessions appeared to be an attainable threshold associated with incremental gains across several academic and social outcomes. Effects of compliance were amplified at 6-months follow-up, supporting the hypothesized theory of change of training interventions. Future work should focus on facilitators of treatment engagement and feasibility of the CHP as delivered by high school personnel.

User-defined outcomes of the Danish cardiovascular screening (DANCAVAS) trial: A post hoc analyses of a population-based, randomised controlled trial

BACKGROUND

The Danish cardiovascular screening (DANCAVAS) trial, a nationwide trial designed to investigate the impact of cardiovascular screening in men, did not decrease all-cause mortality, an outcome decided by the investigators. However, the target group may have varied preferences. In this study, we aimed to evaluate whether men aged 65 to 74 years requested a CT-based cardiovascular screening examination and to assess its impact on outcomes determined by their preferences.

METHODS AND FINDINGS

This is a post hoc study of the randomised DANCAVAS trial. All men 65 to 74 years of age residing in specific areas of Denmark were randomised (1:2) to invitation-to-screening (16,736 men, of which 10,471 underwent screening) or usual-care (29,790 men). The examination included among others a non-contrast CT scan (to assess the coronary artery calcium score and aortic aneurysms). Positive findings prompted preventive treatment with atorvastatin, aspirin, and surveillance/surgical evaluation. The usual-care group remained unaware of the trial and the assignments. The user-defined outcome was based on patient preferences and determined through a survey sent in January 2023 to a random sample of 9,095 men from the target group, with a 68.0% response rate (6,182 respondents). Safety outcomes included severe bleeding and mortality within 30 days after cardiovascular surgery. Analyses were performed on an intention-to-screen basis. Prevention of stroke and myocardial infarction was the primary motivation for participating in the screening examination. After a median follow-up of 6.4 years, 1,800 of 16,736 men (10.8%) in the invited-to-screening group and 3,420 of 29,790 (11.5%) in the usual-care group experienced an event (hazard ratio (HR), 0.93 (95% confidence interval (CI), 0.88 to 0.98; p = 0.010); number needed to invite at 6 years, 148 (95% CI, 80 to 986)). A total of 324 men (1.9%) in the invited-to-screening group and 491 (1.7%) in the usual-care group had an intracranial bleeding (HR, 1.17; 95% CI, 1.02 to 1.35; p = 0.029). Additionally, 994 (5.9%) in the invited-to-screening group and 1,722 (5.8%) in the usual-care group experienced severe gastrointestinal bleeding (HR, 1.02; 95% CI, 0.95 to 1.11; p = 0.583). No differences were found in mortality after cardiovascular surgery. The primary limitation of the study is that exclusive enrolment of men aged 65 to 74 renders the findings non-generalisable to women or men of other age groups.

CONCLUSION

In this comprehensive population-based cardiovascular screening and intervention program, we observed a reduction in the user-defined outcome, stroke and myocardial infarction, but entail a small increased risk of intracranial bleeding.

TRIAL REGISTRATION

ISRCTN Registry number, ISRCTN12157806 https://www.isrctn.com/ISRCTN12157806.

The Effect of CenteringPregnancy Group Antenatal Care on Maternal, Birth, and Neonatal Outcomes Among Low-Risk Women in the Netherlands: A Stepped-Wedge Cluster Randomized Trial

INTRODUCTION

This study was carried out to assess the effects of participating in CenteringPregnancy (CP) on maternal, birth, and neonatal outcomes among low-risk pregnant women in the Netherlands.

METHODS

A total of 2124 pregnant women in primary care were included in the study. Data were derived from the Dutch national database, Perined, complemented with data from questionnaires completed by pregnant women. A stepwise-wedge design was employed; multilevel intention-to-treat analyses and propensity score matching were the main analytic approaches. Propensity score matching resulted in sample sizes of 305 nulliparous women in both the individual care (IC) and the matched control group (control-IC) and 267 in the CP and control-CP groups. For multiparous women, 354 matches were found for IC and control-IC groups and 152 for CP and control-CP groups. Main outcome measures were maternal, birth, and neonatal outcomes.

RESULTS

Compared with the control-CP group receiving standard antenatal care, nulliparous women participating in CP had a lower risk of maternal hypertensive disorders (odds ratio [OR], 0.53; 95% CI, 0.30-0.93) and for the composite adverse maternal outcome (OR, 0.52; 95% CI, 0.33-0.82). Breastfeeding initiation rates were higher amongst nulliparous (OR, 2.23; 95% CI, 134-3.69) and multiparous women (OR, 1.62; 95% CI, 1.00-2.62) participating in CP compared with women in the control-CP group.

CONCLUSION

Nulliparous women in CP were at lower risk of developing hypertensive disorders during pregnancy and, consequently, at lower risk of having adverse maternal outcomes. The results confirmed our hypothesis that both nulliparous and multiparous women who participated in CP would have higher breastfeeding rates compared with women receiving standard antenatal care.

