Estimation of survival distributions of treatment policies in two-stage randomization designs in clinical trials

Challenges and Opportunities in Interdisciplinary Research and Real-World Data for Treatment Sequences in Health Technology Assessments

With an ever-increasing number of treatment options, the assessment of treatment sequences has become crucial in health technology assessment (HTA). This review systematically explores the multifaceted challenges inherent in evaluating sequences, delving into their interplay and nuances that go beyond economic model structures. We synthesised a 'roadmap' of literature from key methodological studies, highlighting the evolution of recent advances and emerging research themes. These insights were compared against HTA guidelines to identify potential avenues for future research. Our findings reveal a spectrum of challenges in sequence evaluation, encompassing selecting appropriate decision-analytic modelling approaches and comparators, deriving appropriate clinical effectiveness evidence in the face of data scarcity, scrutinising effectiveness assumptions and statistical adjustments, considering treatment displacement, and optimising model computations. Integrating methodologies from diverse disciplines-statistics, epidemiology, causal inference, operational research and computer science-has demonstrated promise in addressing these challenges. An updated review of application studies is warranted to provide detailed insights into the extent and manner in which these methodologies have been implemented. Data scarcity on the effectiveness of treatment sequences emerged as a dominant concern, especially because treatment sequences are rarely compared in clinical trials. Real-world data (RWD) provide an alternative means for capturing evidence on effectiveness and future research should prioritise harnessing causal inference methods, particularly Target Trial Emulation, to evaluate treatment sequence effectiveness using RWD. This approach is also adaptable for analysing trials harbouring sequencing information and adjusting indirect comparisons when collating evidence from heterogeneous sources. Such investigative efforts could lend support to reviews of HTA recommendations and contribute to synthesising external control arms involving treatment sequences.

Behavioral cancer pain intervention dosing: results of a Sequential Multiple Assignment Randomized Trial

ABSTRACT

Behavioral pain management interventions are efficacious for reducing pain in patients with cancer. However, optimal dosing of behavioral pain interventions for pain reduction is unknown, and this hinders routine clinical use. A Sequential Multiple Assignment Randomized Trial (SMART) was used to evaluate whether varying doses of Pain Coping Skills Training (PCST) and response-based dose adaptation can improve pain management in women with breast cancer. Participants (N = 327) had stage I-IIIC breast cancer and a worst pain score of > 5/10. Pain severity (a priori primary outcome) was assessed before initial randomization (1:1 allocation) to PCST-Full (5 sessions) or PCST-Brief (1 session) and 5 to 8 weeks later. Responders ( > 30% pain reduction) were rerandomized to a maintenance dose or no dose and nonresponders (<30% pain reduction) to an increased or maintenance dose. Pain severity was assessed again 5 to 8 weeks later (assessment 3) and 6 months later (assessment 4). As hypothesized, PCST-Full resulted in greater mean percent pain reduction than PCST-Brief (M [SD] = -28.5% [39.6%] vs M [SD]= -14.8% [71.8%]; P = 0.041). At assessment 3 after second dosing, all intervention sequences evidenced pain reduction from assessment 1 with no differences between sequences. At assessment 4, all sequences evidenced pain reduction from assessment 1 with differences between sequences ( P = 0.027). Participants initially receiving PCST-Full had greater pain reduction at assessment 4 ( P = 0.056). Varying PCST doses led to pain reduction over time. Intervention sequences demonstrating the most durable decreases in pain reduction included PCST-Full. Pain Coping Skills Training with intervention adjustment based on response can produce sustainable pain reduction.

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.

Multitiered Systems of Support, Adaptive Interventions, and SMART Designs

This article introduces the special section on adaptive interventions and sequential multiple-assignment randomized trial (SMART) research designs. In addition to describing the two accompanying articles, we discuss features of adaptive interventions (AIs) and describe the use of SMART design to optimize AIs in the context of multitiered systems of support (MTSS) and integrated MTSS. AI is a treatment delivery model that explicitly specifies how information about individuals should be used to decide which treatment to provide in practice. Principles that apply to the design of AIs may help to more clearly operationalize MTSS-based programs, improve their implementation in school settings, and increase their efficacy when used according to evidence-based decision rules. A SMART is a research design for developing and optimizing MTSS-based programs. We provide a running example of a SMART design to optimize an MTSS-aligned AI that integrates academic and behavioral interventions.

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.

