SMART designs in cancer research: Past, present, and future

Datopotamab-deruxtecan plus durvalumab in early-stage breast cancer: the sequential multiple assignment randomized I-SPY2.2 phase 2 trial

Sequential adaptive trial designs can help accomplish the goals of personalized medicine, optimizing outcomes and avoiding unnecessary toxicity. Here we describe the results of incorporating a promising antibody-drug conjugate, datopotamab-deruxtecan (Dato-DXd) in combination with programmed cell death-ligand 1 inhibitor, durvalumab, as the first sequence of therapy in the I-SPY2.2 phase 2 neoadjuvant sequential multiple assignment randomization trial for high-risk stage 2/3 breast cancer. The trial includes three blocks of treatment, with initial randomization to different experimental agent(s) (block A), followed by a taxane-based regimen tailored to tumor subtype (block B), followed by doxorubicin-cyclophosphamide (block C). Subtype-specific algorithms based on magnetic resonance imaging volume change and core biopsy guide treatment redirection after each block, including the option of early surgical resection in patients predicted to have a high likelihood of pathologic complete response, which is the primary endpoint assessed when resection occurs. There are two primary efficacy analyses: after block A and across all blocks for six prespecified HER2-negative subtypes (defined by hormone receptor status and/or response-predictive subtypes). In total, 106 patients were treated with Dato-DXd/durvalumab in block A. In the immune-positive subtype, Dato-DXd/durvalumab exceeded the prespecified threshold for success (graduated) after block A; and across all blocks, pathologic complete response rates were equivalent to the rate expected for the standard of care (79%), but 54% achieved that result after Dato-DXd/durvalumab alone (block A) and 92% without doxorubicin-cyclophosphamide (after blocks A + B). The treatment strategy across all blocks graduated in the hormone-negative/immune-negative subtype. No new toxicities were observed. Stomatitis was the most common side effect in block A. No patients receiving block A treatment alone had adrenal insufficiency. Dato-DXd/durvalumab is a promising therapy combination that can eliminate standard chemotherapy in many patients, particularly the immune-positive subtype.ClinicalTrials.gov registration: NCT01042379 .

Causal inference for oncology: past developments and current challenges

In this paper, we review some important early developments on causal inference in medical statistics and epidemiology that were inspired by questions in oncology. We examine two classical examples from the literature and point to a current area of ongoing methodological development, namely the estimation of optimal adaptive treatment strategies. While causal approaches to analysis have become more routine in oncology research, many exciting challenges and open problems remain, particularly in the context of censored outcomes.

Bayesian sample size calculations for comparing two strategies in SMART studies

In the management of most chronic conditions characterized by the lack of universally effective treatments, adaptive treatment strategies (ATSs) have grown in popularity as they offer a more individualized approach. As a result, sequential multiple assignment randomized trials (SMARTs) have gained attention as the most suitable clinical trial design to formalize the study of these strategies. While the number of SMARTs has increased in recent years, sample size and design considerations have generally been carried out in frequentist settings. However, standard frequentist formulae require assumptions on interim response rates and variance components. Misspecifying these can lead to incorrect sample size calculations and correspondingly inadequate levels of power. The Bayesian framework offers a straightforward path to alleviate some of these concerns. In this paper, we provide calculations in a Bayesian setting to allow more realistic and robust estimates that account for uncertainty in inputs through the 'two priors' approach. Additionally, compared to the standard frequentist formulae, this methodology allows us to rely on fewer assumptions, integrate pre-trial knowledge, and switch the focus from the standardized effect size to the MDD. The proposed methodology is evaluated in a thorough simulation study and is implemented to estimate the sample size for a full-scale SMART of an internet-based adaptive stress management intervention on cardiovascular disease patients using data from its pilot study conducted in two Canadian provinces.

Multiple Pharmacotherapy Adaptations for Smoking Cessation Based on Treatment Response in Black Adults Who Smoke: A Randomized Clinical Trial

Importance

Adapting to different smoking cessation medications when an individual has not stopped smoking has shown promise, but efficacy has not been tested in racial and ethnic minority individuals who smoke and tend to have less success in quitting and bear a disproportionate share of tobacco-related morbidity and mortality.

Objective

To evaluate efficacy of multiple smoking cessation pharmacotherapy adaptations based on treatment response in Black adults who smoke daily.

Design, Setting, and Participants

This randomized clinical trial of adapted therapy (ADT) or enhanced usual care (UC) included non-Hispanic Black adults who smoke and was conducted from May 2019 to January 2022 at a federally qualified health center in Kansas City, Missouri. Data analysis took place from March 2022 to January 2023.

Interventions

Both groups received 18 weeks of pharmacotherapy with long-term follow-up through week 26. The ADT group consisted of 196 individuals who received a nicotine patch (NP) and up to 2 pharmacotherapy adaptations, with a first switch to varenicline at week 2 and, if needed, a second switch to bupropion plus NP (bupropion + NP) based on carbon monoxide (CO)-verified smoking status (CO ≥6 ppm) at week 6. The UC group consisted of 196 individuals who received NP throughout the duration of treatment.

Main Outcomes and Measures

Anabasine-verified and anatabine-verified point-prevalence abstinence at week 12 (primary end point) and weeks 18 and 26 (secondary end points). The χ2 test was used to compare verified abstinence at week 12 (primary end point) and weeks 18 and 26 (secondary end points) between ADT and UC. A post hoc sensitivity analysis of smoking abstinence at week 12 was performed with multiple imputation using a monotone logistic regression with treatment and gender as covariates to impute the missing data.