Principal stratification analysis of noncompliance with time-to-event outcomes

Post-randomization events, also known as intercurrent events, such as treatment noncompliance and censoring due to a terminal event, are common in clinical trials. Principal stratification is a framework for causal inference in the presence of intercurrent events. The existing literature on principal stratification lacks generally applicable and accessible methods for time-to-event outcomes. In this paper, we focus on the noncompliance setting. We specify 2 causal estimands for time-to-event outcomes in principal stratification and provide a nonparametric identification formula. For estimation, we adopt the latent mixture modeling approach and illustrate the general strategy with a mixture of Bayesian parametric Weibull-Cox proportional hazards model for the outcome. We utilize the Stan programming language to obtain automatic posterior sampling of the model parameters. We provide analytical forms of the causal estimands as functions of the model parameters and an alternative numerical method when analytical forms are not available. We apply the proposed method to the ADAPTABLE (Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness) trial to evaluate the causal effect of taking 81 versus 325 mg aspirin on the risk of major adverse cardiovascular events. We develop the corresponding R package PStrata.

A mixed model approach to estimate the survivor average causal effect in cluster-randomized trials

In many medical studies, the outcome measure (such as quality of life, QOL) for some study participants becomes informatively truncated (censored, missing, or unobserved) due to death or other forms of dropout, creating a nonignorable missing data problem. In such cases, the use of a composite outcome or imputation methods that fill in unmeasurable QOL values for those who died rely on strong and untestable assumptions and may be conceptually unappealing to certain stakeholders when estimating a treatment effect. The survivor average causal effect (SACE) is an alternative causal estimand that surmounts some of these issues. While principal stratification has been applied to estimate the SACE in individually randomized trials, methods for estimating the SACE in cluster-randomized trials are currently limited. To address this gap, we develop a mixed model approach along with an expectation-maximization algorithm to estimate the SACE in cluster-randomized trials. We model the continuous outcome measure with a random intercept to account for intracluster correlations due to cluster-level randomization, and model the principal strata membership both with and without a random intercept. In simulations, we compare the performance of our approaches with an existing fixed-effects approach to illustrate the importance of accounting for clustering in cluster-randomized trials. The methodology is then illustrated using a cluster-randomized trial of telecare and assistive technology on health-related QOL in the elderly.

Principal stratification for quantile causal effects under partial compliance

Within the principal stratification framework in causal inference, the majority of the literature has focused on binary compliance with an intervention and modelling means. Yet in some research areas, compliance is partial, and research questions-and hence analyses-are concerned with causal effects on (possibly high) quantiles rather than on shifts in average outcomes. Modelling partial compliance is challenging because it can suffer from lack of identifiability. We develop an approach to estimate quantile causal effects within a principal stratification framework, where principal strata are defined by the bivariate vector of (partial) compliance to the two levels of a binary intervention. We propose a conditional copula approach to impute the missing potential compliance and estimate the principal quantile treatment effect surface at high quantiles, allowing the copula association parameter to vary with the covariates. A bootstrap procedure is used to estimate the parameter to account for inflation due to imputation of missing compliance. Moreover, we describe precise assumptions on which the proposed approach is based, and investigate the finite sample behavior of our method by a simulation study. The proposed approach is used to study the 90th principal quantile treatment effect of executive stay-at-home orders on mitigating the risk of COVID-19 transmission in the United States.

Predictors of compliance with higher dose omega-3 fatty acid supplementation during pregnancy and implications for the risk of prematurity: exploratory analysis of the ORIP randomised trial

BACKGROUND

Intention-to-treat analyses of the Omega-3 to Reduce the Incidence of Prematurity (ORIP) trial found that omega-3 (n-3) fatty acid supplementation reduces the risk of prematurity in the subgroup of women with a singleton pregnancy and low n-3 status early in pregnancy, but not overall. However, results may have been influenced by less-than-optimal compliance.

OBJECTIVES

To identify predictors of compliance with n-3 supplementation and determine treatment effects among compliers.

DESIGN

Exploratory analyses of a multicentre-blinded randomised trial.

SETTING

6 tertiary care centres in Australia.

PARTICIPANTS

5328 singleton pregnancies.

INTERVENTIONS

Daily capsules containing 900 mg n-3 long-chain polyunsaturated fatty acids or vegetable oil, consumed from before 20 weeks gestation until 34 weeks gestation.

OUTCOME MEASURES

Early preterm (<34 weeks gestation) and preterm birth (<37 weeks gestation). Women were considered compliant if they reported missing less than a third of their allocated capsules in the previous week during a mid-pregnancy appointment.

RESULTS

Among 2654 singleton pregnancies in the n-3 intervention group, 1727 (65%) were deemed compliant with supplementation. Maternal characteristics associated with compliance included age, years of full-time education, consuming alcohol but not smoking in the 3 months leading up to pregnancy, fewer previous births and taking dietary supplements at enrolment. Based on complier average causal effects, n-3 supplementation reduced the risk of preterm birth in compliers (relative risk=0.76; 95% CI 0.60 to 0.97), but not early preterm birth (relative risk=0.80; 95% CI 0.44 to 1.46). Consistent with intention-to-treat analyses, the lack of an overall effect on early preterm birth in compliers appeared to be due to beneficial effects in women with low n-3 status at enrolment but not women with replete status.

CONCLUSIONS

Results in compliers were similar to those from intention-to-treat analyses, suggesting that non-compliance was not a major factor in explaining outcomes from the ORIP trial.

TRIAL REGISTRATION NUMBER

ACTRN12613001142729.