SMARTAR: an R package for designing and analyzing Sequential Multiple Assignment Randomized Trials

This article introduces an R package, SMARTAR (Sequential Multiple Assignment Randomized Trial with Adaptive Randomization), by which clinical investigators can design and analyze a sequential multiple assignment randomized trial (SMART) for comparing adaptive treatment strategies. Adaptive treatment strategies are commonly used in clinical practice to personalize healthcare in chronic disorder management. SMART is an efficient clinical design for selecting the best adaptive treatment strategy from a family of candidates. Although some R packages can help in adaptive treatment strategies research, they mainly focus on secondary data analysis for observational studies, instead of clinical trials. SMARTAR is the first R package provides functions that can support clinical investigators and data analysts at every step of the statistical work pipeline in clinical trial practice. In this article, we demonstrate how to use this package, using a real data example.

Synthesizing independent stagewise trials for optimal dynamic treatment regimes

Dynamic treatment regimes (DTRs) adaptively prescribe treatments based on patients' intermediate responses and evolving health status over multiple treatment stages. Data from sequential multiple assignment randomization trials (SMARTs) are recommended to be used for learning DTRs. However, due to re-randomization of the same patients over multiple treatment stages and a prolonged follow-up period, SMARTs are often difficult to implement and costly to manage, and patient adherence is always a concern in practice. To lessen such practical challenges, we propose an alternative approach to learn optimal DTRs by synthesizing independent trials over different stages. Specifically, at each stage, data from a single randomized trial along with patients' natural medical history and health status in previous stages are used. We use a backward learning method to estimate optimal treatment decisions at a particular stage, where patients' future optimal outcome increments are estimated using data observed from independent trials with future stages' information. Under some conditions, we show that the proposed method yields consistent estimation of the optimal DTRs and we obtain the same learning rates as those from SMARTs. We conduct simulation studies to demonstrate the advantage of the proposed method. Finally, we learn optimal DTRs for treating major depressive disorder (MDD) by stagewise synthesis of two randomized trials. We perform a validation study on independent subjects and show that the synthesized DTRs lead to the greatest MDD symptom reduction compared to alternative methods.

Joint modeling and multiple comparisons with the best of data from a SMART with survival outcomes

A dynamic treatment regimen (DTR) is a sequence of decision rules that can alter treatments or doses based on outcomes from prior treatment. In the case of two lines of treatment, a DTR specifies first-line treatment, and second-line treatment for responders and treatment for non-responders to the first-line treatment. A sequential, multiple assignment, randomized trial (SMART) is one such type of trial that has been designed to assess DTRs. The primary goal of our project is to identify the treatments, covariates, and their interactions result in the best overall survival rate. Many previously proposed methods to analyze data with survival outcomes from a SMART use inverse probability weighting and provide non-parametric estimation of survival rates, but no other information. Other methods have been proposed to identify and estimate the optimal DTR, but inference issues were seldom addressed. We apply a joint modeling approach to provide unbiased survival estimates as a mechanism to quantify baseline and time-varying covariate effects, treatment effects, and their interactions within regimens. The issue of multiple comparisons at specific time points is addressed using multiple comparisons with the best method.

Sensitivity analysis for subsequent treatments in confirmatory oncology clinical trials: A two-stage stochastic dynamic treatment regime approach

Subsequent treatments can result in a difficulty in interpretation of the overall survival results in confirmatory oncology clinical trials. To complement the intention-to-treat (ITT) analysis affected by subsequent treatment patterns unintentional in the trial protocol, several causal methods targeting the per-protocol effect have been proposed. When two or more types of subsequent treatments are allowed in the trial protocol, however, these methods cannot answer clinical questions such as how sensitive the ITT analysis result is to higher or lower proportions of each subsequent treatment allowed in the trial protocol than observed, and to what extent ITT analysis result is generalizable to subsequent treatment patterns other than observed one. To answer these clinical questions, we propose a sensitivity analysis method for subsequent treatments using the inverse probability of treatment weighting method for stochastic dynamic treatment regimes (DTRs). We formulate oncology clinical trials with subsequent treatments as two-stage designs in which initial treatments are randomized, but subsequent treatments are observational. In this formulation, we use stochastic DTRs to simulate specific proportions of each subsequent treatment and compare an initial experimental treatment with an initial control treatment under various proportions of each subsequent treatment. We applied our proposed method to a motivating randomized noninferiority trial for metastatic breast cancer. Simulation results are also reported to show the usefulness of the proposed method.

Cumulative incidence regression for dynamic treatment regimens

Recently dynamic treatment regimens (DTRs) have drawn considerable attention, as an effective tool for personalizing medicine. Sequential Multiple Assignment Randomized Trials (SMARTs) are often used to gather data for making inference on DTRs. In this article, we focus on regression analysis of DTRs from a two-stage SMART for competing risk outcomes based on cumulative incidence functions (CIFs). Even though there are extensive works on the regression problem for DTRs, no research has been done on modeling the CIF for SMART trials. We extend existing CIF regression models to handle covariate effects for DTRs. Asymptotic properties are established for our proposed estimators. The models can be implemented using existing software by an augmented-data approximation. We show the improvement provided by our proposed methods by simulation and illustrate its practical utility through an analysis of a SMART neuroblastoma study, where disease progression cannot be observed after death.

