Adaptive treatment strategies in chronic disease

A clinician's guide to single vs multiple inhaler therapy for COPD

INTRODUCTION

In the management of chronic obstructive pulmonary disease (COPD), inhalation therapy plays a pivotal role. However, clinicians often face the dilemma of choosing between single and multiple inhaler therapies for their patients. This choice is critical because it can affect treatment efficacy, patient adherence, and overall disease management.

AREAS COVERED

This article examines the advantages and factors to be taken into consideration when selecting between single and multiple inhaler therapies for COPD.

EXPERT OPINION

Both single and multiple inhaler therapies must be considered in COPD management. While single inhaler therapy offers simplicity and convenience, multiple inhaler therapy provides greater flexibility and customization. Clinicians must carefully evaluate individual patient needs and preferences to determine the most appropriate inhaler therapy regimen. Through personalized treatment approaches and shared decision-making, clinicians can optimize COPD management and improve patient well-being. Nevertheless, further research is required to compare the effectiveness of single versus multiple inhaler strategies through rigorous clinical trials, free from industry bias, to determine the optimal inhaler strategy. Smart inhaler technology appears to have the potential to enhance adherence and personalized management, but the relative merits of smart inhalers in single inhaler regimens versus multiple inhaler regimens remain to be determined.

Development of decision rules for an adaptive aftercare intervention based on individual symptom courses for agoraphobia patients

As other mental illnesses, agoraphobia is associated with a significant risk for relapse after the end of treatment. Personalized and adaptive approaches appear promising to improve maintenance treatment and aftercare as they acknowledge patients' varying individual needs with respect to intensity of care over time. Currently, there is a deficit of knowledge about the detailed symptom course after discharge from acute treatment, which is a prerequisite for the empirical development of rules to decide if and when aftercare should be intensified. Therefore, this study aimed firstly at the investigation of the naturalistic symptom course of agoraphobia after discharge from initial treatment and secondly at the development and evaluation of a data-driven algorithm for a digital adaptive aftercare intervention. A total of 56 agoraphobia patients were recruited in 3 hospitals. Following discharge, participants completed a weekly online monitoring assessment for three months. While symptom severity remained stable at the group level, individual courses were highly heterogeneous. Approximately two-thirds of the patients (70%) reported considerable symptoms at some time, indicating a need for medium or high-intense therapeutic support. Simulating the application of the algorithm to the data set resulted in an early (86% before week six) and relatively even allocation of patients to three groups (need for no, medium, and high-intense support respectively). Overall, findings confirm the need for adaptive aftercare strategies in agoraphobia. Digital, adaptive approaches may provide immediate support to patients who experience symptom deterioration and thus promise to contribute to an optimized allocation of therapeutic resources and overall improvement of care.

On "Reflections on the concept of optimality of single decision point treatment regimes"

This is a discussion of "Reflections on the concept of optimality of single decision point treatment regimes" by Trung Dung Tran, Ariel Alonso Abad, Geert Verbeke, Geert Molenberghs, and Iven Van Mechelen. The authors propose a thoughtful consideration of optimization targets and the implications of such targets for the resulting optimal treatment rule. However, we contest the assertation that targets of optimization have been overlooked and suggest additional considerations that researchers must contemplate as part of a complete framework for learning about optimal treatment regimes.

iVY: protocol for a randomised clinical trial to test the effect of a technology-based intervention to improve virological suppression among young adults with HIV in the USA

INTRODUCTION

Young adults with HIV (YWH) experience worse clinical outcomes than adults and have high rates of substance use (SU) and mental illness that impact their engagement in care and adherence to antiretroviral therapy (ART). The intervention for Virologic Suppression in Youth (iVY) aims to address treatment engagement/adherence, mental health (MH) and SU in a tailored manner using a differentiated care approach that is youth friendly. Findings will provide information about the impact of iVY on HIV virological suppression, MH and SU among YWH who are disproportionately impacted by HIV and at elevated risk for poor health outcomes.

METHODS AND ANALYSIS

The iVY study will test the effect of a technology-based intervention with differing levels of resource requirements (ie, financial and personnel time) in a randomised clinical trial with an adaptive treatment strategy among 200 YWH (18-29 years old). The primary outcome is HIV virological suppression measured via dried blood spot. This piloted and protocolised intervention combines: (1) brief weekly sessions with a counsellor via a video-chat platform (video-counselling) to discuss MH, SU, HIV care engagement/adherence and other barriers to care; and (2) a mobile health app to address barriers such as ART forgetfulness, and social isolation. iVY has the potential to address important, distinct and changing barriers to HIV care engagement (eg, MH, SU) to increase virological suppression among YWH at elevated risk for poor health outcomes.

ETHICS AND DISSEMINATION

This study and its protocols have been approved by the University of California, San Francisco Institutional Review Board. Study staff will work with a Youth Advisory Panel to disseminate results to YWH, participants and the academic community.

TRIAL REGISTRATION NUMBER

NCT05877729.

Adaptive Smoking Cessation Using Precessation Varenicline or Nicotine Patch: A Randomized Clinical Trial

Importance

Adaptive pharmacotherapy, ie, starting a medication regimen and then modifying that regimen based on patient response, is common in many medical domains but is not common in smoking cessation. Recently, studies have found that adaptive treatment using precessation nicotine patches is efficacious for smoking cessation; however, adaptive treatment using precessation varenicline and adaptive treatment in clinical practice settings have not been fully assessed.

Objective

To determine whether adaptive pharmacotherapy leads to higher smoking abstinence rates than standard pharmacotherapy in a clinical practice setting.

Design, Setting, and Participants

This double-blinded stratified placebo-controlled randomized clinical trial compared adaptive treatment with standard treatment for smoking cessation. The study was conducted at a university health system in Durham, North Carolina, from February 2018 to May 2020 and was stopped early due to COVID-19. Data were analyzed as intent-to-treat from May 24, 2021, to February 27, 2022.

Interventions

Participants were allowed to choose varenicline or nicotine patches and were then randomized to adaptive or nonadaptive (standard) treatment. Participants started on their chosen medication (adaptive) or placebo (standard) 4 weeks before their target quit day. Two weeks later, participants were assessed for treatment response. Adaptive participants who did not decrease daily cigarettes smoked by at least 50% (nonresponders) received bupropion in addition to their chosen medication. Participants in the adaptative treatment group who did decrease daily cigarettes smoked by at least 50% (responders) and participants in the standard treatment group received additional placebo bupropion. Participants in the standard treatment group received varenicline starting 1 week before the target quit date or nicotine patches starting on the target quit day. All participants received brief behavioral support.

Main Outcome and Measures

The main outcome was biochemically verified 30-day continuous smoking abstinence 12 weeks after their target quit smoking day. Other measures included demographic characteristics, smoking history, and repeated smoking assessments.