Sensitivity analyses for the principal ignorability assumption using multiple imputation

In the context of clinical trials, there is interest in the treatment effect for subpopulations of patients defined by intercurrent events, namely disease-related events occurring after treatment initiation that affect either the interpretation or the existence of endpoints. With the principal stratum strategy, the ICH E9(R1) guideline introduces a formal framework in drug development for defining treatment effects in such subpopulations. Statistical estimation of the treatment effect can be performed based on the principal ignorability assumption using multiple imputation approaches. Principal ignorability is a conditional independence assumption that cannot be directly verified; therefore, it is crucial to evaluate the robustness of results to deviations from this assumption. As a sensitivity analysis, we propose a joint model that multiply imputes the principal stratum membership and the outcome variable while allowing different levels of violation of the principal ignorability assumption. We illustrate with a simulation study that the joint imputation model-based approaches are superior to naive subpopulation analyses. Motivated by an oncology clinical trial, we implement the sensitivity analysis on a time-to-event outcome to assess the treatment effect in the subpopulation of patients who discontinued due to adverse events using a synthetic dataset. Finally, we explore the potential usage and provide interpretation of such analyses in clinical settings, as well as possible extension of such models in more general cases.

Using principal stratification in analysis of clinical trials

The ICH E9(R1) addendum (2019) proposed principal stratification (PS) as one of five strategies for dealing with intercurrent events. Therefore, understanding the strengths, limitations, and assumptions of PS is important for the broad community of clinical trialists. Many approaches have been developed under the general framework of PS in different areas of research, including experimental and observational studies. These diverse applications have utilized a diverse set of tools and assumptions. Thus, need exists to present these approaches in a unifying manner. The goal of this tutorial is threefold. First, we provide a coherent and unifying description of PS. Second, we emphasize that estimation of effects within PS relies on strong assumptions and we thoroughly examine the consequences of these assumptions to understand in which situations certain assumptions are reasonable. Finally, we provide an overview of a variety of key methods for PS analysis and use a real clinical trial example to illustrate them. Examples of code for implementation of some of these approaches are given in Supplemental Materials.

Translating questions to estimands in randomized clinical trials with intercurrent events

Intercurrent (post-treatment) events occur frequently in randomized trials, and investigators often express interest in treatment effects that suitably take account of these events. Contrasts that naively condition on intercurrent events do not have a straight-forward causal interpretation, and the practical relevance of other commonly used approaches is debated. In this work, we discuss how to formulate and choose an estimand, beyond the marginal intention-to-treat effect, from the point of view of a decision maker and drug developer. In particular, we argue that careful articulation of a practically useful research question should either reflect decision making at this point in time or future drug development. Indeed, a substantially interesting estimand is simply a formalization of the (plain English) description of a research question. A common feature of estimands that are practically useful is that they correspond to possibly hypothetical but well-defined interventions in identifiable (sub)populations. To illustrate our points, we consider five examples that were recently used to motivate consideration of principal stratum estimands in clinical trials. In all of these examples, we propose alternative causal estimands, such as conditional effects, sequential regime effects, and separable effects, that correspond to explicit research questions of substantial interest.

Handling parametric assumptions in principal causal effect estimation using Gaussian mixtures

Given the latent stratum membership, principal stratification models with continuous outcomes naturally fit in the parametric estimation framework of Gaussian mixtures. However, with models that are not nonparametrically identified, relying on parametric mixture modeling has been mostly discouraged as a way of identifying principal effects. This study revisits this rather deserted use of parametric mixture modeling, which may open up various possibilities in principal stratification modeling. The main problem with using the parametric mixture modeling approach is that it is hard to assess the quality of principal effect estimates given its reliance on parametric conditions. As a way of assessing the estimation quality in this situation, this study proposes that we use parametric mixture modeling in two different ways, with and without the assurance of nonparametric identification. The key identifying assumption employed in this study is the moving exclusion restriction, a flexible version of the standard exclusion restriction assumption. This assumption is used as a temporary vehicle to help assess the quality of principal effect estimates obtained relying on parametric mixture modeling. The study presents promising results, showing the possibility of using parametric mixture modeling as an accessible tool for causal inference.

Estimating the treatment effect for adherers using multiple imputation

Randomized controlled trials are considered the gold standard to evaluate the treatment effect (estimand) for efficacy and safety. According to the recent International Council on Harmonization (ICH)-E9 addendum (R1), intercurrent events (ICEs) need to be considered when defining an estimand, and principal stratum is one of the five strategies to handle ICEs. Qu et al. (2020, Statistics in Biopharmaceutical Research 12:1-18) proposed estimators for the adherer average causal effect (AdACE) for estimating the treatment difference for those who adhere to one or both treatments based on the causal-inference framework, and demonstrated the consistency of those estimators; however, this method requires complex custom programming related to high-dimensional numeric integrations. In this article, we implemented the AdACE estimators using multiple imputation (MI) and constructed confidence intervals (CIs) through bootstrapping. A simulation study showed that the MI-based estimators provided consistent estimators with the nominal coverage probabilities of CIs for the treatment difference for the adherent populations of interest. As an illustrative example, the new method was applied to data from a real clinical trial comparing two types of basal insulin for patients with type 1 diabetes.