Results

Among 392 participants who were enrolled (mean [SD] age, 53 [11.6] years; 224 [57%] female; 186 [47%] ≤ 100% federal poverty level; mean [SD] 13 [12.4] cigarettes per day), 324 (83%) completed the trial. Overall, 196 individuals were randomized to each study group. Using intent-to-treat and imputing missing data as participants who smoke, verified 7-day abstinence was not significantly different by treatment group at 12 weeks (ADT: 34 of 196 [17.4%]; UC: 23 of 196 [11.7%]; odds ratio [OR], 1.58; 95% CI, 0.89-2.80; P = .12), 18 weeks (ADT: 32 of 196 [16.3%]; UC: 31 of 196 [15.8%]; OR, 1.04; 95% CI, 0.61-1.78; P = .89), and 26 weeks (ADT: 24 of 196 [12.2%]; UC: 26 of 196 [13.3%]; OR, 0.91; 95% CI, 0.50-1.65; P = .76). Of the ADT participants who received pharmacotherapy adaptations (135/188 [71.8%]), 11 of 135 (8.1%) were abstinent at week 12. Controlling for treatment, individuals who responded to treatment and had CO-verified abstinence at week 2 had 4.6 times greater odds of being abstinent at week 12 (37 of 129 [28.7%] abstinence) than those who did not respond to treatment (19 of 245 [7.8%] abstinence; OR; 4.6; 95% CI, 2.5-8.6; P < .001).

Conclusions and Relevance

In this randomized clinical trial of adapted vs standard of care pharmacotherapy, adaptation to varenicline and/or bupropion + NP after failure of NP monotherapy did not significantly improve abstinence rates for Black adults who smoke relative to those who continued treatment with NP. Those who achieved abstinence in the first 2 weeks of the study were significantly more likely to achieve later abstinence, highlighting early treatment response as an important area for preemptive intervention.

Trial Registration

ClinicalTrials.gov Identifier: NCT03897439.

Interim monitoring in sequential multiple assignment randomized trials

A sequential multiple assignment randomized trial (SMART) facilitates the comparison of multiple adaptive treatment strategies (ATSs) simultaneously. Previous studies have established a framework to test the homogeneity of multiple ATSs by a global Wald test through inverse probability weighting. SMARTs are generally lengthier than classical clinical trials due to the sequential nature of treatment randomization in multiple stages. Thus, it would be beneficial to add interim analyses allowing for an early stop if overwhelming efficacy is observed. We introduce group sequential methods to SMARTs to facilitate interim monitoring based on the multivariate chi-square distribution. Simulation studies demonstrate that the proposed interim monitoring in SMART (IM-SMART) maintains the desired type I error and power with reduced expected sample size compared to the classical SMART. Finally, we illustrate our method by reanalyzing a SMART assessing the effects of cognitive behavioral and physical therapies in patients with knee osteoarthritis and comorbid subsyndromal depressive symptoms.

Associations of disordered eating with the intestinal microbiota and short-chain fatty acids among young adults with type 1 diabetes

BACKGROUND AND AIMS

Disordered eating (DE) in type 1 diabetes (T1D) includes insulin restriction for weight loss with serious complications. Gut microbiota-derived short chain fatty acids (SCFA) may benefit host metabolism but are reduced in T1D. We evaluated the hypothesis that DE and insulin restriction were associated with reduced SCFA-producing gut microbes, SCFA, and intestinal microbial diversity in adults with T1D.

METHODS AND RESULTS

We collected stool samples at four timepoints in a hypothesis-generating gut microbiome pilot study ancillary to a weight management pilot in young adults with T1D. 16S ribosomal RNA gene sequencing measured the normalized abundance of SCFA-producing intestinal microbes. Gas-chromatography mass-spectrometry measured SCFA (total, acetate, butyrate, and propionate). The Diabetes Eating Problem Survey-Revised (DEPS-R) assessed DE and insulin restriction. Covariate-adjusted and Bonferroni-corrected generalized estimating equations modeled the associations. COVID-19 interrupted data collection, so models were repeated restricted to pre-COVID-19 data. Data were available for 45 participants at 109 visits, which included 42 participants at 65 visits pre-COVID-19. Participants reported restricting insulin "At least sometimes" at 53.3% of visits. Pre-COVID-19, each 5-point DEPS-R increase was associated with a -0.34 (95% CI -0.56, -0.13, p = 0.07) lower normalized abundance of genus Anaerostipes; and the normalized abundance of Lachnospira genus was -0.94 (95% CI -1.5, -0.42), p = 0.02 lower when insulin restriction was reported "At least sometimes" compared to "Rarely or Never".

CONCLUSION

DE and insulin restriction were associated with a reduced abundance of SCFA-producing gut microbes pre-COVID-19. Additional studies are needed to confirm these associations to inform microbiota-based therapies in T1D.