Videira P, Valadão G. Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs’ challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs’ AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.

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.

Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens

Dynamic treatment regimens (DTRs) are sequential treatment decisions tailored by patient's evolving features and intermediate outcomes at each treatment stage. Patient heterogeneity and the complexity and chronicity of many diseases call for learning optimal DTRs that can best tailor treatment according to each individual's time-varying characteristics (eg, intermediate response over time). In this paper, we propose a robust and efficient approach referred to as Augmented Outcome-weighted Learning (AOL) to identify optimal DTRs from sequential multiple assignment randomized trials. We improve previously proposed outcome-weighted learning to allow for negative weights. Furthermore, to reduce the variability of weights for numeric stability and improve estimation accuracy, in AOL, we propose a robust augmentation to the weights by making use of predicted pseudooutcomes from regression models for Q-functions. We show that AOL still yields Fisher-consistent DTRs even if the regression models are misspecified and that an appropriate choice of the augmentation guarantees smaller stochastic errors in value function estimation for AOL than the previous outcome-weighted learning. Finally, we establish the convergence rates for AOL. The comparative advantage of AOL over existing methods is demonstrated through extensive simulation studies and an application to a sequential multiple assignment randomized trial for major depressive disorder.

A framework for the design, conduct and interpretation of randomised controlled trials in the presence of treatment changes

BACKGROUND

When a randomised trial is subject to deviations from randomised treatment, analysis according to intention-to-treat does not estimate two important quantities: relative treatment efficacy and effectiveness in a setting different from that in the trial. Even in trials of a predominantly pragmatic nature, there may be numerous reasons to consider the extent, and impact on analysis, of such deviations from protocol. Simple methods such as per-protocol or as-treated analyses, which exclude or censor patients on the basis of their adherence, usually introduce selection and confounding biases. However, there exist appropriate causal estimation methods which seek to overcome these inherent biases, but these methods remain relatively unfamiliar and are rarely implemented in trials.

METHODS

This paper demonstrates when it may be of interest to look beyond intention-to-treat analysis for answers to alternative causal research questions through illustrative case studies. We seek to guide trialists on how to handle treatment changes in the design, conduct and planning the analysis of a trial; these changes may be planned or unplanned, and may or may not be permitted in the protocol. We highlight issues that must be considered at the trial planning stage relating to: the definition of nonadherence and the causal research question of interest, trial design, data collection, monitoring, statistical analysis and sample size.

RESULTS AND CONCLUSIONS

During trial planning, trialists should define their causal research questions of interest, anticipate the likely extent of treatment changes and use these to inform trial design, including the extent of data collection and data monitoring. A series of concise recommendations is presented to guide trialists when considering undertaking causal analyses.

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.

Optimizing delivery of a behavioral pain intervention in cancer patients using a sequential multiple assignment randomized trial SMART

BACKGROUND/AIMS

Pain is common in cancer patients and results in lower quality of life, depression, poor physical functioning, financial difficulty, and decreased survival time. Behavioral pain interventions are effective and nonpharmacologic. Traditional randomized controlled trials (RCT) test interventions of fixed time and dose, which poorly represent successive treatment decisions in clinical practice. We utilize a novel approach to conduct a RCT, the sequential multiple assignment randomized trial (SMART) design, to provide comparative evidence of: 1) response to differing initial doses of a pain coping skills training (PCST) intervention and 2) intervention dose sequences adjusted based on patient response. We also examine: 3) participant characteristics moderating intervention responses and 4) cost-effectiveness and practicality.

METHODS/DESIGN

Breast cancer patients (N=327) having pain (ratings≥5) are recruited and randomly assigned to: 1) PCST-Full or 2) PCST-Brief. PCST-Full consists of 5 PCST sessions. PCST-Brief consists of one 60-min PCST session. Five weeks post-randomization, participants re-rate their pain and are re-randomized, based on intervention response, to receive additional PCST sessions, maintenance calls, or no further intervention. Participants complete measures of pain intensity, interference and catastrophizing.

CONCLUSIONS

Novel RCT designs may provide information that can be used to optimize behavioral pain interventions to be adaptive, better meet patients' needs, reduce barriers, and match with clinical practice. This is one of the first trials to use a novel design to evaluate symptom management in cancer patients and in chronic illness; if successful, it could serve as a model for future work with a wide range of chronic illnesses.