Bayesian phase II clinical trial design with noncompliance

Noncompliance issue is common in early phase clinical trials; and may lead to biased estimation of the intent-to-treat effect and incorrect conclusions for the clinical trial. In this work, we propose a Bayesian approach for sequentially monitoring the phase II randomized clinical trials that takes account for the noncompliance information. We adopt the principal stratification framework and propose to use Bayesian additive regression trees for selecting useful baseline covariates and estimating the complier average causal effect (CACE) for both efficacy and toxicity outcomes. The decision of early termination or not is then made adaptively based on the estimated CACE from the accumulated data. Simulation studies have confirmed the excellent performance of the proposed design in the presence of noncompliance.

Randomized Trial Replication Using Observational Data for Comparative Effectiveness of Secukinumab and Ustekinumab in Psoriasis: A Study From the British Association of Dermatologists Biologics and Immunomodulators Register

Importance

Treatments for psoriasis may be less effective in everyday practice than in clinical trials. Emulating a target trial using data from the British Association of Dermatologists Biologics and Immunomodulators Register (BADBIR) can provide treatment effect estimates that are robust and can inform both clinicians and regulatory bodies.

Objectives

To assess the comparative effectiveness of ustekinumab and secukinumab in patients with psoriasis, and to test whether the relative effectiveness estimate of the CLEAR trial, a randomized clinical trial that compared secukinumab with ustekinumab for psoriasis, can be replicated.

Design, Setting, and Participants

This comparative effectiveness research study used a target trial emulation approach and was performed between November 2007 and August 2019. Data were obtained from BADBIR, a multicenter longitudinal pharmacovigilance register of patients with moderate to severe psoriasis in the United Kingdom and Republic of Ireland. Participants had chronic plaque psoriasis, were 18 years or older, and had at least 1 record of a Psoriasis Area and Severity Index (PASI) of 12 or higher before their initiation to secukinumab or ustekinumab. Propensity score (PS) 1:1 matched analysis and inverse probability treatment weighted analysis were performed.

Main Outcomes and Measures

The primary outcomes were the risk ratio (RR) and the risk difference (RD) for achieving PASI of 2 or lower after 12 months of therapy for secukinumab compared with ustekinumab. Methods to account for missing outcome data were complete case analysis, nonresponder imputation, last observation carried forward, inverse probability of censoring weighting, and multiple imputation. Regulatory and estimate agreement metrics were used to benchmark the effect estimates in this study against those in the CLEAR trial.

Results

A total of 1231 patients were included in the analysis, with 917 receiving ustekinumab and 314 receiving secukinumab. Secukinumab was superior to ustekinumab in all analyses, except under the nonresponder imputation method, in the proportion of participants achieving a PASI of 2 or lower (PS-weighted complete case analysis: RR, 1.28 [95% CI, 1.06-1.55]; RD, 11.9% [1.6-22.1]). All analyses, except for nonresponder imputation, reached regulatory agreement in both PS-matching and PS-weighted analyses.

Conclusions and Relevance

This comparative effectiveness study found that secukinumab resulted in more patients achieving a PASI of 2 or lower after 12 months of therapy compared with ustekinumab in patients with psoriasis. Target trial emulation in this study resulted in regulatory and estimate agreement with the CLEAR randomized clinical trial; further such studies may help fill the evidence gap when comparing other systemic therapies for psoriasis.

Principal stratum strategy: Potential role in drug development

A randomized trial allows estimation of the causal effect of an intervention compared to a control in the overall population and in subpopulations defined by baseline characteristics. Often, however, clinical questions also arise regarding the treatment effect in subpopulations of patients, which would experience clinical or disease related events post-randomization. Events that occur after treatment initiation and potentially affect the interpretation or the existence of the measurements are called intercurrent events in the ICH E9(R1) guideline. If the intercurrent event is a consequence of treatment, randomization alone is no longer sufficient to meaningfully estimate the treatment effect. Analyses comparing the subgroups of patients without the intercurrent events for intervention and control will not estimate a causal effect. This is well known, but post-hoc analyses of this kind are commonly performed in drug development. An alternative approach is the principal stratum strategy, which classifies subjects according to their potential occurrence of an intercurrent event on both study arms. We illustrate with examples that questions formulated through principal strata occur naturally in drug development and argue that approaching these questions with the ICH E9(R1) estimand framework has the potential to lead to more transparent assumptions as well as more adequate analyses and conclusions. In addition, we provide an overview of assumptions required for estimation of effects in principal strata. Most of these assumptions are unverifiable and should hence be based on solid scientific understanding. Sensitivity analyses are needed to assess robustness of conclusions.

Post-randomization Biomarker Effect Modification Analysis in an HIV Vaccine Clinical Trial

While the HVTN 505 trial showed no overall efficacy of the tested vaccine to prevent HIV infection over placebo, markers measuring immune response to vaccination were strongly correlated with infection. This finding generated the hypothesis that some marker-defined vaccinated subgroups were partially protected whereas others had their risk increased. This hypothesis can be assessed using the principal stratification framework (Frangakis and Rubin, 2002) for studying treatment effect modification by an intermediate response variable, using methods in the sub-field of principal surrogate (PS) analysis that studies multiple principal strata. Unfortunately, available methods for PS analysis require an augmented study design not available in HVTN 505, and make untestable structural risk assumptions, motivating a need for more robust PS methods. Fortunately, another sub-field of principal stratification, survivor average causal effect (SACE) analysis (Rubin, 2006) - which studies effects in a single principal stratum - provides many methods not requiring an augmented design and making fewer assumptions. We show how, for a binary intermediate response variable, methods developed for SACE analysis can be adapted to PS analysis, providing new and more robust PS methods. Application to HVTN 505 supports that the vaccine partially protected individuals with vaccine-induced T-cells expressing certain combinations of functions.