Use of Sequential Multiple Assignment Randomized Trials (SMARTs) in oncology: systematic review of published studies

Sequential multiple assignments randomized trials (SMARTs) are a type of experimental design where patients may be randomised multiple times according to pre-specified decision rules. The present work investigates the state-of-the-art of SMART designs in oncology, focusing on the discrepancy between the available methodological approaches in the statistical literature and the procedures applied within cancer clinical trials. A systematic review was conducted, searching PubMed, Embase and CENTRAL for protocols or reports of results of SMART designs and registrations of SMART designs in clinical trial registries applied to solid tumour research. After title/abstract and full-text screening, 33 records were included. Fifteen were reports of trials' results, four were trials' protocols and fourteen were trials' registrations. The study design was defined as SMART by only one out of fifteen trial reports. Conversely, 13 of 18 study protocols and trial registrations defined the study design SMART. Furthermore, most of the records considered each stage separately in the analysis, without considering treatment regimens embedded in the trial. SMART designs in oncology are still limited. Study powering and analysis is mainly based on statistical approaches traditionally used in single-stage parallel trial designs. Formal reporting guidelines for SMART designs are needed.

The Intestinal Microbiota and Short-Chain Fatty Acids in Association with Advanced Metrics of Glycemia and Adiposity Among Young Adults with Type 1 Diabetes and Overweight or Obesity

Background

Comanagement of glycemia and adiposity is the cornerstone of cardiometabolic risk reduction in type 1 diabetes (T1D), but targets are often not met. The intestinal microbiota and microbiota-derived short-chain fatty acids (SCFAs) influence glycemia and adiposity but have not been sufficiently investigated in longstanding T1D.

Objectives

We evaluated the hypothesis that an increased abundance of SCFA-producing gut microbes, fecal SCFAs, and intestinal microbial diversity were associated with improved glycemia but increased adiposity in young adults with longstanding T1D.

Methods

Participants provided stool samples at ≤4 time points (NCT03651622: https://clinicaltrials.gov/ct2/show/NCT03651622). Sequencing of the 16S ribosomal RNA gene measured abundances of SCFA-producing intestinal microbes. GC-MS measured total and specific SCFAs (acetate, butyrate, propionate). DXA (body fat percentage and percentage lean mass) and anthropometrics (BMI) measured adiposity. Continuous glucose monitoring [percentage of time in range (70-180 mg/dL), above range (>180 mg/dL), and below range (54-69 mg/dL)] and glycated hemoglobin (i.e., HbA1c) assessed glycemia. Adjusted and Bonferroni-corrected generalized estimating equations modeled the associations of SCFA-producing gut microbes, fecal SCFAs, and intestinal microbial diversity with glycemia and adiposity. COVID-19 interrupted data collection, so models were repeated restricted to pre-COVID-19 visits.

Results

Data were available for ≤45 participants at 101 visits (including 40 participants at 54 visits pre-COVID-19). Abundance of Eubacterium hallii was associated inversely with BMI (all data). Pre-COVID-19, increased fecal propionate was associated with increased percentage of time above range and reduced percentage of time in target and below range; and abundances of 3 SCFA-producing taxa (Ruminococcus gnavus, Eubacterium ventriosum, and Lachnospira) were associated inversely with body fat percentage, of which two microbes were positively associated with percentage lean mass. Abundance of Anaerostipes was associated with reduced percentage of time in range (all data) and with increased body fat percentage and reduced percentage lean mass (pre-COVID-19).

Conclusions

Unexpectedly, fecal propionate was associated with detriment to glycemia, whereas most SCFA-producing intestinal microbes were associated with benefit to adiposity. Future studies should confirm these associations and determine their potential causal linkages in T1D.This study is registered at clinical.trials.gov (NCT03651622; https://clinicaltrials.gov/ct2/show/NCT03651622).

Understanding heterogeneity of responses to, and optimizing clinical efficacy of, exercise training in older adults: NIH NIA Workshop summary

Exercise is a cornerstone of preventive medicine and a promising strategy to intervene on the biology of aging. Variation in the response to exercise is a widely accepted concept that dates back to the 1980s with classic genetic studies identifying sequence variations as modifiers of the VO₂max response to training. Since that time, the literature of exercise response variance has been populated with retrospective analyses of existing datasets that are limited by a lack of statistical power from technical error of the measurements and small sample sizes, as well as diffuse outcomes, very few of which have included older adults. Prospective studies that are appropriately designed to interrogate exercise response variation in key outcomes identified a priori and inclusive of individuals over the age of 70 are long overdue. Understanding the underlying intrinsic (e.g., genetics and epigenetics) and extrinsic (e.g., medication use, diet, chronic disease) factors that determine robust versus poor responses to various exercise factors will be used to improve exercise prescription to target the pillars of aging and optimize the clinical efficacy of exercise training in older adults. This review summarizes the proceedings of the NIA-sponsored workshop entitled, "Understanding Heterogeneity of Responses to, and Optimizing Clinical Efficacy of, Exercise Training in Older Adults" and highlights the importance and current state of exercise response variation research, particularly in older adults, prevailing challenges, and future directions.

Morpholine substituted quinazoline derivatives as anticancer agents against MCF-7, A549 and SHSY-5Y cancer cell lines and mechanistic studies

A series of morpholine substituted quinazoline derivatives have been synthesized and evaluated for cytotoxic potential against A549, MCF-7 and SHSY-5Y cancer cell lines. These compounds were found to be non-toxic against HEK293 cells at 25 μM and hence display anticancer potential. In these series compounds, AK-3 and AK-10 displayed significant cytotoxic activity against all the three cell lines. AK-3 displayed IC50 values of 10.38 ± 0.27 μM, 6.44 ± 0.29 μM and 9.54 ± 0.15 μM against A549, MCF-7 and SHSY-5Y cancer cell lines. Similarly, AK-10 showed IC50 values of 8.55 ± 0.67 μM, 3.15 ± 0.23 μM and 3.36 ± 0.29 μM against A549, MCF-7 and SHSY-5Y, respectively. In the mechanistic studies, it was found that AK-3 and AK-10 inhibit the cell proliferation in the G1 phase of the cell cycle and the primary cause of death of the cells was found to be through apoptosis. Thus, morpholine based quinazoline derivatives have the potential to be developed as potent anticancer drug molecules.