Efficient estimation in two-stage randomized clinical trials using ranked sets

Clinical trials designed for survival probability estimation of different treatment policies for chronic diseases like cancer, leukemia, and schizophrenia usually need randomization of treatments in two stages. Since complete remission is rare for these diseases, initially an induction therapy is given for patient's remission. Further treatment, which is often an expensive maintenance therapy, is administered only for the patients with remission. If the maintenance therapy is so expensive that the cost of the trial inflates, only a simple random sample of patients will be treated with the expensive maintenance due to budget constraint. In this article, we have implemented a design using ranked sets instead of simple randomization in the second stage and obtained an unbiased estimator of the overall survival distribution for a particular treatment combination. Through simulation studies under different conditions, we have found that the design we developed based on ranked sets gives an unbiased estimate of the population survival probability which is more efficient than the estimate obtained by the usual design.

Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition Times

We analyze a dataset arising from a clinical trial involving multi-stage chemotherapy regimes for acute leukemia. The trial design was a 2 × 2 factorial for frontline therapies only. Motivated by the idea that subsequent salvage treatments affect survival time, we model therapy as a dynamic treatment regime (DTR), that is, an alternating sequence of adaptive treatments or other actions and transition times between disease states. These sequences may vary substantially between patients, depending on how the regime plays out. To evaluate the regimes, mean overall survival time is expressed as a weighted average of the means of all possible sums of successive transitions times. We assume a Bayesian nonparametric survival regression model for each transition time, with a dependent Dirichlet process prior and Gaussian process base measure (DDP-GP). Posterior simulation is implemented by Markov chain Monte Carlo (MCMC) sampling. We provide general guidelines for constructing a prior using empirical Bayes methods. The proposed approach is compared with inverse probability of treatment weighting, including a doubly robust augmented version of this approach, for both single-stage and multi-stage regimes with treatment assignment depending on baseline covariates. The simulations show that the proposed nonparametric Bayesian approach can substantially improve inference compared to existing methods. An R program for implementing the DDP-GP-based Bayesian nonparametric analysis is freely available at https://www.ma.utexas.edu/users/yxu/.

Comparison of adaptive treatment strategies based on longitudinal outcomes in sequential multiple assignment randomized trials

In sequential multiple assignment randomized trials, longitudinal outcomes may be the most important outcomes of interest because this type of trials is usually conducted in areas of chronic diseases or conditions. We propose to use a weighted generalized estimating equation (GEE) approach to analyzing data from such type of trials for comparing two adaptive treatment strategies based on generalized linear models. Although the randomization probabilities are known, we consider estimated weights in which the randomization probabilities are replaced by their empirical estimates and prove that the resulting weighted GEE estimator is more efficient than the estimators with true weights. The variance of the weighted GEE estimator is estimated by an empirical sandwich estimator. The time variable in the model can be linear, piecewise linear, or more complicated forms. This provides more flexibility that is important because, in the adaptive treatment setting, the treatment changes over time and, hence, a single linear trend over the whole period of study may not be practical. Simulation results show that the weighted GEE estimators of regression coefficients are consistent regardless of the specification of the correlation structure of the longitudinal outcomes. The weighted GEE method is then applied in analyzing data from the Clinical Antipsychotic Trials of Intervention Effectiveness. Copyright © 2016 John Wiley & Sons, Ltd.

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.

Daclatasvir plus simeprevir with or without ribavirin for the treatment of chronic hepatitis C virus genotype 1 infection

BACKGROUND & AIMS

We evaluated the combination of daclatasvir (pan-genotypic NS5A inhibitor) and simeprevir (NS3/4A protease inhibitor), with or without ribavirin, in hepatitis C virus genotype 1-infected patients.

METHODS

This phase II, open-label study enrolled treatment-naive patients or prior null responders with genotype 1b (n=147) or 1a (n=21) infection. Genotype 1b-infected patients were randomized 1:1 to receive daclatasvir 30mg plus simeprevir 150mg once daily with or without ribavirin; those who completed the initial 12-week treatment were re-randomized 1:1 to stop treatment or continue treatment through to week 24. Genotype 1a-infected patients received daclatasvir plus simeprevir with ribavirin for 24weeks. The primary endpoint was the proportion of patients with sustained virologic response at posttreatment week 12 (SVR12).

RESULTS

For genotype 1b, 84.9% (45/53) and 74.5% (38/51) of treatment-naive patients and 69.6% (16/23) and 95.0% (19/20) of prior null responders to peginterferon and ribavirin achieved SVR12 with daclatasvir plus simeprevir alone and with ribavirin, respectively. Treatment duration did not have a well-defined impact on response. For genotype 1a, daclatasvir plus simeprevir with ribavirin provided a 66.7% (8/12) response rate in treatment-naive patients and was not effective in prior null responders. Data suggest that baseline resistance polymorphisms influenced SVR12 rates. Daclatasvir plus simeprevir was well tolerated with or without ribavirin with low incidences of serious adverse events and adverse events leading to discontinuation.