Results

Of the planned 300 participants, a total of 188 participants (mean [SD] age, 49.1 [12.5] years; 102 [54%] female) were enrolled before the trial was stopped because of the COVID-19 pandemic. A total of 127 participants chose to use varenicline, including 64 randomized to adaptive treatment and 63 randomized to standard treatment, and 61 participants chose to use nicotine patches, including 31 randomized to adaptive treatment and 30 randomized to standard treatment. At baseline, participants smoked a mean (SD) of 15.4 (7.3) cigarettes per day. At 12 weeks after the target quit day, biochemically verified 30-day continuous smoking abstinence was observed in 23 of 95 participants (24%) in the adaptive treatment group and 8 of 93 participants (9%) in the standard treatment (odds ratio [OR], 3.38; 95% CI, 1.43-7.99; P = .004); among participants who used varenicline, 30-day continuous abstinence was 18 participants (28%) in the adaptive treatment group, and 5 participants (8%) in the standard treatment group (OR, 4.54; 95% CI, 1.57-13.15); among participants who used nicotine patches, 30-day continuous abstinence was 5 participants (16%) in the adaptive treatment group and 3 participants (10%) in the standard treatment group (OR, 1.73; 95% CI, 0.38-7.99). Sleep problems were more common for participants in the varenicline adaptive treatment group than in the varenicline standard treatment group (rate ratio, 1.74; 95% CI, 1.18-2.58; P = .03).

Conclusions and Relevance

This randomized clinical trial found that adaptive pharmacotherapy was efficacious for smoking cessation treatment in a practice setting.

Trial Registration

ClinicalTrials.gov Identifier: NCT02501265.

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.

Accessibility of Psychological Treatments for Bulimia Nervosa: A Review of Efficacy and Engagement in Online Self-Help Treatments

Bulimia nervosa is an eating disorder characterised by marked impairment to one's physical health and social functioning, as well as high rates of chronicity and comorbidity. This literature review aims to summarise existing academic research related to the symptom profile of BN, the costs and burden imposed by the illness, barriers to the receipt of care, and the evidence base for available psychological treatments. As a consequence of well-documented difficulties in accessing evidence-based treatments for eating disorders, efforts have been made towards developing innovative, diverse channels to deliver treatment, with several of these attempting to harness the potential of digital platforms. In response to the increasing number of trials investigating the utility of online treatments, this paper provides a critical review of previous attempts to examine digital interventions in the treatment of eating disorders. The results of a focused literature review are presented, including a detailed synthesis of a knowledgeable selection of high-quality articles with the aim of providing an update on the current state of research in the field. The results of the review highlight the potential for online self-help treatments to produce moderately sized reductions in core behavioural and cognitive symptoms of eating disorders. However, concern is raised regarding the methodological limitations of previous research in the field, as well as the high rates of dropout and poor adherence reported across most studies. The review suggests directions for future research, including the need to replicate previous findings using rigorous study design and methodology, as well as further investigation regarding the utility of clinician support and interactive digital features as potential mechanisms for offsetting low rates of engagement with online treatments.

An efficient approach for optimizing the cost-effective individualized treatment rule using conditional random forest

Evidence from observational studies has become increasingly important for supporting healthcare policy making via cost-effectiveness analyses. Similar as in comparative effectiveness studies, health economic evaluations that consider subject-level heterogeneity produce individualized treatment rules that are often more cost-effective than one-size-fits-all treatment. Thus, it is of great interest to develop statistical tools for learning such a cost-effective individualized treatment rule under the causal inference framework that allows proper handling of potential confounding and can be applied to both trials and observational studies. In this paper, we use the concept of net-monetary-benefit to assess the trade-off between health benefits and related costs. We estimate cost-effective individualized treatment rule as a function of patients' characteristics that, when implemented, optimizes the allocation of limited healthcare resources by maximizing health gains while minimizing treatment-related costs. We employ the conditional random forest approach and identify the optimal cost-effective individualized treatment rule using net-monetary-benefit-based classification algorithms, where two partitioned estimators are proposed for the subject-specific weights to effectively incorporate information from censored individuals. We conduct simulation studies to evaluate the performance of our proposals. We apply our top-performing algorithm to the NIH-funded Systolic Blood Pressure Intervention Trial to illustrate the cost-effectiveness gains of assigning customized intensive blood pressure therapy.

Projection based inverse reinforcement learning for the analysis of dynamic treatment regimes

Dynamic Treatment Regimes (DTRs) are adaptive treatment strategies that allow clinicians to personalize dynamically the treatment for each patient based on their step-by-step response to their treatment. There are a series of predefined alternative treatments for each disease and any patient may associate with one of these treatments according to his/her demographics. DTRs for a certain disease are studied and evaluated by means of statistical approaches where patients are randomized at each step of the treatment and their responses are observed. Recently, the Reinforcement Learning (RL) paradigm has also been applied to determine DTRs. However, such approaches may be limited by the need to design a true reward function, which may be difficult to formalize when the expert knowledge is not well assessed, as when the DTR is in the design phase. To address this limitation, an extension of the RL paradigm, namely Inverse Reinforcement Learning (IRL), has been adopted to learn the reward function from data, such as those derived from DTR trials. In this paper, we define a Projection Based Inverse Reinforcement Learning (PB-IRL) approach to learn the true underlying reward function for given demonstrations (DTR trials). Such a reward function can be used both to evaluate the set of DTRs determined for a certain disease, as well as to enable an RL-based intelligent agent to self-learn the best way and then act as a decision support system for the clinician.

Extended-release pharmacotherapy for opioid use disorder (EXPO): protocol for an open-label randomised controlled trial of the effectiveness and cost-effectiveness of injectable buprenorphine versus sublingual tablet buprenorphine and oral liquid methadone

BACKGROUND

Sublingual tablet buprenorphine (BUP-SL) and oral liquid methadone (MET) are the daily, standard-of-care (SOC) opioid agonist treatment medications for opioid use disorder (OUD). A sizable proportion of the OUD treatment population is not exposed to sufficient treatment to attain the desired clinical benefit. Two promising therapeutic technologies address this deficit: long-acting injectable buprenorphine and personalised psychosocial interventions (PSI). This study will determine (A) the effectiveness and cost-effectiveness - monthly injectable, extended-release (BUP-XR) in a head-to-head comparison with BUP-SL and MET, and (B) the effectiveness of BUP-XR with adjunctive PSI versus BUP-SL and MET with PSI. Safety, retention, craving, substance use, quality-adjusted life years, social functioning, and subjective recovery from OUD will be also evaluated.

METHODS

This is a pragmatic, multi-centre, open-label, parallel-group, superiority RCT, with a qualitative (mixed-methods) evaluation. The study population is adults. The setting is five National Health Service community treatment centres in England and Scotland. At each centre, participants will be randomly allocated (1:1) to BUP-XR or SOC. At the London study co-ordinating centre, there will also be allocation of participants to BUP-XR with PSI or SOC with PSI. With 24 weeks of study treatment, the primary outcome is days of abstinence from non-medical opioids during study weeks 2-24 combined with up to 12 urine drug screen tests for opioids. For 90% power (alpha, 5%; 15% inflation for attrition), 304 participants are needed for the BUP-XR versus SOC comparison. With the same planning parameters, 300 participants are needed for the BUP-XR and PSI versus SOC and PSI comparison. Statistical and health economic analysis plans will be published before data-lock on the Open Science Framework. Findings will be reported in accordance with the Consolidated Standards of Reporting Trials and Consolidated Health Economic Evaluation Reporting Standards.