Clarifying causal mediation analysis for the applied researcher: Defining effects based on what we want to learn

The incorporation of causal inference in mediation analysis has led to theoretical and methodological advancements-effect definitions with causal interpretation, clarification of assumptions required for effect identification, and an expanding array of options for effect estimation. However, the literature on these results is fast-growing and complex, which may be confusing to researchers unfamiliar with causal inference or unfamiliar with mediation. The goal of this article is to help ease the understanding and adoption of causal mediation analysis. It starts by highlighting a key difference between the causal inference and traditional approaches to mediation analysis and making a case for the need for explicit causal thinking and the causal inference approach in mediation analysis. It then explains in as-plain-as-possible language existing effect types, paying special attention to motivating these effects with different types of research questions, and using concrete examples for illustration. This presentation differentiates 2 perspectives (or purposes of analysis): the explanatory perspective (aiming to explain the total effect) and the interventional perspective (asking questions about hypothetical interventions on the exposure and mediator, or hypothetically modified exposures). For the latter perspective, the article proposes tapping into a general class of interventional effects that contains as special cases most of the usual effect types-interventional direct and indirect effects, controlled direct effects and also a generalized interventional direct effect type, as well as the total effect and overall effect. This general class allows flexible effect definitions which better match many research questions than the standard interventional direct and indirect effects. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Assessing the Impact of COVID-19 on the Clinical Trial Objective and Analysis of Oncology Clinical Trials-Application of the Estimand Framework

Abstract-Coronavirus disease 2019 (COVID-19) outbreak has rapidly evolved into a global pandemic. The impact of COVID-19 on patient journeys in oncology represents a new risk to interpretation of trial results and its broad applicability for future clinical practice. We identify key intercurrent events (ICEs) that may occur due to COVID-19 in oncology clinical trials with a focus on time-to-event endpoints and discuss considerations pertaining to the other estimand attributes introduced in the ICH E9 addendum. We propose strategies to handle COVID-19 related ICEs, depending on their relationship with malignancy and treatment and the interpretability of data after them. We argue that the clinical trial objective from a world without COVID-19 pandemic remains valid. The estimand framework provides a common language to discuss the impact of COVID-19 in a structured and transparent manner. This demonstrates that the applicability of the framework may even go beyond what it was initially intended for.

The evaluation of the Woman's Condom marketing approach: What value did peer-led interpersonal communication add to the promotion of a new female condom in urban Lusaka?

During a mass media campaign accompanying the launch of the Maximum Diva Woman's Condom (WC) in Lusaka, Zambia, a cluster-randomized evaluation was implemented to measure the added impact of a peer-led interpersonal communication (IPC) intervention on the awareness and uptake of the new female condom (FC). The WC and mass media campaign were introduced simultaneously in 40 urban wards in April 2016; half of the wards were randomly assigned to the treatment (IPC intervention) with cross-sectional surveys conducted before (n = 2,364) and one year after (n = 2,430) the start of the intervention. A pre-specified intention-to-treat (ITT) analysis measured the impact of randomization to IPC at the community level. In adjusted ITT models, there were no statistically significant differences between intervention and control groups. Due to significant implementation challenges, we also conducted exploratory secondary analyses to estimate effects among those who attended an IPC event (n = 66) using instrumental variable and inverse probability weighting analyses. In addition to increases in FC identification (IPC attendees had higher reported use of any condom, improved perceptions of FC's, and were more likely to have discussed contraceptive use with their partner as compared to non-attendees). The introduction of a new FC product combined with an IPC intervention significantly increased general knowledge and awareness in the community as compared to media alone, but did not lead to detectable community level impacts on other primary outcomes of interest. Observational evidence from our study suggests that IPC attendance is associated with increased use and negotiation. Future studies should explore the intensity and duration of IPC programming necessary to achieve detectable community level impacts on behavior. Trial Registration: AEARCTR-0000899.

Implementing integrated services in routine behavioral health care: primary outcomes from a cluster randomized controlled trial

BACKGROUND

An estimated 8.2 million adults in the United States live with co-occurring mental health and substance use disorders. Although the benefits of integrated treatment services for persons with co-occurring disorders has been well-established, gaps in access to integrated care persist. Implementation research can address this gap. We evaluated if the Network for the Improvement of Addiction Treatment (NIATx) implementation strategy was effective in increasing integrated services capacity among organizations treating persons with co-occurring disorders.

METHODS

This study employed a cluster randomized waitlist control group design. Forty-nine addiction treatment organizations from the State of Washington were randomized into one of two study arms: (1) NIATx strategy (active implementation strategy), or (2) waitlist (control). The primary outcome was a standardized organizational measure of integrated service capability: the Dual Diagnosis in Addiction Treatment (DDCAT) Index. Intent-to-treat analyses and per-protocol analyses were conducted to address the following questions: (1) Is NIATx effective in increasing integrated service capacity? and (2) Are there differences in organizations that actually use NIATx per-protocol versus those that do not?