CONCLUSIONS

Daclatasvir plus simeprevir, with or without ribavirin, was effective with a 12- or 24-week duration in genotype 1b-infected patients and was well tolerated. ClinicalTrials.gov identifier: NCT01628692.

Optimization of multi-stage dynamic treatment regimes utilizing accumulated data

In medical therapies involving multiple stages, a physician's choice of a subject's treatment at each stage depends on the subject's history of previous treatments and outcomes. The sequence of decisions is known as a dynamic treatment regime or treatment policy. We consider dynamic treatment regimes in settings where each subject's final outcome can be defined as the sum of longitudinally observed values, each corresponding to a stage of the regime. Q-learning, which is a backward induction method, is used to first optimize the last stage treatment then sequentially optimize each previous stage treatment until the first stage treatment is optimized. During this process, model-based expectations of outcomes of late stages are used in the optimization of earlier stages. When the outcome models are misspecified, bias can accumulate from stage to stage and become severe, especially when the number of treatment stages is large. We demonstrate that a modification of standard Q-learning can help reduce the accumulated bias. We provide a computational algorithm, estimators, and closed-form variance formulas. Simulation studies show that the modified Q-learning method has a higher probability of identifying the optimal treatment regime even in settings with misspecified models for outcomes. It is applied to identify optimal treatment regimes in a study for advanced prostate cancer and to estimate and compare the final mean rewards of all the possible discrete two-stage treatment sequences.

Comparing treatment policies with assistance from the structural nested mean model

Treatment policies, also known as dynamic treatment regimes, are sequences of decision rules that link the observed patient history with treatment recommendations. Multiple, plausible, treatment policies are frequently constructed by researchers using expert opinion, theories, and reviews of the literature. Often these different policies represent competing approaches to managing an illness. Here, we develop an "assisted estimator" that can be used to compare the mean outcome of competing treatment policies. The term "assisted" refers to the fact estimators from the Structural Nested Mean Model, a parametric model for the causal effect of treatment at each time point, are used in the process of estimating the mean outcome. This work is motivated by our work on comparing the mean outcome of two competing treatment policies using data from the ExTENd study in alcohol dependence.

Design Issues in Randomized Clinical Trials of Maintenance Therapies

A potential therapeutic strategy for patients who respond (or have stable disease) on a fixed-duration induction therapy is to receive maintenance therapy, typically given for a prolonged period of time. To enable patients and clinicians to make informed treatment decisions, the designs of phase III randomized clinical trials (RCTs) assessing maintenance strategies need to be such that their results will provide clear assessment of the relevant risks and benefits of these strategies. We review the key aspects of maintenance RCT designs. Important design considerations include choice of first-line and second-line therapies, minimizing between-arm differences in follow-up schedules, and choice of the primary endpoint. In order to change clinical practice, RCTs should be designed to accurately isolate and quantify the clinical benefit of maintenance as compared with the standard approach of fixed-duration induction followed by the second-line treatment at progression. To accomplish this, RCTs need to utilize an overall survival (or quality of life) endpoint or, in settings where this is not feasible, endpoints that incorporate the effects of the subsequent line of therapy (eg, time from randomization to second progression or death). Toxicity and symptom information over both the study treatment (maintenance) and the second-line treatment should also be collected and reported.

Innovative designs of point-of-care comparative effectiveness trials

One of the provisions of the health care reform legislation in 2010 was for funding pragmatic clinical trials or large observational studies for comparing the effectiveness of different approved medical treatments, involving broadly representative patient populations. After reviewing pragmatic clinical trials and the issues and challenges that have made them just a small fraction of comparative effectiveness research (CER), we focus on a recent development that uses point-of-care (POC) clinical trials to address the issue of "knowledge-action gap" in pragmatic CER trials. We give illustrative examples of POC-CER trials and describe a trial that we are currently planning to compare the effectiveness of newly approved oral anticoagulants. We also develop novel stage-wise designs of information-rich POC-CER trials under competitive budget constraints, by using recent advances in adaptive designs and other statistical methodologies.

Bayesian Dose-Finding in Two Treatment Cycles Based on the Joint Utility of Efficacy and Toxicity

A phase I/II clinical trial design is proposed for adaptively and dynamically optimizing each patient's dose in each of two cycles of therapy based on the joint binary efficacy and toxicity outcomes in each cycle. A dose-outcome model is assumed that includes a Bayesian hierarchical latent variable structure to induce association among the outcomes and also facilitate posterior computation. Doses are chosen in each cycle based on posteriors of a model-based objective function, similar to a reinforcement learning or Q-learning function, defined in terms of numerical utilities of the joint outcomes in each cycle. For each patient, the procedure outputs a sequence of two actions, one for each cycle, with each action being the decision to either treat the patient at a chosen dose or not to treat. The cycle 2 action depends on the individual patient's cycle 1 dose and outcomes. In addition, decisions are based on posterior inference using other patients' data, and therefore the proposed method is adaptive both within and between patients. A simulation study of the method is presented, including comparison to two-cycle extensions of the conventional 3+3 algorithm, continual reassessment method, and a Bayesian model-based design, and evaluation of robustness.