DISCUSSION

This pragmatic randomised controlled trial is the first evaluation of injectable BUP-XR versus the SOC medications BUP-SL and MET, with personalised PSI. If there is evidence for the superiority of BUP-XR over SOC medication, study findings will have substantial implications for OUD clinical practice and treatment policy in the UK and elsewhere.

TRIAL REGISTRATION

EU Clinical Trials register 2018-004460-63.

Optimizing the dynamic treatment regime of in-hospital warfarin anticoagulation in patients after surgical valve replacement using reinforcement learning

OBJECTIVE

Warfarin anticoagulation management requires sequential decision-making to adjust dosages based on patients' evolving states continuously. We aimed to leverage reinforcement learning (RL) to optimize the dynamic in-hospital warfarin dosing in patients after surgical valve replacement (SVR).

MATERIALS AND METHODS

10 408 SVR cases with warfarin dosage-response data were retrospectively collected to develop and test an RL algorithm that can continuously recommend daily warfarin doses based on patients' evolving multidimensional states. The RL algorithm was compared with clinicians' actual practice and other machine learning and clinical decision rule-based algorithms. The primary outcome was the ratio of patients without in-hospital INRs >3.0 and the INR at discharge within the target range (1.8-2.5) (excellent responders). The secondary outcomes were the safety responder ratio (no INRs >3.0) and the target responder ratio (the discharge INR within 1.8-2.5).

RESULTS

In the test set (n = 1260), the excellent responder ratio under clinicians' guidance was significantly lower than the RL algorithm: 41.6% versus 80.8% (relative risk [RR], 0.51; 95% confidence interval [CI], 0.48-0.55), also the safety responder ratio: 83.1% versus 99.5% (RR, 0.83; 95% CI, 0.81-0.86), and the target responder ratio: 49.7% versus 81.1% (RR, 0.61; 95% CI, 0.58-0.65). The RL algorithms performed significantly better than all the other algorithms. Compared with clinicians' actual practice, the RL-optimized INR trajectory reached and maintained within the target range significantly faster and longer.

DISCUSSION

RL could offer interactive, practical clinical decision support for sequential decision-making tasks and is potentially adaptable for varied clinical scenarios. Prospective validation is needed.

CONCLUSION

An RL algorithm significantly optimized the post-operation warfarin anticoagulation quality compared with clinicians' actual practice, suggesting its potential for challenging sequential decision-making tasks.

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.

Sequential Multiple Assignment Randomized Trial (SMART) to identify optimal sequences of telemedicine interventions for improving initiation of insulin therapy: A simulation study

BACKGROUND

To examine the value of a Sequential Multiple Assignment Randomized Trial (SMART) design compared to a conventional randomized control trial (RCT) for telemedicine strategies to support titration of insulin therapy for Type 2 Diabetes Mellitus (T2DM) patients new to insulin.

METHODS

Microsimulation models were created in R using a synthetic sample based on primary data from 63 subjects enrolled in a pilot study of a smartphone application (App), Diabetes Pal compared to a nurse-based telemedicine strategy (Nurse). For comparability, the SMART and an RCT design were constructed to allow comparison of four (embedded) adaptive interventions (AIs).

RESULTS

In the base case scenario, the SMART has similar overall mean expected HbA1c and cost per subject compared with RCT, for sample size of n = 100 over 10,000 simulations. SMART has lower (better) standard deviations of the mean expected HbA1c per AI, and higher efficiency of choosing the correct AI across various sample sizes. The differences between SMART and RCT become apparent as sample size decreases. For both trial designs, the threshold value at which a subject was deemed to have been responsive at an intermediate point in the trial had an optimal choice (i.e., the sensitivity curve had a U-shape). SMART design dominates the RCT, in the overall mean HbA1c (lower value) when the threshold value is close to optimal.

CONCLUSIONS

SMART is suited to evaluating the efficacy of different sequences of treatment options, in addition to the advantage of providing information on optimal treatment sequences.

Innovative trial approaches in immune-mediated inflammatory diseases: current use and future potential

BACKGROUND

Despite progress that has been made in the treatment of many immune-mediated inflammatory diseases (IMIDs), there remains a need for improved treatments. Randomised controlled trials (RCTs) provide the highest form of evidence on the effectiveness of a potential new treatment regimen, but they are extremely expensive and time consuming to conduct. Consequently, much focus has been given in recent years to innovative design and analysis methods that could improve the efficiency of RCTs. In this article, we review the current use and future potential of these methods within the context of IMID trials.

METHODS

We provide a review of several innovative methods that would provide utility in IMID research. These include novel study designs (adaptive trials, Sequential Multi-Assignment Randomised Trials, basket, and umbrella trials) and data analysis methodologies (augmented analyses of composite responder endpoints, using high-dimensional biomarker information to stratify patients, and emulation of RCTs from routinely collected data). IMID trials are now well-placed to embrace innovative methods. For example, well-developed statistical frameworks for adaptive trial design are ready for implementation, whilst the growing availability of historical datasets makes the use of Bayesian methods particularly applicable. To assess whether and how these innovative methods have been used in practice, we conducted a review via PubMed of clinical trials pertaining to any of 51 IMIDs that were published between 2018 and 20 in five high impact factor clinical journals.

RESULTS

Amongst 97 articles included in the review, 19 (19.6%) used an innovative design method, but most of these were relatively straightforward examples of innovative approaches. Only two (2.1%) reported the use of evidence from routinely collected data, cohorts, or biobanks. Eight (9.2%) collected high-dimensional data.

CONCLUSIONS

Application of innovative statistical methodology to IMID trials has the potential to greatly improve efficiency, to generalise and extrapolate trial results, and to further personalise treatment strategies. Currently, such methods are infrequently utilised in practice. New research is required to ensure that IMID trials can benefit from the most suitable methods.

Replicating an effective VA program to train and support family caregivers: a hybrid type III effectiveness-implementation design

BACKGROUND

Caring for a growing aging population using existing long-term care resources while simultaneously supporting and educating family caregivers, is a public health challenge. We describe the application of the Replicating Effective Programs (REP) framework, developed by the Centers for Disease Control Prevention and used in public health program implementation, to scale up an evidence-based family caregiver training intervention in the Veterans Affairs (VA) healthcare system.

METHODS

From 2018 to 2020, clinicians at eight VA medical centers received REP-guided implementation including facilitation, technical assistance, and implementation tools to deliver the training program. The project team used the REP framework to develop activities across four distinct phases - (1) pre-conditions, (2) pre-implementation, (3) implementation, and (4) maintenance and evolution - and systematically tracked implementation facilitators, barriers, and adaptations.

RESULTS

Within the REP framework, results describe how each medical center adapted implementation approaches to fit local needs. We highlight examples of how sites balanced adaptations and intervention fidelity.

CONCLUSIONS

The REP framework shows promise for national expansion of the caregiver training intervention, including to non-VA systems of care, because it allows sites to adapt while maintaining intervention fidelity.