Design of the advancing care for type 1 diabetes and obesity network energy metabolism and sequential multiple assignment randomized trial nutrition pilot studies: An integrated approach to develop weight management solutions for individuals with type 1 diabetes

Young adults with type 1 diabetes (T1D) often have difficulty co-managing weight and glycemia. The prevalence of overweight and obesity among individuals with T1D now parallels that of the general population and contributes to dyslipidemia, insulin resistance, and risk for cardiovascular disease. There is a compelling need to develop a program of research designed to optimize two key outcomes-weight management and glycemia-and to address the underlying metabolic processes and behavioral challenges unique to people with T1D. For an intervention addressing these dual outcomes to be effective, it must be appropriate to the unique metabolic phenotype of T1D, and to biological and behavioral responses to glycemia (including hypoglycemia) that relate to weight management. The intervention must also be safe, feasible, and accepted by young adults with T1D. In 2015, we established a consortium called ACT1ON: Advancing Care for Type 1 Diabetes and Obesity Network, a transdisciplinary team of scientists at multiple institutions. The ACT1ON consortium designed a multi-phase study which, in parallel, evaluated the mechanistic aspects of the unique metabolism and energy requirements of individuals with T1D, alongside a rigorous adaptive behavioral intervention to simultaneously facilitate weight management while optimizing glycemia. This manuscript describes the design of our integrative study-comprised of an inpatient mechanistic phase and an outpatient behavioral phase-to generate metabolic, behavioral, feasibility, and acceptability data to support a future, fully powered sequential, multiple assignment, randomized trial to evaluate the best approaches to prevent and treat obesity while co-managing glycemia in people with T1D. Clinicaltrials.gov identifiers: NCT03651622 and NCT03379792. The present study references can be found here: https://clinicaltrials.gov/ct2/show/NCT03651622 https://clinicaltrials.gov/ct2/show/NCT03379792?term=NCT03379792&draw=2&rank=1 Submission Category: "Study Design, Statistical Design, Study Protocols".

Randomized and non-randomized designs for causal inference with longitudinal data in rare disorders

In the United States, approximately 7000 rare diseases affect 30 million patients, and only 10% of these diseases have existing therapies. Sound study design and causal inference methods are essential to demonstrate the therapeutic efficacy, safety, and effectiveness of new therapies. In the rare diseases setting, several factors challenge the use of typical parallel control designs: the small patient population size, genotypic and phenotypic diversity, and the complexity and incomplete understanding of the disorder’s progression. Repeated measures, when spaced appropriately relative to disease progression and exploited in design and analysis, can increase study power and reduce variability in treatment effect estimation. This paper reviews these longitudinal designs and draws the parallel between some new and existing randomized studies in rare diseases and their less well-known controlled observational study designs. We show that self-controlled randomized crossover and N-of-1 designs have similar considerations as the observational case series and case-crossover designs. Also, randomized sequential designs have similar considerations to longitudinal cohort studies using sequential matching or weighting to control confounding. We discuss design and analysis considerations for valid causal inference and illustrate them with examples of analyses in multiple rare disorders, including urea cycle disorder and cystic fibrosis.

Sequential multiple assignment randomized trial studies should report all key components: a systematic review

OBJECTIVE

Sequential Multiple Assignment Randomised Trial (SMART) designs allow multiple randomisations of participants; this allows assessment of stage-specific questions (individual randomisations) and adaptive interventions (i.e. treatment strategies). We assessed the quality of reporting of the information required to design SMART studies.

STUDY DESIGN AND SETTING

We systematically searched four databases (PubMed, Ovid, Web of Science and Scopus) for all trial reports, protocols, reviews, and methodological papers which mentioned SMART designs up to June 15, 2020.

RESULTS

Of the 157 selected records, 12 (7.64%) were trial reports, 24 (15.29%) were study protocols, 91 (58%) were methodological papers, and 30 (19.1%) were review papers. All these trials were powered using stage-specific aims. Only four (33.33%) of these trials reported parameters required for sample size calculations. A small number of the trials (16.67 %) were interested in determining the best embedded adaptive interventions. Most of the trials did not report information about multiple testing adjustment. Furthermore, most of records reported designs that were mainly focused on stage-specific aims.

CONCLUSIONS

Some features of SMART designs are seldomly reported and/or used. Furthermore, studies using this design tend to not adequately report information about all the design parameters, limiting their transparency and interpretability.

Optimal allocation to treatments in a sequential multiple assignment randomized trial

One of the main questions in the design of a trial is how many subjects should be assigned to each treatment condition. Previous research has shown that equal randomization is not necessarily the best choice. We study the optimal allocation for a novel trial design, the sequential multiple assignment randomized trial, where subjects receive a sequence of treatments across various stages. A subject's randomization probabilities to treatments in the next stage depend on whether he or she responded to treatment in the current stage. We consider a prototypical sequential multiple assignment randomized trial design with two stages. Within such a design, many pairwise comparisons of treatment sequences can be made, and a multiple-objective optimal design strategy is proposed to consider all such comparisons simultaneously. The optimal design is sought under either a fixed total sample size or a fixed budget. A Shiny App is made available to find the optimal allocations and to evaluate the efficiency of competing designs. As the optimal design depends on the response rates to first-stage treatments, maximin optimal design methodology is used to find robust optimal designs. The proposed methodology is illustrated using a sequential multiple assignment randomized trial example on weight loss management.