On Sparse representation for Optimal Individualized Treatment Selection with Penalized Outcome Weighted Learning

As a new strategy for treatment which takes individual heterogeneity into consideration, personalized medicine is of growing interest. Discovering individualized treatment rules (ITRs) for patients who have heterogeneous responses to treatment is one of the important areas in developing personalized medicine. As more and more information per individual is being collected in clinical studies and not all of the information is relevant for treatment discovery, variable selection becomes increasingly important in discovering individualized treatment rules. In this article, we develop a variable selection method based on penalized outcome weighted learning through which an optimal treatment rule is considered as a classification problem where each subject is weighted proportional to his or her clinical outcome. We show that the resulting estimator of the treatment rule is consistent and establish variable selection consistency and the asymptotic distribution of the estimators. The performance of the proposed approach is demonstrated via simulation studies and an analysis of chronic depression data.

Use of personalized Dynamic Treatment Regimes (DTRs) and Sequential Multiple Assignment Randomized Trials (SMARTs) in mental health studies

Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each point where a clinical decision is made based on each patient’s time-varying characteristics and intermediate outcomes observed at earlier points in time. The complexity, patient heterogeneity, and chronicity of mental disorders call for learning optimal DTRs to dynamically adapt treatment to an individual’s response over time. The Sequential Multiple Assignment Randomized Trial (SMARTs) design allows for estimating causal effects of DTRs. Modern statistical tools have been developed to optimize DTRs based on personalized variables and intermediate outcomes using rich data collected from SMARTs; these statistical methods can also be used to recommend tailoring variables for designing future SMART studies. This paper introduces DTRs and SMARTs using two examples in mental health studies, discusses two machine learning methods for estimating optimal DTR from SMARTs data, and demonstrates the performance of the statistical methods using simulated data.

Penalized Q-Learning for Dynamic Treatment Regimens

A dynamic treatment regimen incorporates both accrued information and long-term effects of treatment from specially designed clinical trials. As these trials become more and more popular in conjunction with longitudinal data from clinical studies, the development of statistical inference for optimal dynamic treatment regimens is a high priority. In this paper, we propose a new machine learning framework called penalized Q-learning, under which valid statistical inference is established. We also propose a new statistical procedure: individual selection and corresponding methods for incorporating individual selection within penalized Q-learning. Extensive numerical studies are presented which compare the proposed methods with existing methods, under a variety of scenarios, and demonstrate that the proposed approach is both inferentially and computationally superior. It is illustrated with a depression clinical trial study.

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.

Estimation of treatment policies based on functional predictors

Biosignatures such as brain scans, mass spectrometry, or gene expression profiles might one day be used to guide treatment selection and improve outcomes. This article develops a way of estimating optimal treatment policies based on data from randomized clinical trials by interpreting patient biosignatures as functional predictors. A flexible functional regression model is used to represent the treatment effect and construct the estimated policy. The effectiveness of the estimated policy is assessed by furnishing prediction intervals for the mean outcome when all patients follow the policy. The validity of these prediction intervals is established under mild regularity conditions on the functional regression model. The performance of the proposed approach is evaluated in numerical studies.

Pattern-mixture-type Estimation and Testing of Neuroblastoma Treatment Regimes

Sequentially randomized designs are commonly used in biomedical research, particularly in clinical trials, to assess and compare the effects of different treatment regimes. In such designs, eligible patients are first randomized to one of the initial therapies, then patients with some intermediate response (e.g. without progressive diseases) are randomized to one of the maintenance therapies. The goal is to evaluate dynamic treatment regimes consisting of an initial therapy, the intermediate response, and a maintenance therapy. In this article, we demonstrate the use of pattern-mixture model (commonly used for analyzing missing data) for estimating the effects of treatment regimes based on familiar survival analysis techniques such as Nelson-Aalen and parametric models. Moreover, we demonstrate how to use estimates from pattern-mixture models to test for the differences across treatment regimes in a weighted log-rank setting. We investigate the properties of the proposed estimators and test in a Monte Carlo simulation study. Finally we demonstrate the methods using the long-term survival data from the high risk neuroblastoma study.