TRIAL REGISTRATION

NCT03474380 . Date registered: March 22, 2018.

Timing matters: real-world effectiveness of early combination of biologic and conventional synthetic disease-modifying antirheumatic drugs for treating newly diagnosed polyarticular course juvenile idiopathic arthritis

OBJECTIVES

To compare real-world effectiveness of two adaptive treatment strategies of disease-modifying antirheumatic drugs (DMARDs) in treating children with newly diagnosed polyarticular course juvenile idiopathic arthritis (pcJIA): early aggressive use of biologic DMARDs (bDMARDs) in combination with conventional synthetic DMARDs (csDMARDs) versus conservative delayed use of bDMARDs following the initial csDMARD prescription.

METHODS

A single-centre newly diagnosed DMARD-naive pcJIA patient database (n=465) was derived from the electronic medical records between 1 January 2009 and 31 December 2018. The primary study endpoints were clinical Juvenile Arthritis Disease Activity Score (cJADAS) at 6 and 12 months following the first DMARD prescription. The secondary study endpoint was Pediatric Quality of Life Inventory (PedsQL) generic total score at 12 months. Averaged causal treatment effects were assessed using a Bayesian non-parametric casual inference method.

RESULTS

Both cJADAS and PedsQL improve over time, regardless of the treatment strategies. Compared with the conservative approach, early aggressive approach is more effective in reducing cJADAS (mean -2.17, 95% CI -3.77 to -0.56) by 6 months. Adding bDMARD after 6 months to the initial treatment provides very little added benefit. The averaged treatment effect was 6.35 (95% CI -5.89 to 18.58) improvement in PedsQL at 12 months.

CONCLUSIONS

Timing matters-early aggressive use with bDMARDs is more effective than conservative delayed treatment in lowering disease activity after 6 and 12 months of treatment.

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.

A scoping review of studies using observational data to optimise dynamic treatment regimens

BACKGROUND

Dynamic treatment regimens (DTRs) formalise the multi-stage and dynamic decision problems that clinicians often face when treating chronic or progressive medical conditions. Compared to randomised controlled trials, using observational data to optimise DTRs may allow a wider range of treatments to be evaluated at a lower cost. This review aimed to provide an overview of how DTRs are optimised with observational data in practice.

METHODS

Using the PubMed database, a scoping review of studies in which DTRs were optimised using observational data was performed in October 2020. Data extracted from eligible articles included target medical condition, source and type of data, statistical methods, and translational relevance of the included studies.

RESULTS

From 209 PubMed abstracts, 37 full-text articles were identified, and a further 26 were screened from the reference lists, totalling 63 articles for inclusion in a narrative data synthesis. Observational DTR models are a recent development and their application has been concentrated in a few medical areas, primarily HIV/AIDS (27, 43%), followed by cancer (8, 13%), and diabetes (6, 10%). There was substantial variation in the scope, intent, complexity, and quality between the included studies. Statistical methods that were used included inverse-probability weighting (26, 41%), the parametric G-formula (16, 25%), Q-learning (10, 16%), G-estimation (4, 6%), targeted maximum likelihood/minimum loss-based estimation (4, 6%), regret regression (3, 5%), and other less common approaches (10, 16%). Notably, studies that were primarily intended to address real-world clinical questions (18, 29%) tended to use inverse-probability weighting and the parametric G-formula, relatively well-established methods, along with a large amount of data. Studies focused on methodological developments (45, 71%) tended to be more complicated and included a demonstrative real-world application only.

CONCLUSIONS

As chronic and progressive conditions become more common, the need will grow for personalised treatments and methods to estimate the effects of DTRs. Observational DTR studies will be necessary, but so far their use to inform clinical practice has been limited. Focusing on simple DTRs, collecting large and rich clinical datasets, and fostering tight partnerships between content experts and data analysts may result in more clinically relevant observational DTR studies.

Reinforcement Learning in Neurocritical and Neurosurgical Care: Principles and Possible Applications

Dynamic decision-making was essential in the clinical care of surgical patients. Reinforcement learning (RL) algorithm is a computational method to find sequential optimal decisions among multiple suboptimal options. This review is aimed at introducing RL's basic concepts, including three basic components: the state, the action, and the reward. Most medical studies using reinforcement learning methods were trained on a fixed observational dataset. This paper also reviews the literature of existing practical applications using reinforcement learning methods, which can be further categorized as a statistical RL study and a computational RL study. The review proposes several potential aspects where reinforcement learning can be applied in neurocritical and neurosurgical care. These include sequential treatment strategies of intracranial tumors and traumatic brain injury and intraoperative endoscope motion control. Several limitations of reinforcement learning are representations of basic components, the positivity violation, and validation methods.

Bringing New Meaning to the Term "Adaptive Trial": Challenges of Conducting Clinical Research During the Coronavirus Disease 2019 Pandemic and Implications for Implementation Science

Although implementation of evidence-based practices takes an average of 17 years, in the context of the global pandemic, coronavirus disease 2019 (COVID-19) interventions were adopted in a greatly compressed time frame. This rapid uptake creates major challenges for conducting COVID-19 clinical research studies, because quickly evolving standards make it difficult to adapt in real time. The rapid dissemination and implementation of COVID-19 interventions is the realization of goals long pursued by the implementation science community. However, the downside of the rapid implementation is that low-quality evidence with little to no scientific vetting may be quickly integrated into clinical care, resulting in lost opportunities to advance our scientific understanding about how to manage infected patients. In the future, novel adaptive designs embedded into electronic health records (Embedded Quantified, Integrated-into-Practice Trial [EQuIPT] designs) that allow for easier and better access to clinical trials may simultaneously improve care and advance healthcare innovations.

Innovative Implementation Strategies for Hypertension Control in Low- and Middle-Income Countries: a Narrative Review

PURPOSE OF REVIEW

This review summarizes the most recent and innovative implementation strategies for hypertension control in low- and middle-income countries (LMICs).

RECENT FINDINGS

Implementation strategies from Latin America, Africa, and Asia were organized across three levels: community, health system, and policy/population. Multicomponent interventions involving task-shifting strategies, with or without mobile health tools, had the most supporting evidence, with policy or population-level interventions having the least, focused only on salt reduction with mixed results. More research is needed to better understand how context affects intervention implementation. There is an emerging evidence base for implementation strategies for hypertension control and CVD risk reduction in LMICs at the community and health system levels, but further research is needed to determine the most effective policy and population-level strategies. How to best account for local context in adapting and implementing these evidence-based interventions in LMICs still remains largely unknown. Accelerating the translation of this implementation research into policy and practice is imperative to improve health and save lives globally.