RESULTS

From baseline to one-year post active implementation, both the NIATx strategy and waitlist arms demonstrated improvements over time in DDCAT Index total and DDCAT dimension scores. In intent-to-treat analyses, a moderate but statistically significant difference in improvement between study arms was seen only in the Program Milieu dimension (p = 0.020, Cohen's d = 0.54). In per-protocol analyses, moderate-to-large effects in Program Milieu (p = 0.002, Cohen's d = 0.91) and Continuity of Care (p = 0.026, Cohen's d = 0.63) dimensions, and in total DDCAT Index (p = 0.046, Cohen's d = 0.51) were found.

CONCLUSIONS

Overall, organizations in both study arms improved DDCAT Index scores over time. Organizations in the NIATx strategy arm with full adherence to the NIATx protocol had significantly greater improvements in the primary outcome measure of integrated service capacity for persons with co-occurring disorders.

TRAIL REGISTRATION

ClinicalTrials.gov, NCT03007940 . Retrospectively registered January 2017.

Analyzing patient-reported outcome data when completion differs between arms in open-label trials: an application of principal stratification

PURPOSE

Cancer trials are often open-label and include patient-reported outcomes (PROs). Previous work has demonstrated that patients may complete PRO assessments less frequently in the control arm compared with the experimental arm in open-label trials. Such differential completion may affect PRO results. This paper sought to explore principal stratification methodology to address potential bias caused by the posttreatment intermediate variable of questionnaire completion.

METHODS

We evaluated six randomized trials (five open-label and one double-blind) of anticancer therapies with varying levels of PRO completion submitted to the Food and Drug Administration (FDA). We applied complete case analysis (CCA), multiple imputation (MI), and principal stratification to evaluate PRO results for quality of life (QOL) and the domains of physical, role, and emotional function (PF, RF, and EF). Assignment to potential principal strata was by the expectation maximization algorithm using patient baseline characteristics.

RESULTS

Completion rates in the experimental arm ranged from 66% to 94% and 51% to 95% in the control arm. Four trials had negligible completion differences between arms (1%-2%), and two had large differences favoring the experimental arm (15%-17%). For trials with negligible completion differences, principal stratification results were similar to CCA and MI results for all domains. Notable differences in point estimates may be observed in trials with large differences in completion rates. However, in the examined trials, the confidence intervals for the principal stratification estimates overlapped with the ones obtained using CCA.

CONCLUSIONS

The principal stratification estimand may be a useful additional analysis, especially if PRO completion differs between arms.

Sensitivity analyses for average treatment effects when outcome is censored by death in instrumental variable models

Two problems that arise in making causal inferences for nonmortality outcomes such as bronchopulmonary dysplasia (BPD) are unmeasured confounding and censoring by death, ie, the outcome is observed only when subjects survive. In randomized experiments with noncompliance and no censoring by death, instrumental variable (IV) methods can be used to control for the unmeasured confounding. But, when there is censoring by death, the average causal treatment effect cannot be identified under usual assumptions but can be studied for a specific subpopulation by using sensitivity analysis with additional assumptions. However, evaluating the local average treatment effect (LATE) in observational studies with censoring by death problems while controlling for unmeasured confounding is not well studied. We develop a novel sensitivity analysis method based on IV models for studying the LATE. Specifically, we present the identification results under an additional assumption and propose a three-step procedure for the LATE estimation. Also, we propose an improved two-step procedure by simultaneously estimating the instrument propensity score (ie, the probability of instrument given covariates) and the parameters induced by the assumption. We show with simulation studies that the two-step procedure can be more robust and efficient than the three-step procedure. Finally, we apply our sensitivity analysis methods to a study on the effect of delivery at high-level neonatal intensive care units on the risk of BPD.

A Complier Average Causal Effect Analysis of the Stimulant Reduction Intervention using Dosed Exercise Study

Objective

Exercise is a promising treatment for substance use disorders, yet an intention-to-treat analysis of a large, multi-site study found no reduction in stimulant use for exercise versus health education. Exercise adherence was sub-optimal; therefore, secondary post-hoc complier average causal effects (CACE) analysis was conducted to determine the potential effectiveness of adequately dosed exercise.

Method

The STimulant use Reduction Intervention using Dosed Exercise study was a randomized controlled trial comparing a 12 kcal/kg/week (KKW) exercise dose versus a health education control conducted at nine residential substance use treatment settings across the U.S. that are affiliated with the National Drug Abuse Treatment Clinical Trials Network. Participants were sedentary but medically approved for exercise, used stimulants within 30 days prior to study entry, and received a DSM-IV stimulant abuse or dependence diagnosis within the past year. A CACE analysis adjusted to include only participants with a minimum threshold of adherence (at least 8.3 KKW) and using a negative-binomial hurdle model focused on 218 participants who were 36.2% female, mean age 39.4 years (SD = 11.1), and averaged 13.0 (SD = 9.2) stimulant use days in the 30 days before residential treatment. The outcome was days of stimulant use as assessed by the self-reported TimeLine Follow Back and urine drug screen results.

Results

The CACE-adjusted analysis found a significantly lower probability of relapse to stimulant use in the exercise group versus the health education group (41.0% vs. 55.7%, p < .01) and significantly lower days of stimulant use among those who relapsed (5.0 days vs. 9.9 days, p < .01).