Adaptive randomization in a two-stage sequential multiple assignment randomized trial

Sequential multiple assignment randomized trials (SMARTs) are systematic and efficient media for comparing dynamic treatment regimes (DTRs), where each patient is involved in multiple stages of treatment with the randomization at each stage depending on the patient's previous treatment history and interim outcomes. Generally, patients enrolled in SMARTs are randomized equally to ethically acceptable treatment options regardless of how effective those treatments were during the previous stages, which results in some undesirable consequences in practice, such as low recruitment, less retention, and lower treatment adherence. In this article, we propose a response-adaptive SMART (RA-SMART) design where the allocation probabilities are imbalanced in favor of more promising treatments based on the accumulated information on treatment efficacy from previous patients and stages. The operating characteristics of the RA-SMART design relative to SMART design, including the consistency and efficiency of estimated response rate under each DTR, the power of identifying the optimal DTR, and the number of patients treated with the optimal and the worst DTRs, are assessed through extensive simulation studies. Some practical suggestions are discussed in the conclusion.

Study design and primary outcome in randomized controlled trials in periodontology. A systematic review

AIM

The aim of this review is to assess study design and risk of bias related to primary outcome in recently published randomized controlled trials (RCTs) in periodontology.

METHOD

An electronic (Medline, EMBASE and Cochrane library) and a manual search were completed to detect RCTs in humans, with an outcome in the field of periodontology and published in English from January 2018 up to March 2020.

RESULTS

Data extraction of 318 publications meeting the inclusion criteria was performed by two reviewers. Most studies adopted a parallel-group superiority design in a university setting. Overall, 54% of papers reported the primary outcome and relative sample size calculation, while only 37% also included reproducibility estimates relative to the primary outcome. Papers published in journals with higher impact factors had better compliance with primary outcome reporting and lower overall risk of bias scores.

CONCLUSION

Improvements in the quality of RCTs in periodontology are still needed. The importance of defining a clinically relevant study primary outcome and building the study around it needs to be emphasized. Furthermore, RCTs in periodontology could consider, when appropriate, some of the study design options which facilitate application of the principles of personalized medicine.

A Quantitative Paradigm for Decision-Making in Precision Oncology

The complexity and variability of cancer progression necessitate a quantitative paradigm for therapeutic decision-making that is dynamic, personalized, and capable of identifying optimal treatment strategies for individual patients under substantial uncertainty. Here, we discuss the core components and challenges of such an approach and highlight the need for comprehensive longitudinal clinical and molecular data integration in its development. We describe the complementary and varied roles of mathematical modeling and machine learning in constructing dynamic optimal cancer treatment strategies and highlight the potential of reinforcement learning approaches in this endeavor.

Quantitative Evidence Synthesis Methods for the Assessment of the Effectiveness of Treatment Sequences for Clinical and Economic Decision Making: A Review and Taxonomy of Simplifying Assumptions

Sequential use of alternative treatments for chronic conditions represents a complex intervention pathway; previous treatment and patient characteristics affect both the choice and effectiveness of subsequent treatments. This paper critically explores the methods for quantitative evidence synthesis of the effectiveness of sequential treatment options within a health technology assessment (HTA) or similar process. It covers methods for developing summary estimates of clinical effectiveness or the clinical inputs for the cost-effectiveness assessment and can encompass any disease condition. A comprehensive review of current approaches is presented, which considers meta-analytic methods for assessing the clinical effectiveness of treatment sequences and decision-analytic modelling approaches used to evaluate the effectiveness of treatment sequences. Estimating the effectiveness of a sequence of treatments is not straightforward or trivial and is severely hampered by the limitations of the evidence base. Randomised controlled trials (RCTs) of sequences were often absent or very limited. In the absence of sufficient RCTs of whole sequences, there is no single best way to evaluate treatment sequences; however, some approaches could be re-used or adapted, sharing ideas across different disease conditions. Each has advantages and disadvantages, and is influenced by the evidence available, extent of treatment sequences (number of treatment lines or permutations), and complexity of the decision problem. Due to the scarcity of data, modelling studies applied simplifying assumptions to data on discrete treatments. A taxonomy for all possible assumptions was developed, providing a unique resource to aid the critique of existing decision-analytic models.

Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness

Dynamic treatment regimes operationalize precision medicine as a sequence of decision rules, one per stage of clinical intervention, that map up-to-date patient information to a recommended intervention. An optimal treatment regime maximizes the mean utility when applied to the population of interest. Methods for estimating an optimal treatment regime assume the data to be fully observed, which rarely occurs in practice. A common approach is to first use multiple imputation and then pool the estimators across imputed datasets. However, this approach requires estimating the joint distribution of patient trajectories, which can be high-dimensional, especially when there are multiple stages of intervention. We examine the application of inverse probability weighted estimating equations as an alternative to multiple imputation in the context of monotonic missingness. This approach applies to a broad class of estimators of an optimal treatment regime including both Q-learning and a generalization of outcome weighted learning. We establish consistency under mild regularity conditions and demonstrate its advantages in finite samples using a series of simulation experiments and an application to a schizophrenia study.