Small molecules under development for psoriasis: on the road to the individualized therapies

Psoriasis is an incurable cutaneous illness characterized by the presence of well-delimited reddish plaques and silvery-white dry scales. So far, there is a limited understanding of its pathogenesis, though recent discoveries on the immunological, genetic and molecular aspects of this disease have significantly contributed to the identification of new targets and the development of novel drugs. Despite these advances, many patients are still dissatisfied, so to improve patient satisfaction, reliability, and clinical outcomes, the individualization of the treatments for this disease becomes a necessity. This review summarizes recent findings related to psoriasis pathogenesis and describes new small molecules and targets recently identified as promising for treatments. Additionally, the current status, challenges and the future directions for achieving individualized therapy for this disease and the need for more collaborative studies are discussed. The individualization of treatments for psoriasis, rather than a goal, is analyzed as a process where a dynamic integration between the needs and characteristics of the patients, the pharmacological progress, and the clinical decisions takes place.

Randomized controlled trial testing the effectiveness of adaptive "SMART" stepped-care treatment for adults with binge-eating disorder comorbid with obesity

This randomized controlled trial (RCT) tested effectiveness of adaptive SMART stepped-care treatment to "standard" behavioral weight loss (BWL [standard]) for patients with binge-eating disorder (BED) and obesity. One hundred ninety-one patients were randomly assigned to 6 months of BWL (standard; n = 39) or stepped care (n = 152). Within stepped care, patients started with BWL for 1 month; treatment responders continued BWL, whereas nonresponders switched to cognitive-behavioral therapy (CBT), and patients receiving stepped care were additionally randomized to weight-loss medication or placebo (double-blind) for the remaining 5 months. Independent assessments were performed reliably at baseline, throughout treatment, and posttreatment. Intent-to-treat (ITT) analyses of remission rates (zero binges/month) revealed that BWL (standard) and stepped care did not differ (74.4% vs. 66.5%); within stepped care, remission rates ranged 40.0% to 83.3%, with medication significantly superior to placebo (overall) and among nonresponders switched to CBT. Mixed-models analyses of binge-eating frequency revealed significant time effects, but BWL (standard) and stepped care did not differ; within stepped care, medication was significantly superior to placebo and among nonresponders switched to CBT. Mixed models revealed significant weight loss, but BWL (standard; 5.1% weight-loss) and stepped care (5.8% weight-loss) did not differ; within stepped care (range = 0.4% to 8.8% weight-loss), medication was significantly superior to placebo and among both responders continued on BWL and nonresponders switched to CBT. In summary, BWL (standard) and adaptive stepped-care treatments produced robust improvements in binge eating and weight loss in patients with BED/obesity. Within adaptive stepped care, weight-loss medication enhanced outcomes for BED/obesity. Implications for clinical practice and future adaptive designs are offered. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Adaptive Intervention Designs to Promote Behavioral Change in Adults: What Is the Evidence?

PURPOSE OF REVIEW

Adaptive behavioral interventions tailor the type or dose of intervention strategies to individuals over time to improve saliency and intervention efficacy. This review describes the unique characteristics of adaptive intervention designs, summarizes recent diabetes-related prevention studies, which used adaptive designs, and offers recommendations for future research.

RECENT FINDINGS

Eight adaptive intervention studies were reported since 2013 to reduce sedentary behavior or improve weight management in overweight or obese adults. Primarily, feasibility studies were conducted. Preliminary results suggest that just-in-time adaptive interventions can reduce sedentary behavior or increase minutes of physical activity through repeated prompts. A stepped-down weight management intervention did not increase weight loss compared to a fixed intervention. Other adaptive interventions to promote weight management are underway and require further evaluation. Additional research is needed to target a broader range of health-related behaviors, identify optimal decision points and dose for intervention, develop effective engagement strategies, and evaluate outcomes using randomized trials.

The effectiveness and safety of combining varenicline with nicotine e-cigarettes for smoking cessation in people with mental illnesses and addictions: study protocol for a randomised-controlled trial

Background

Smoking rates are higher in New Zealand (NZ) adults with mental illnesses and alcohol and other drug (AOD) addictions, compared to the overall population. Quit attempts using “gold standard” smoking cessation treatments often fail in people with these conditions, so more flexible treatment regimens that adapt to a person’s responsiveness to treatment are worth investigating. The STATUS trial aims to evaluate the effectiveness and safety of combining varenicline with nicotine e-cigarettes for smoking cessation among varenicline non-responders in treatment for mental health illnesses and/or AOD addictions.

Methods

This is a pragmatic two-arm, open-label, randomised trial. Participants will be daily smokers using mental health and/or addiction services in Auckland, aged ≥18 years, motivated to quit smoking, and eligible to access varenicline through the NZ special authority process. After 2 weeks of using varenicline plus behavioural support, participants who have not reduced their daily smoking by ≥50% will be randomised (1:1) to either 10 weeks of continued varenicline use or 10 weeks of varenicline plus an 18 mg/mL nicotine e-cigarette. All participants will receive weekly withdrawal-orientated behavioural support calls for 6 weeks post-randomisation. The primary outcome is self-reported biochemically-verified (exhaled carbon monoxide) continuous abstinence at 24 weeks post-randomisation. Secondary outcomes, measured at six, 12 and 24 weeks post-randomisation include: self-reported continuous abstinence, 7-day point prevalence abstinence, smoking reduction, time to relapse, cross-over, use of other smoking cessation support, serious adverse events, treatment adherence, compliance, acceptability, dual use, continuation of treatment use, mental illness symptoms and AOD use, health-related quality of life, and cost-analysis. A sample size of 338 will confer 80% power (p = 0.05) to detect a 15% absolute difference between the varenicline alone and varenicline plus e-cigarette groups.

Discussion

People with mental illness and/or AOD addictions are just as motivated as others to quit smoking, but are less likely to succeed. Adapting smoking cessation medication after a lack of responsiveness in the first 2 weeks of initial treatment in this priority population by adding a nicotine e-cigarette may be one way to increase long-term quit rates.

Trial Registration

Australian NZ Clinical Trial Registry: ACTRN12616001355460 (29 September 2016).

Electronic supplementary material

The online version of this article (10.1186/s12889-018-5351-7) contains supplementary material, which is available to authorized users.

Getting "SMART" about implementing multi-tiered systems of support to promote school mental health

With the growing adoption and implementation of multi-tiered systems of support (MTSS) in school settings, there is increasing need for rigorous evaluations of adaptive-sequential interventions. That is, MTSS specify universal, selected, and indicated interventions to be delivered at each tier of support, yet few investigations have empirically examined the continuum of supports that are provided to students both within and across tiers. This need is compounded by a variety of prevention approaches that have been developed with distinct theoretical foundations (e.g., Positive Behavioral Interventions and Supports, Social-Emotional Learning) that are available within and across tiers. As evidence-based interventions continue to flourish, school-based practitioners greatly need evaluations regarding optimal treatment sequencing. To this end, we describe adaptive treatment strategies as a natural fit within the MTSS framework. Specifically, sequential multiple assignment randomized trials (SMART) offer a promising empirical approach to rigorously develop and compare adaptive treatment regimens within this framework.

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.

A SMART data analysis method for constructing adaptive treatment strategies for substance use disorders

AIMS

To demonstrate how Q-learning, a novel data analysis method, can be used with data from a sequential, multiple assignment, randomized trial (SMART) to construct empirically an adaptive treatment strategy (ATS) that is more tailored than the ATSs already embedded in a SMART.