Inference about the expected performance of a data-driven dynamic treatment regime

BACKGROUND

A dynamic treatment regime (DTR) comprises a sequence of decision rules, one per stage of intervention, that recommends how to individualize treatment to patients based on evolving treatment and covariate history. These regimes are useful for managing chronic disorders, and fit into the larger paradigm of personalized medicine. The Value of a DTR is the expected outcome when the DTR is used to assign treatments to a population of interest.

PURPOSE

The Value of a data-driven DTR, estimated using data from a Sequential Multiple Assignment Randomized Trial, is both a data-dependent parameter and a non-smooth function of the underlying generative distribution. These features introduce additional variability that is not accounted for by standard methods for conducting statistical inference, for example, the bootstrap or normal approximations, if applied without adjustment. Our purpose is to provide a feasible method for constructing valid confidence intervals (CIs) for this quantity of practical interest.

METHODS

We propose a conceptually simple and computationally feasible method for constructing valid CIs for the Value of an estimated DTR based on subsampling. The method is self-tuning by virtue of an approach called the double bootstrap. We demonstrate the proposed method using a series of simulated experiments.

RESULTS

The proposed method offers considerable improvement in terms of coverage rates of the CIs over the standard bootstrap approach.

LIMITATIONS

In this article, we have restricted our attention to Q-learning for estimating the optimal DTR. However, other methods can be employed for this purpose; to keep the discussion focused, we have not explored these alternatives.

CONCLUSION

Subsampling-based CIs provide much better performance compared to standard bootstrap for the Value of an estimated DTR.

Introduction to dynamic treatment strategies and sequential multiple assignment randomization

BACKGROUND

In June 2013, a 1-day workshop on Dynamic Treatment Strategies (DTSs) and Sequential Multiple Assignment Randomized Trials (SMARTs) was held at the University of Pennsylvania in Philadelphia, Pennsylvania. These two linked topics have generated a great deal of interest as researchers have recognized the importance of comparing entire strategies for managing chronic disease. A number of articles emerged from that workshop.

PURPOSE

The purpose of this survey of the DTS/SMART methodology (which is taken from the introductory talk in the workshop) is to provide the reader the collected articles presented in this volume with sufficient background to appreciate the more detailed discussions in the articles.

METHODS

The way that the DTS arises naturally in clinical practice is described, along with its connection to the well-known difficulties of interpreting the analysis by intention-to-treat. The SMART methodology for comparing DTS is described, and the basics of estimation and inference presented.

RESULTS

The DTS/SMART methodology can be a flexible and practical way to optimize ongoing clinical decision making, providing evidence (based on randomization) for comparative effectiveness.

LIMITATIONS

The DTS/SMART methodology is not a solution for unstandardized study protocols.

CONCLUSIONS

The DTS/SMART methodology has growing relevance to comparative effectiveness research and the needs of the learning healthcare system.

Inference for the median residual life function in sequential multiple assignment randomized trials

In survival analysis, median residual lifetime is often used as a summary measure to assess treatment effectiveness; it is not clear, however, how such a quantity could be estimated for a given dynamic treatment regimen using data from sequential randomized clinical trials. We propose a method to estimate a dynamic treatment regimen-specific median residual life (MERL) function from sequential multiple assignment randomized trials. We present the MERL estimator, which is based on inverse probability weighting, as well as, two variance estimates for the MERL estimator. One variance estimate follows from Lunceford, Davidian and Tsiatis' 2002 survival function-based variance estimate and the other uses the sandwich estimator. The MERL estimator is evaluated, and its two variance estimates are compared through simulation studies, showing that the estimator and both variance estimates produce approximately unbiased results in large samples. To demonstrate our methods, the estimator has been applied to data from a sequentially randomized leukemia clinical trial.

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.

A global logrank test for adaptive treatment strategies based on observational studies

In studying adaptive treatment strategies, a natural question that is of paramount interest is whether there is any significant difference among all possible treatment strategies. When the outcome variable of interest is time-to-event, we propose an inverse probability weighted logrank test for testing the equivalence of a fixed set of pre-specified adaptive treatment strategies based on data from an observational study. The weights take into account both the possible selection bias in an observational study and the fact that the same subject may be consistent with more than one treatment strategy. The asymptotic distribution of the weighted logrank statistic under the null hypothesis is obtained. We show that, in an observational study where the treatment selection probabilities need to be estimated, the estimation of these probabilities does not have an effect on the asymptotic distribution of the weighted logrank statistic, as long as the estimation of the parameters in the models for these probabilities is n-consistent. Finite sample performance of the test is assessed via a simulation study. We also show in the simulation that the test can be pretty robust to misspecification of the models for the probabilities of treatment selection. The method is applied to analyze data on antidepressant adherence time from an observational database maintained at the Department of Veterans Affairs' Serious Mental Illness Treatment Research and Evaluation Center.