The use of PROMs and shared decision-making in medical encounters with patients: An opportunity to deliver value-based health care to patients

BACKGROUND

The recent emphasis on value-based health care (VBHC) is thought to provide new opportunities for shared decision-making (SDM) in the Netherlands, especially when using patient-reported outcome measures (PROMs) in routine medical encounters. It is still largely unclear about how PROMs could be linked to SDM and what we expect from clinicians in this respect.

AIM

To describe approaches and lessons learned in the fields of SDM and VBHC implementation that converge in using PROMs in medical encounters.

APPROACH

Based on input from three Dutch forerunner case examples and available evidence about SDM and VBHC, we describe barriers and facilitators regarding the use of PROMs and SDM in the medical encounter. Barriers and facilitators were structured according to a conversational model that included monitoring and managing, team talk, option talk, choice talk, and decision talk. Key lessons learned and recommendations were synthesized.

RESULTS

The use of individual, N = 1 PROMs scores in the medical encounter has been largely achieved in the forerunner projects. Conversation on monitoring and managing is relatively well implemented, and option talk to some extent, unlike team talk, and decision talk. Aggregated PROMs information describing outcomes of treatment options seemed to be scarcely used. Experienced barriers largely corresponded to what is known from the literature, eg, perceived lack of time and lack of tools summarizing the options. Some concerns were identified about increasing health care consumption as a result of using PROMs and SDM in the medical encounter.

CONCLUSION

Successful implementation of SDM within VBHC initiatives may not be self-evident, even though individual, N = 1 PROMs scores are being used in the medical encounter. Education and staff resources on meso and macro levels may facilitate the more time-consuming SDM aspects. It seems fruitful to especially target team talk and choice talk in redesigning clinical pathways.

Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome

Clinicians and researchers alike are increasingly interested in how best to personalize interventions. A dynamic treatment regimen is a sequence of prespecified decision rules which can be used to guide the delivery of a sequence of treatments or interventions that is tailored to the changing needs of the individual. The sequential multiple-assignment randomized trial is a research tool which allows for the construction of effective dynamic treatment regimens. We derive easy-to-use formulae for computing the total sample size for three common two-stage sequential multiple-assignment randomized trial designs in which the primary aim is to compare mean end-of-study outcomes for two embedded dynamic treatment regimens which recommend different first-stage treatments. The formulae are derived in the context of a regression model which leverages information from a longitudinal outcome collected over the entire study. We show that the sample size formula for a sequential multiple-assignment randomized trial can be written as the product of the sample size formula for a standard two-arm randomized trial, a deflation factor that accounts for the increased statistical efficiency resulting from a longitudinal analysis, and an inflation factor that accounts for the design of a sequential multiple-assignment randomized trial. The sequential multiple-assignment randomized trial design inflation factor is typically a function of the anticipated probability of response to first-stage treatment. We review modeling and estimation for dynamic treatment regimen effect analyses using a longitudinal outcome from a sequential multiple-assignment randomized trial, as well as the estimation of standard errors. We also present estimators for the covariance matrix for a variety of common working correlation structures. Methods are motivated using the ENGAGE study, a sequential multiple-assignment randomized trial aimed at developing a dynamic treatment regimen for increasing motivation to attend treatments among alcohol- and cocaine-dependent patients.

ipcwswitch: An R package for inverse probability of censoring weighting with an application to switches in clinical trials

In randomized clinical trials (RCT), the analysis is based on the intent-to-treat principle to avoid any selection bias in the constitution of groups. However, estimates of overall survival can be biased when significant crossover occurs because the separation of randomized groups is lost. To handle these switches, the inverse probability of censoring weighting (IPCW) method has been proposed; however, it is still poorly used in RCT, notably because of its complex implementation. In particular, for time-to-event outcomes, it can be difficult to format data, especially when time-dependent covariates have to be managed, with different measurement times between patients. This paper aims to present the R package ipcwswitch with some guidance for the analysis of the treatment effect on survival in a hypothetical setting where all patients would have continued to take the randomization treatment. After a brief recall of the key principles of the IPCW method, each step of the implementation is described using a toy example. The guidelines are illustrated in a case study that aimed at evaluating the benefit of therapy based on tumour molecular profiling for advanced cancers, SHIVA01.

Trial design and methodology for a non-restricted sequential multiple assignment randomized trial to evaluate combinations of perinatal interventions to optimize women's health

Pre-pregnancy overweight/obesity and excessive gestational weight gain (GWG) independently predict negative maternal and child health outcomes. To date, however, interventions that target GWG have not produced lasting improvements in maternal weight or health at 12-months postpartum. Given that interventions solely aimed at addressing GWG may not equip women with the skills needed for postpartum weight management, interventions that address health behaviors over the perinatal period might maximize maternal health in the first postpartum year. Thus, the current study leveraged a sequential multiple assignment randomized trial (SMART) design to evaluate sequences of prenatal (i.e., during pregnancy) and postpartum lifestyle interventions that optimize maternal weight, cardiometabolic health, and psychosocial outcomes at 12-months postpartum. Pregnant women (N = 300; ≤16 weeks pregnant) with overweight/obesity (BMI ≥ 25 kg/m2) are being recruited. Women are randomized to intervention or treatment as usual on two occasions: (1) early in pregnancy, and (2) prior to delivery, resulting in four intervention sequences. Intervention during pregnancy is designed to moderate GWG and introduce skills for management of weight as a chronic condition, while intervention in the postpartum period addresses weight loss. The primary outcome is weight at 12-months postpartum and secondary outcomes include variables of cardiometabolic health and psychosocial well-being. Analyses will evaluate the combination of prenatal and postpartum lifestyle interventions that optimizes maternal weight and secondary outcomes at 12-months postpartum. Optimizing the sequence of behavioral interventions to address specific needs during pregnancy and the first postpartum year can maximize intervention potency and mitigate longer-term cardiometabolic health risks for women.