METHOD

We use Q-learning with data from the Extending Treatment Effectiveness of Naltrexone (ExTENd) SMART (N = 250) to construct empirically an ATS employing naltrexone, behavioral intervention, and telephone disease management to reduce alcohol consumption over 24 weeks in alcohol dependent individuals.

RESULTS

Q-learning helped to identify a subset of individuals who, despite showing early signs of response to naltrexone, require additional treatment to maintain progress.

CONCLUSIONS

Q-learning can inform the development of more cost-effective, adaptive treatment strategies for treating substance use disorders.

Identification of the joint effect of a dynamic treatment intervention and a stochastic monitoring intervention under the no direct effect assumption

The management of chronic conditions is characterized by frequent re-assessment of therapy decisions in response to the patient's changing condition over the course of the illness. Evidence most suitable to inform care thus often concerns the contrast of adaptive treatment strategies that repeatedly personalize treatment decisions over time using the latest accumulated data available from the patient's previous clinic visits such as laboratory exams (e.g., hemoglobin A1c measurements in diabetes care). The frequency at which such information is monitored implicitly defines the causal estimand that is typically evaluated in an observational or randomized study of such adaptive treatment strategies. Analytic control of monitoring with standard estimation approaches for time-varying interventions can therefore not only improve study generalizibility but also inform the optimal timing of clinical surveillance. Valid inference with these estimators requires the upholding of a positivity assumption that can hinder their applicability. To potentially weaken this requirement for monitoring control, we introduce identifiability results that will facilitate the derivation of alternate estimators of effects defined by general joint treatment and monitoring interventions in the context of time-to-event outcomes. These results are developed based on the nonparametric structural equation modeling framework using a no direct effect assumption originally introduced in a prior paper that inspired this work. The relevance and scope of the results presented here are illustrated with examples in diabetes comparative effectiveness research.

Effectiveness of adjunctive, personalised psychosocial intervention for non-response to opioid agonist treatment: Study protocol for a pragmatic randomised controlled trial

INTRODUCTION

Opioid use disorder (OUD) is a debilitating and relapsing psychiatric disorder; opioid agonist therapy (OAT) is the front-line, evidence-supported treatment. A substantial number of patients relapse or continue to use heroin or other illicit drugs during OAT. There is considerable heterogeneity in the OAT-resistant sub-population, with many behavioural moderators of treatment response. We have developed a personalised psychosocial intervention (PSI) targeting these individuals. A formulation-guided assessment is linked to a toolkit of motivational, cognitive/behavioural and social support techniques. Change methods have been adapted from evidence-supported psychological therapies and are idiosyncratically tailored to the need and response.

METHODS

In this single-centre, 18-week, parallel group, pragmatic randomised clinical trial, we will determine the clinical and cost-effectiveness of the PSI as an adjunctive intervention during OAT, in comparison to opioid agonist treatment-as-usual. We plan to recruit 368 adults. The primary outcome measure is the proportion of participants categorised as 'responders' at the end of the intervention (defined as self-reported abstinence from heroin and cocaine with no positive biological drug tests during the 28days prior to the endpoint). Secondary outcomes include: percentage of days abstinent from heroin and cocaine in the 28days before follow-up; treatment retention; therapy compliance; health and social functioning; exploratory genetic biomarkers; and analyses of treatment moderation and mediation.

CONCLUSIONS

This pragmatic controlled trial determines the effectiveness and cost-effectiveness of a personalised PSI for non-responding patients during OAT. Our intervention applies motivational, cognitive/behavioural and social support techniques adapted from evidence-based therapies. Findings will inform stratified delivery of OAT.

Internet-Delivered Disease Management for Recurrent Depression: A Multicenter Randomized Controlled Trial

BACKGROUND

Strategies to improve the life of patients suffering from recurrent major depression have a high relevance. This study examined the efficacy of 2 Internet-delivered augmentation strategies that aim to prolong symptom-free intervals.

METHODS

Efficacy was tested in a 3-arm, multicenter, open-label, evaluator-blind, randomized controlled trial. Upon discharge from inpatient mental health care, 232 adults with 3 or more major depressive episodes were randomized to 1 of 2 intervention groups (SUMMIT or SUMMIT-PERSON) or to treatment as usual (TAU) alone. Over 12 months, participants in both intervention arms received, in addition to TAU, intense monitoring via e-mail or a smartphone, including signaling of upcoming crises, assistance with personal crisis management, and facilitation of early intervention. SUMMIT-PERSON additionally offered regular expert chats. The primary outcome was 'well weeks', i.e. weeks with at most mild symptoms assessed by the Longitudinal Interval Follow-Up Evaluation, during 24 months after the index treatment.

RESULTS

SUMMIT compared to TAU reduced the time with an unwell status (OR 0.48; 95% CI 0.23-0.98) through faster transitions from unwell to well (OR 1.44; 95% CI 0.83-2.50) and slower transitions from well to unwell (OR 0.69; 95% CI 0.44-1.09). Contrary to the hypothesis, SUMMIT-PERSON was not superior to either SUMMIT (OR 0.77; 95% CI 0.38-1.56) or TAU (OR 0.62; 95% CI 0.31-1.24). The efficacy of SUMMIT was strongest 8 months after the intervention.

CONCLUSIONS

The fully automated Internet-delivered augmentation strategy SUMMIT has the potential to improve TAU by reducing the lifelong burden of patients with recurrent depression. The fact that the effects wear off suggests a time-unlimited extension.

Being "SMART" About Adolescent Conduct Problems Prevention: Executing a SMART Pilot Study in a Juvenile Diversion Agency

The development of adaptive treatment strategies (ATS) represents the next step in innovating conduct problems prevention programs within a juvenile diversion context. Toward this goal, we present the theoretical rationale, associated methods, and anticipated challenges for a feasibility pilot study in preparation for implementing a full-scale SMART (i.e., sequential, multiple assignment, randomized trial) for conduct problems prevention. The role of a SMART design in constructing ATS is presented. The SMART feasibility pilot study includes a sample of 100 youth (13-17 years of age) identified by law enforcement as early stage offenders and referred for precourt juvenile diversion programming. Prior data on the sample population detail a high level of ethnic diversity and approximately equal representations of both genders. Within the SMART, youth and their families are first randomly assigned to one of two different brief-type evidence-based prevention programs, featuring parent-focused behavioral management or youth-focused strengths-building components. Youth who do not respond sufficiently to brief first-stage programming will be randomly assigned a second time to either an extended parent- or youth-focused second-stage programming. Measures of proximal intervention response and measures of potential candidate tailoring variables for developing ATS within this sample are detailed. Results of the described pilot study will include information regarding feasibility and acceptability of the SMART design. This information will be used to refine a subsequent full-scale SMART. The use of a SMART to develop ATS for prevention will increase the efficiency and effectiveness of prevention programing for youth with developing conduct problems.

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.