Conclusions

The CACE adjustment revealed significant, positive effects for exercise. Further research is warranted to develop strategies for exercise adherence that can ensure achievement of an exercise dose sufficient to produce a significant treatment effect.

Reporting non-adherence in cluster randomised trials: A systematic review

BACKGROUND

Treatment non-adherence in randomised trials refers to situations where some participants do not receive their allocated treatment as intended. For cluster randomised trials, where the unit of randomisation is a group of participants, non-adherence may occur at the cluster or individual level. When non-adherence occurs, randomisation no longer guarantees that the relationship between treatment receipt and outcome is unconfounded, and the power to detect the treatment effects in intention-to-treat analysis may be reduced. Thus, recording adherence and estimating the causal treatment effect adequately are of interest for clinical trials.

OBJECTIVES

To assess the extent of reporting of non-adherence issues in published cluster trials and to establish which methods are currently being used for addressing non-adherence, if any, and whether clustering is accounted for in these.

METHODS

We systematically reviewed 132 cluster trials published in English in 2011 previously identified through a search in PubMed.

RESULTS

One-hundred and twenty three cluster trials were included in this systematic review. Non-adherence was reported in 56 cluster trials. Among these, 19 reported a treatment efficacy estimate: per protocol in 15 and as treated in 4. No study discussed the assumptions made by these methods, their plausibility or the sensitivity of the results to deviations from these assumptions.

LIMITATIONS

The year of publication of the cluster trials included in this review (2011) could be considered a limitation of this study; however, no new guidelines regarding the reporting and the handling of non-adherence for cluster trials have been published since. In addition, a single reviewer undertook the data extraction. To mitigate this, a second reviewer conducted a validation of the extraction process on 15 randomly selected reports. Agreement was satisfactory (93%).

CONCLUSION

Despite the recommendations of the Consolidated Standards of Reporting Trials statement extension to cluster randomised trials, treatment adherence is under-reported. Among the trials providing adherence information, there was substantial variation in how adherence was defined, handled and reported. Researchers should discuss the assumptions required for the results to be interpreted causally and whether these are scientifically plausible in their studies. Sensitivity analyses to study the robustness of the results to departures from these assumptions should be performed.

Analysis of randomised trials with long-term follow-up

Randomised trials with long-term follow-up can provide estimates of the long-term effects of health interventions. However, analysis of long-term outcomes in randomised trials may be complicated by problems with the administration of treatment such as non-adherence, treatment switching and co-intervention, and problems obtaining outcome measurements arising from loss to follow-up and death of participants. Methods for dealing with these issues that involve conditioning on post-randomisation variables are unsatisfactory because they may involve the comparison of non-exchangeable groups and generate estimates that do not have a valid causal interpretation. We describe approaches to analysis that potentially provide estimates of causal effects when such issues arise. Brief descriptions are provided of the use of instrumental variable and propensity score methods in trials with imperfect adherence, marginal structural models and g-estimation in trials with treatment switching, mixed longitudinal models and multiple imputation in trials with loss to follow-up, and a sensitivity analysis that can be used when trial follow-up is truncated by death or other events. Clinical trialists might consider these methods both at the design and analysis stages of randomised trials with long-term follow-up.

Latent Class Survival Models Linked by Principal Stratification to Investigate Heterogenous Survival Subgroups Among Individuals With Early-Stage Kidney Cancer

Rates of kidney cancer have been increasing, with small incidental tumors experiencing the fastest growth rates. Much of the increase could be due to increased use of CT scans, MRIs, and ultrasounds for unrelated conditions. Many tumors might never have been detected or become symptomatic in the past. This suggests that many patients might benefit from less aggressive therapy, such as active surveillance by which tumors are surgically removed only if they become sufficiently large. However, it has been difficult for clinicians to identify subgroups of patients for whom treatment might be especially beneficial or harmful. In this work, we use a principal stratification framework to estimate the proportion and characteristics of individuals who have large or small hazard rates of death in two treatment arms. This allows us to assess who might be helped or harmed by aggressive treatment. We also use Weibull mixture models. This work differs from much previous work in that the survival classes upon which principal stratification is based are latent variables. That is, survival class is not an observed variable. We apply this work using Surveillance Epidemiology and End Results-Medicare claims data. Clinicians can use our methods for investigating treatments with heterogeneous effects.

Estimating the palliative effect of percutaneous endoscopic gastrostomy in an observational registry using principal stratification and generalized propensity scores

Clinical disease registries offer a rich collection of valuable patient information but also pose challenges that require special care and attention in statistical analyses. The goal of this paper is to propose a statistical framework that allows for estimating the effect of surgical insertion of a percutaneous endogastrostomy (PEG) tube for patients living with amyotrophic lateral sclerosis (ALS) using data from a clinical registry. Although all ALS patients are informed about PEG, only some patients agree to the procedure which, leads to the potential for selection bias. Assessing the effect of PEG is further complicated by the aggressively fatal disease, such that time to death competes directly with both the opportunity to receive PEG and clinical outcome measurements. Our proposed methodology handles the "censoring by death" phenomenon through principal stratification and selection bias for PEG treatment through generalized propensity scores. We develop a fully Bayesian modeling approach to estimate the survivor average causal effect (SACE) of PEG on BMI, a surrogate outcome measure of nutrition and quality of life. The use of propensity score methods within the principal stratification framework demonstrates a significant and positive effect of PEG treatment, particularly when time of treatment is included in the treatment definition.