Application of a sequential multiple assignment randomized trial (SMART) design in older patients with chronic lymphocytic leukemia

BACKGROUND

Ibrutinib therapy is safe and effective in patients with chronic lymphocytic leukemia (CLL). Currently, ibrutinib is administered continuously until disease progression. Combination regimens with ibrutinib are being developed to deepen response which could allow for ibrutinib maintenance (IM) discontinuation. Among untreated older patients with CLL, clinical investigators had the following questions: (i) does ibrutinib + venetoclax + obinutuzumab (IVO) with IM have superior progression-free survival (PFS) compared with ibrutinib + obinutuzumab (IO) with IM, and (ii) does the treatment strategy of IVO + IM for patients without minimal residual disease complete response (MRD- CR) or IVO + IM discontinuation for patients with MRD- CR have superior PFS compared with IO + IM.

DESIGN

Conventional designs randomize patients to IO with IM or IVO with IM to address the first objective, or randomize patients to each treatment strategy to address the second objective. A sequential multiple assignment randomized trial (SMART) design and analysis is proposed to address both objectives.

RESULTS

A SMART design strategy is appropriate when comparing adaptive interventions, which are defined by an individual's sequence of treatment decisions and guided by intermediate outcomes, such as response to therapy. A review of common applications of SMART design strategies is provided. Specific to the SMART design previously considered for Alliance study A041702, the general structure of the SMART is presented, an approach to sample size and power calculations when comparing adaptive interventions embedded in the SMART with a time-to-event end point is fully described, and analyses plans are outlined.

CONCLUSION

SMART design strategies can be used in cancer clinical trials with adaptive interventions to identify optimal treatment strategies. Further, standard software exists to provide sample size, power calculations, and data analysis for a SMART design.

The Challenges of Informed Consent in High-Stakes, Randomized Oncology Trials: A Systematic Review

Importance. Oncology trials often entail high-stakes interventions where potential for morbidity and fatal side effects, and for life-prolongation or cure, intensify bioethical issues surrounding informed consent. These challenges are compounded in multistage randomized trials, which are prevalent in oncology. Objective. We sought to elucidate the major barriers to informed consent in high-stakes oncology trials in general and the best consent practices for multistage randomized trials. Evidence Review. We queried PubMed for original studies published from January 1, 1990, to April 5, 2018, that focused on readability, quality, complexity or length of consent documents, motivation and sickness level of participants, or interventions and enhancements that influence informed consent for high-stakes oncologic interventions. Exclusion criteria included articles focused on populations outside industrialized countries, minors or other vulnerable populations, physician preferences, cancer screening and prevention, or recruitment strategies. Additional articles were identified through comprehensive bibliographic review. Findings. Twenty-seven articles were retained; 19 enrolled participants and 8 examined samples of consent documents. Methodologic quality was variable. This body of literature identified certain challenges that can be readily remedied. For example, the average length of the consent forms has increased 10-fold from 1987 to 2010, and patient understanding was shown to be inversely proportional to page count; shortening forms, or providing a concise summary as mandated by the revised Common Rule, might help. However, barriers to understanding that stem from deeply ingrained and flawed sociocultural perceptions of medical research seem more difficult to surmount. Although no studies specifically addressed problems posed by multiple sequential randomizations (such as change in risk-benefit ratio due to time-varying treatment responses or organ toxicities), the findings are likely applicable and especially relevant in that context. Concrete suggestions for improvement are proposed.

Moonshot Objectives: Catalyze New Scientific Breakthroughs-Proteogenomics

Breaking down the silos between disciplines to accelerate the pace of cancer research is a key paradigm for the Cancer Moonshot. Molecular analyses of cancer biology have tended to segregate between a focus on nucleic acids-DNA, RNA, and their modifications-and a focus on proteins and protein function. Proteogenomics represents a fusion of those two approaches, leveraging the strengths of each to provide a more integrated vision of the flow of information from DNA to RNA to protein and eventually function at the molecular level. Proteogenomic studies have been incorporated into multiple activities associated with the Cancer Moonshot, demonstrating substantial added value. Innovative study designs integrating genomic, transcriptomic, and proteomic data, particularly those using clinically relevant samples and involving clinical trials, are poised to provide new insights regarding cancer risk, progression, and response to therapy.

Precision Medicine

Precision medicine seeks to maximize the quality of healthcare by individualizing the healthcare process to the uniquely evolving health status of each patient. This endeavor spans a broad range of scientific areas including drug discovery, genetics/genomics, health communication, and causal inference all in support of evidence-based, i.e., data-driven, decision making. Precision medicine is formalized as a treatment regime which comprises a sequence of decision rules, one per decision point, which map up-to-date patient information to a recommended action. The potential actions could be the selection of which drug to use, the selection of dose, timing of administration, specific diet or exercise recommendation, or other aspects of treatment or care. Statistics research in precision medicine is broadly focused on methodological development for estimation of and inference for treatment regimes which maximize some cumulative clinical outcome. In this review, we provide an overview of this vibrant area of research and present important and emerging challenges.