Comparing dynamic treatment regimes using repeated-measures outcomes: modeling considerations in SMART studies

A dynamic treatment regime (DTR) is a sequence of decision rules, each of which recommends a treatment based on a patient's past and current health status. Sequential, multiple assignment, randomized trials (SMARTs) are multi-stage trial designs that yield data specifically for building effective DTRs. Modeling the marginal mean trajectories of a repeated-measures outcome arising from a SMART presents challenges, because traditional longitudinal models used for randomized clinical trials do not take into account the unique design features of SMART. We discuss modeling considerations for various forms of SMART designs, emphasizing the importance of considering the timing of repeated measures in relation to the treatment stages in a SMART. For illustration, we use data from three SMART case studies with increasing level of complexity, in autism, child attention deficit hyperactivity disorder, and adult alcoholism. In all three SMARTs, we illustrate how to accommodate the design features along with the timing of the repeated measures when comparing DTRs based on mean trajectories of the repeated-measures outcome.

A new era in idiopathic pulmonary fibrosis: considerations for future clinical trials

The past decade has seen substantial progress in understanding the pathobiology, natural history, and clinical significance of idiopathic pulmonary fibrosis (IPF), culminating in the establishment of two effective medical therapies. Now seems an important time to reconsider the design and conduct of future IPF clinical trials. Building on lessons learned over the past decade, we use this perspective to lay out four key considerations for moving forward effectively and efficiently with the next generation of clinical trials in IPF. These are: development of a coordinated IPF clinical trials network; establishment of expectations for early phase proof of concept studies; adaptation of late-phase efficacy trial designs to the emergence of approved therapies, and; agreement on primary end-points for late phase clinical trials. Continued progress in the field of IPF will require creativity and collaboration on the part of all stakeholders. We believe that addressing these four considerations will encourage and enable investment in this new era of drug development in IPF, and will lead to more rapid development of effective therapies.

Targeted Learning of the Mean Outcome under an Optimal Dynamic Treatment Rule

We consider estimation of and inference for the mean outcome under the optimal dynamic two time-point treatment rule defined as the rule that maximizes the mean outcome under the dynamic treatment, where the candidate rules are restricted to depend only on a user-supplied subset of the baseline and intermediate covariates. This estimation problem is addressed in a statistical model for the data distribution that is nonparametric beyond possible knowledge about the treatment and censoring mechanism. This contrasts from the current literature that relies on parametric assumptions. We establish that the mean of the counterfactual outcome under the optimal dynamic treatment is a pathwise differentiable parameter under conditions, and develop a targeted minimum loss-based estimator (TMLE) of this target parameter. We establish asymptotic linearity and statistical inference for this estimator under specified conditions. In a sequentially randomized trial the statistical inference relies upon a second-order difference between the estimator of the optimal dynamic treatment and the optimal dynamic treatment to be asymptotically negligible, which may be a problematic condition when the rule is based on multivariate time-dependent covariates. To avoid this condition, we also develop TMLEs and statistical inference for data adaptive target parameters that are defined in terms of the mean outcome under the estimate of the optimal dynamic treatment. In particular, we develop a novel cross-validated TMLE approach that provides asymptotic inference under minimal conditions, avoiding the need for any empirical process conditions. We offer simulation results to support our theoretical findings.

Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research

The management of many health disorders often entails a sequential, individualized approach whereby treatment is adapted and readapted over time in response to the specific needs and evolving status of the individual. Adaptive interventions provide one way to operationalize the strategies (e.g., continue, augment, switch, step-down) leading to individualized sequences of treatment. Often, a wide variety of critical questions must be answered when developing a high-quality adaptive intervention. Yet, there is often insufficient empirical evidence or theoretical basis to address these questions. The Sequential Multiple Assignment Randomized Trial (SMART)-a type of research design-was developed explicitly for the purpose of building optimal adaptive interventions by providing answers to such questions. Despite increasing popularity, SMARTs remain relatively new to intervention scientists. This manuscript provides an introduction to adaptive interventions and SMARTs. We discuss SMART design considerations, including common primary and secondary aims. For illustration, we discuss the development of an adaptive intervention for optimizing weight loss among adult individuals who are overweight.

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

BACKGROUND

Cancer affects millions of people worldwide each year. Patients require sequences of treatment based on their response to previous treatments to combat cancer and fight metastases. Physicians provide treatment based on clinical characteristics, changing over time. Guidelines for these individualized sequences of treatments are known as dynamic treatment regimens (DTRs) where the initial treatment and subsequent modifications depend on the response to previous treatments, disease progression, and other patient characteristics or behaviors. To provide evidence-based DTRs, the Sequential Multiple Assignment Randomized Trial (SMART) has emerged over the past few decades.

PURPOSE

To examine and learn from past SMARTs investigating cancer treatment options, to discuss potential limitations preventing the widespread use of SMARTs in cancer research, and to describe courses of action to increase the implementation of SMARTs and collaboration between statisticians and clinicians.

CONCLUSION

There have been SMARTs investigating treatment questions in areas of cancer, but the novelty and perceived complexity has limited its use. By building bridges between statisticians and clinicians, clarifying research objectives, and furthering methods work, there should be an increase in SMARTs addressing relevant cancer treatment questions. Within any area of cancer, SMARTs develop DTRs that can guide treatment decisions over the disease history and improve patient outcomes.

Development of the Addiction Dimensions for Assessment and Personalised Treatment (ADAPT)

BACKGROUND

Convergent research reveals heterogeneity in substance use disorders (SUD). The Addiction Dimensions for Assessment and Personalised Treatment (ADAPT) is designed to help clinicians tailor therapies.

METHODS

Multicentre study in 21 SUD clinics in London, Birmingham (England) and Adelaide (Australia). 132 clinicians rated their caseload on a beta version with 16 ordinal indicators of addiction severity, health and social problem complexity, and recovery strengths constructs. In Birmingham, two in-treatment outcomes were recorded after 15-months: 28-day drug use (Treatment Outcome Profile; n=703) and Global Assessment of Functioning (GAF; DSM-IV Axis V; n=695). Following item-level screening (inter-rater reliability [IRR]; n=388), exploratory structural equation models (ESEM), latent profile analysis (LPA), and mixed-effects regression evaluated construct, concurrent and predictive validity characteristics, respectively.

RESULTS

2467 patients rated (majority opioid or stimulant dependent, enrolled in opioid medication assisted or psychological treatment). IRR-screening removed two items and ESEM models identified and recalibrated remaining indicators (root mean square error of approximation 0.066 [90% confidence interval 0.055-0.064]). Following minor re-specification and satisfactory measurement invariance evaluation, ADAPT factor scores discriminated patients by sample, addiction therapy and drug use. LPA identified three patient sub-types: Class 1 (moderate severity, moderate complexity, high strengths profile; 46.9%); Class 2 (low severity, low complexity, high strengths; 25.4%) and Class 3 (high severity, high complexity, low strengths; 27.7%). Class 2 had higher GAF (z=4.30). Class 3 predicted follow-up drug use (z=2.02) and lower GAF (z=3.51).

CONCLUSION

The ADAPT is a valid instrument for SUD treatment planning, clinical review and outcome evaluation. Scoring and application are discussed.