A randomised controlled trial of the effectiveness of a program for early detection and treatment of depression in primary care

OBJECTIVE

There is considerable uncertainty about whether depression screening programs in primary care may improve outcomes and what specific features of such programs may contribute to success. We tested the effectiveness of a program involving substantial commitment from local mental health services.

METHODS

Prospective, randomised, patient- and evaluator-masked, parallel-group, controlled study. Participants were recruited in several urban primary care practices where they completed the PC-SAD screener and WHOQOL-Bref. Those who screened positive and did not report suicidal ideation (N=115) were randomised to an intervention group (communication of the result and offer of psychiatric evaluation and treatment free of charge; N=56) or a control group (no feedback on test result for 3 months; N=59). After 3 months, 100 patients agreed to a follow-up telephone interview including the administration of the PC-SAD5 and WHOQOL-Bref.

RESULTS

Depression severity and quality of life improved significantly in both groups. Intent-to-treat analysis showed no effect of the intervention. As only 37% of patients randomised to the intervention group actually contacted the study outpatient clinic, we performed a per-protocol analysis to determine whether the intervention, if delivered as planned, had been effective. This analysis revealed a significant positive effect of the intervention on severity of depressive symptoms, and on response and remission rate. Complier average causal effect analysis yielded similar results.

CONCLUSION

Due to the relatively small sample size, our findings should be regarded as preliminary and have limited generalizability. They suggest that there are considerable barriers on the part of many patients to the implementation of depression screening programs in primary care. While such programs can be effective, they should be designed based on the understanding of patients' perspectives.

Estimating efficacy in a randomized trial with product nonadherence: application of multiple methods to a trial of preexposure prophylaxis for HIV prevention

Antiretroviral preexposure prophylaxis (PrEP) for persons at high risk of human immunodeficiency virus infection is a promising new prevention strategy. Six randomized trials of oral PrEP were recently conducted and demonstrated efficacy estimates ranging from 75% to no effect, with nonadherence likely resulting in attenuated estimates of the protective effect of PrEP. In 1 of these trials, the Partners PrEP Study (Kenya and Uganda, 2008-2011), participants (4,747 serodiscordant heterosexual couples) were randomized to receipt of tenofovir (TDF), coformulated TDF/emtricitabine (FTC), or placebo. Intention-to-treat analyses found efficacy estimates of 67% for TDF and 75% for TDF/FTC. We applied multiple methods to data from that trial to estimate the efficacy of PrEP with high adherence, including principal stratification and inverse-probability-of-censoring (IPC) weights. Results were further from the null when correcting for nonadherence: 1) among the strata with an estimated 100% probability of high adherence (TDF hazard ratio (HR) = 0.19, 95% confidence interval (CI): 0.07, 0.56; TDF/FTC HR = 0.12, 95% CI: 0.03, 0.52); 2) with IPC weights used to approximate a continuously adherent population (TDF HR = 0.18, 95% CI: 0.06, 0.53; TDF/FTC HR = 0.15, 95% CI: 0.04, 0.52); and 3) in per-protocol analysis (TDF HR = 0.18, 95% CI: 0.06, 0.53; TDF/FTC HR = 0.16, 95% CI: 0.05, 0.53). Our results suggest that the efficacy of PrEP with high adherence is over 80%.

Maximizing Effectiveness Trials in PTSD and SUD Through Secondary Analysis: Benefits and Limitations Using the National Institute on Drug Abuse Clinical Trials Network "Women and Trauma" Study as a Case Example

Recent federal legislation and a renewed focus on integrative care models underscore the need for economical, effective, and science-based behavioral health care treatment. As such, maximizing the impact and reach of treatment research is of great concern. Behavioral health issues, including the frequent co-occurrence of substance use disorders (SUD) and posttraumatic stress disorder (PTSD), are often complex, with a myriad of factors contributing to the success of interventions. Although treatment guides for comorbid SUD/PTSD exist, most patients continue to suffer symptoms following the prescribed treatment course. Further, the study of efficacious treatments has been hampered by methodological challenges (e.g., overreliance on "superiority" designs (i.e., designs structured to test whether or not one treatment statistically surpasses another in terms of effect sizes) and short term interventions). Secondary analyses of randomized controlled clinical trials offer potential benefits to enhance understanding of findings and increase the personalization of treatment. This paper offers a description of the limits of randomized controlled trials as related to SUD/PTSD populations, highlights the benefits and potential pitfalls of secondary analytic techniques, and uses a case example of one of the largest effectiveness trials of behavioral treatment for co-occurring SUD/PTSD conducted within the National Drug Abuse Treatment Clinical Trials Network (NIDA CTN) and producing 19 publications. The paper concludes with implications of this secondary analytic approach to improve addiction researchers' ability to identify best practices for community-based treatment of these disorders. Innovative methods are needed to maximize the benefits of clinical studies and better support SUD/PTSD treatment options for both specialty and non-specialty healthcare settings. Moving forward, planning for and description of secondary analyses in randomized trials should be given equal consideration and care to the primary outcome analysis.