Novel agents for primary central nervous system lymphoma: evidence and perspectives

Primary central nervous system lymphoma (PCNSL) is a rare aggressive extranodal non- Hodgkin lymphoma. Although high remission rates can be achieved with high-dose methotrexate-based immunochemotherapy, risk of relapse and associated death is still substantial in at least a third of patients. Novel agents for treating lymphoid malignancies have substantially enriched treatment options for PCNSL. We herein systematically review the existing clinical evidence of novel agents in treatment of PCNSL, summarize ongoing studies, and discuss perspectives. The body of evidence for novel agents is still limited to noncomparative studies, but the most promising approaches include Bruton kinase inhibition with ibrutinib and immunomodulatory treatment (eg, with lenalidomide). Targeting the mammalian target of rapamycin pathway does not seem to have a meaningful clinical benefit, and evidence of checkpoint inhibition with nivolumab is limited to anecdotal evidence. Future studies should embrace the concept of induction and maintenance therapy as well as the combination of drugs with different mechanisms of action. Selection of patients based on molecular profiling and relapse patterns should be another aspect informing future comparative trials, which are urgently needed to improve prognosis for patients with PCNSL.

Hypertrophic Burn Scar Research: From Quantitative Assessment to Designing Clinical Sequential Multiple Assignment Randomized Trials

This article explores the current options for the quantitative assessment of hypertrophic burn scars. It also introduces a novel type of randomized, controlled trial, which relies on heterogeneity of the subject population to improve the predictive value of personalized treatment strategies.

Sequential, Multiple Assignment, Randomized Trial Designs in Immuno-oncology Research

Clinical trials investigating immune checkpoint inhibitors have led to the approval of anti-CTLA-4 (cytotoxic T-lymphocyte antigen-4), anti-PD-1 (programmed death-1), and anti-PD-L1 (PD-ligand 1) drugs by the FDA for numerous tumor types. In the treatment of metastatic melanoma, combinations of checkpoint inhibitors are more effective than single-agent inhibitors, but combination immunotherapy is associated with increased frequency and severity of toxicity. There are questions about the use of combination immunotherapy or single-agent anti-PD-1 as initial therapy and the number of doses of either approach required to sustain a response. In this article, we describe a novel use of sequential, multiple assignment, randomized trial (SMART) design to evaluate immune checkpoint inhibitors to find treatment regimens that adapt within an individual based on intermediate response and lead to the longest overall survival. We provide a hypothetical example SMART design for BRAF wild-type metastatic melanoma as a framework for investigating immunotherapy treatment regimens. We compare implementing a SMART design to implementing multiple traditional randomized clinical trials. We illustrate the benefits of a SMART over traditional trial designs and acknowledge the complexity of a SMART. SMART designs may be an optimal way to find treatment strategies that yield durable response, longer survival, and lower toxicity. Clin Cancer Res; 24(4); 730-6. ©2017 AACR.

Tools for the Precision Medicine Era: How to Develop Highly Personalized Treatment Recommendations From Cohort and Registry Data Using Q-Learning

Q-learning is a method of reinforcement learning that employs backwards stagewise estimation to identify sequences of actions that maximize some long-term reward. The method can be applied to sequential multiple-assignment randomized trials to develop personalized adaptive treatment strategies (ATSs)-longitudinal practice guidelines highly tailored to time-varying attributes of individual patients. Sometimes, the basis for choosing which ATSs to include in a sequential multiple-assignment randomized trial (or randomized controlled trial) may be inadequate. Nonrandomized data sources may inform the initial design of ATSs, which could later be prospectively validated. In this paper, we illustrate challenges involved in using nonrandomized data for this purpose with a case study from the Center for International Blood and Marrow Transplant Research registry (1995-2007) aimed at 1) determining whether the sequence of therapeutic classes used in graft-versus-host disease prophylaxis and in refractory graft-versus-host disease is associated with improved survival and 2) identifying donor and patient factors with which to guide individualized immunosuppressant selections over time. We discuss how to communicate the potential benefit derived from following an ATS at the population and subgroup levels and how to evaluate its robustness to modeling assumptions. This worked example may serve as a model for developing ATSs from registries and cohorts in oncology and other fields requiring sequential treatment decisions.

Adaptive Interventions in Child and Adolescent Mental Health

The treatment or prevention of child and adolescent mental health (CAMH) disorders often requires an individualized, sequential approach to intervention, whereby treatments (or prevention efforts) are adapted over time based on the youth's evolving status (e.g., early response, adherence). Adaptive interventions are intended to provide a replicable guide for the provision of individualized sequences of interventions in actual clinical practice. Recently, there has been great interest in the development of adaptive intervenions by investigators working in CAMH. The development of such replicable, real-world, individualized sequences of decision rules to guide the treatment or prevention of CAMH disorders represents an important "next step" in interventions research. The primary purpose of this special issue is to showcase some recent work on the science of adaptive interventions in CAMH. In this overview article, we review why individualized sequences of interventions are needed in CAMH, provide an introduction to adaptive interventions, briefly describe each of the articles included in this special issue, and describe some exciting areas of ongoing and future research. A hopeful outcome of this special issue is that it encourages other researchers in CAMH to pursue creative and significant research on adaptive interventions.