Dynamic Treatment Regimes

A dynamic treatment regime consists of a sequence of decision rules, one per stage of intervention, that dictate how to individualize treatments to patients based on evolving treatment and covariate history. These regimes are particularly useful for managing chronic disorders, and fit well into the larger paradigm of personalized medicine. They provide one way to operationalize a clinical decision support system. Statistics plays a key role in the construction of evidence-based dynamic treatment regimes - informing best study design as well as efficient estimation and valid inference. Due to the many novel methodological challenges it offers, this area has been growing in popularity among statisticians in recent years. In this article, we review the key developments in this exciting field of research. In particular, we discuss the sequential multiple assignment randomized trial designs, estimation techniques like Q-learning and marginal structural models, and several inference techniques designed to address the associated non-standard asymptotics. We reference software, whenever available. We also outline some important future directions.

Supportive monitoring and disease management through the internet: an internet-delivered intervention strategy for recurrent depression

Major depression is a highly prevalent, disabling disorder associated with loss of quality of life and large economic burden for the society. Depressive disorders often follow a chronic or recurrent course. The risk of relapses increases with each additional episode. The internet-deliverable intervention strategy SUMMIT (SUpportive Monitoring and Disease Management over the InTernet) for patients with recurrent depression has been developed with the main objectives to prolong symptom-free phases and to shorten symptom-loaden phases. This paper describes the study design of a six-sites, three-arm, randomized clinical trial intended to evaluate the efficacy of this novel strategy compared to treatment as usual (TAU). Two hundred thirty six patients who had been treated for their (at least) third depressive episode in one of the six participating psychiatric centers were randomized into one of three groups: 1) TAU plus a twelve-month SUMMIT program participation with personal support or 2) TAU plus a twelve-month SUMMIT program participation without personal support, or 3) TAU alone. Primary outcome of this study is defined as the number of "well weeks" over 24months after index treatment assessed by blind evaluators based on the Longitudinal Interval Follow-Up Evaluation. If efficacious, the low monetary and nonmonetary expenditures of this automated, yet individualized intervention may open new avenues for providing an acceptable, convenient, and affordable long-term disease management strategy to people with a chronic mental condition such as recurrent depression.

Dynamical models of biomarkers and clinical progression for personalized medicine: the HIV context

Mechanistic models, based on ordinary differential equation systems, can exhibit very good predictive abilities that will be useful to build treatment monitoring strategies. In this review, we present the potential and the limitations of such models for guiding treatment (monitoring and optimizing) in HIV-infected patients. In the context of antiretroviral therapy, several biological processes should be considered in addition to the interaction between viruses and the host immune system: the mechanisms of action of the drugs, their pharmacokinetics and pharmacodynamics, as well as the viral and host characteristics. Another important aspect to take into account is clinical progression, although its implementation in such modelling approaches is not easy. Finally, the control theory and the use of intrinsic properties of mechanistic models make them very relevant for dynamic treatment adaptation. Their implementation would nevertheless require their evaluation through clinical trials.

Interventions to address chronic disease and HIV: strategies to promote smoking cessation among HIV-infected individuals

Tobacco use, especially cigarette smoking, is higher than average in persons living with HIV/AIDS (PLWHA). The Public Health Service Clinical Practice Guideline for Treating Tobacco Use and Dependence states that, during every medical encounter, all smokers should be offered smoking cessation counseling, along with approved medications. The Guideline also recognizes PLWHA as a priority population, given the scarcity of research on effective cessation treatments in this group. The scant evidence suggests that conventional treatments, though worthwhile, are not as successful as might be hoped for. The reasons for this are not entirely clear, but may have to do with the complex array of medical and psychosocial factors that complicate their lives. Clinicians should consider re-treatment strategies for those patients who encounter difficulty when quitting smoking with conventional approaches, switching or augmenting treatments as needed to minimize adverse experiences, and to maximize tolerability, adherence, and cessation outcomes.

Innovative trends in the design of therapeutic trials in psychopharmacology and psychotherapy

The standard randomized controlled trial design is still based on the acute disease model. This is in sharp contrast with the fact that the patient is likely to have experienced other treatments before, that may actually modify clinical course and responsiveness. The current standard of therapeutic trial in psychiatry is represented by the large, multi-center, controlled randomized trial with broad inclusion criteria, and little attention to other factors such as the clinical history of patients and comorbidity. The heterogeneous features of these patients would then affect the outcome of the trial. Conflicting results among randomized controlled trials can represent a spectrum of outcomes, based on different patient groups, more than bias or random variability. If a treatment is tested by a series of small trials with inclusion criteria for specific characteristics (including treatment history, subgroups and comorbidity), we may have a better knowledge of its indications and contraindications. Further, there is increasing need of expanding the content of customary clinical information, by including evaluation of variables such as stress, lifestyle, well-being, illness behavior and psychological symptoms. These joint strategies would actually constitute a paradigm shift in psychopharmacology and psychotherapy research.

SMARTer discontinuation trial designs for developing an adaptive treatment strategy

OBJECTIVE

Developing evidenced-based practices for the management of childhood psychiatric disorders requires research studies that address how to treat children during both the acute phase of the disorder and beyond. Given the selection of a medication for acute treatment, discontinuation trials are used to evaluate the effects of treatment duration (e.g., time on medication) and/or maintenance strategies following successful acute-phase treatment. Recently, sequential multiple assignment randomized trials (SMART) have been proposed for use in informing sequences of critical clinical decisions such as those mentioned. The objective of this article is to illustrate how a SMART study is related to the standard discontinuation trial design, while addressing additional clinically important questions with similar trial resources.

METHOD

The recently completed Child/Adolescent Anxiety Multimodal Study (CAMS), a randomized trial that examined the relative efficacy of three acute-phase treatments for pediatric anxiety disorders, along with a next logical step, a standard discontinuation trial design, is used to clarify the ideas. This example is used to compare the discontinuation trial design relative to the SMART design.

RESULTS

We find that the standard discontinuation trial can be modified slightly using a SMART design to yield high-quality data that can be used to address a wider variety of questions in addition to the impact of treatment duration. We discuss how this innovative trial design is ultimately more efficient and less costly than the standard discontinuation trial, and may result in more representative comparisons between treatments.

CONCLUSIONS

Mental health researchers who are interested in addressing questions concerning the effects of continued treatment (for different durations) following successful acute-phase treatment should consider SMART designs in place of discontinuation trial designs in their research. SMART designs can be used to address these and other questions concerning individualized sequences of treatment, such as the choice of a rescue treatment in case of postacute phase relapse.

Initial management strategies for follicular lymphoma

Follicular lymphoma (FL) can vary markedly in its initial presentation, and no single standard approach for its initial management has been adopted. Available options for the initial management of FL include watchful waiting, radiation, single-agent rituximab and combination of rituximab and chemotherapy with strategies segregated for patients who have low and high tumor burden disease based on established criteria. However, marked debate occurs regarding the role of watchful waiting in the modern era for low tumor burden, asymptomatic patients, the optimal timing of rituximab, the selection of chemotherapy regimen to partner with rituximab in high tumor burden patients, and strategies for the management of relapsed disease. We provide an evidence-based discussion on these and other issues regarding the management of FL, and propose a mathematical modeling approach for addressing some of these questions.