A retrospective analysis of 335 Japanese lung cancer patients who responded to initial gefitinib treatment

BACKGROUND

Gefitinib treatment results in considerably better progression-free survival compared with that of platinum doublets in the first line treatment of nonsmall-cell lung cancer (NSCLC) carrying an activating epidermal growth factor receptor (EGFR) mutation. Some patients who respond to gefitinib have an overall survival (OS) of more than 5 years, whereas other initial responders do less well. Although there has been considerable effort made to elucidate the mechanisms of acquired resistance, there have only been a few studies that addressed the effect of clinical backgrounds and treatment histories on the survival of the patients who had responded to an EGFR-tyrosine kinase inhibitor (TKI). In this study, we especially focused on the clinical benefit of EGFR-TKI administration after progression.

PATIENTS AND METHODS

We retrospectively analyzed consecutive patients with advanced NSCLC who were diagnosed before October 2010, treated with gefitinib after July 2002, and responded to it. The primary objective of this study was to evaluate how clinical backgrounds and treatment histories influence survival of the patients who respond to gefitinib. The secondary objectives were to evaluate the safety of long-term gefitinib use and to establish the optimal treatment sequence using a dynamic treatment regimen analysis (DTRA).

RESULTS

A total of 335 patients were recruited. Twenty-eight (8.4%) patients survived more than 5 years. Sixty-five and 93 patients received gefitinib as rechallenge and beyond progressive disease (BPD), respectively. A statistically significant difference in OS was observed between the patients who underwent gefitinib rechallenge and those who did not rechallenge (median: 1272 days vs. 774 days; p < 0.001), a result supported by a DTRA. Patients treated with gefitinib BPD also showed a tendency of longer survival.

CONCLUSIONS

Gefitinib rechallenge and BPD played a central role in long term survival of the patients who initially responded to gefitinib.

Evaluating and adjusting for premature censoring of progression-free survival

The intent-to-treat principle, grouping subjects as they were randomized and following all subjects to the endpoint or the end of study, allows valid statistical comparisons. Progression-free survival (PFS) has been used as a decision-making endpoint in oncology. It can be difficult to have a meaningful intent-to-treat analysis of PFS as some studies have extensive loss to follow-up for PFS. In the analysis, subjects lost to follow-up for PFS have their PFS times censored, with the censoring treated as noninformative. We use remaining overall survival to investigate whether premature censoring for PFS is informative and the potential bias in treating such censoring as noninformative.

Modeling clinical endpoints as a function of time of switch to second-line ART with incomplete data on switching times

Modeling clinical endpoints as a function of change in antiretroviral therapy (ART) attempts to answer one simple but very challenging question: was the change in ART beneficial or not? We conceive a similar scientific question of interest in the current manuscript except that we are interested in modeling the time of ART regimen change rather than a comparison of two or more ART regimens. The answer to this scientific riddle is unknown and has been difficult to address clinically. Naturally, ART regimen change is left to a participant and his or her provider and so the date of change depends on participant characteristics. There exists a vast literature on how to address potential confounding and those techniques are vital to the success of the method here. A more substantial challenge is devising a systematic modeling strategy to overcome the missing time of regimen change for those participants who do not switch to second-line ART within the study period even after failing the initial ART. In this article, we adopt and apply a statistical method that was originally proposed for modeling infusion trial data, where infusion length may be informatively censored, and argue that the same strategy may be employed here. Our application of this method to therapeutic HIV/AIDS studies is new and interesting. Using data from the AIDS Clinical Trials Group (ACTG) Study A5095, we model immunological endpoints as a polynomial function of a participant's switching time to second-line ART for 182 participants who already failed the initial ART. In our analysis, we find that participants who switch early have somewhat better sustained suppression of HIV-1 RNA after virological failure than those who switch later. However, we also found that participants who switched very late, possibly censored due to the end of the study, had good HIV-1 RNA suppression, on average. We believe our scientific conclusions contribute to the relevant HIV literature and hope that the basic modeling strategy outlined here would be useful to others contemplating similar analyses with partially missing treatment length data.

Cumulative Hazard Ratio Estimation for Treatment Regimes in Sequentially Randomized Clinical Trials

The proportional hazards model is widely used in survival analysis to allow adjustment for baseline covariates. The proportional hazard assumption may not be valid for treatment regimes that depend on intermediate responses to prior treatments received, and it is not clear how such a model can be adapted to clinical trials employing more than one randomization. Besides, since treatment is modified post-baseline, the hazards are unlikely to be proportional across treatment regimes. Although Lokhnygina and Helterbrand (Biometrics 63: 422-428, 2007) introduced the Cox regression method for two-stage randomization designs, their method can only be applied to test the equality of two treatment regimes that share the same maintenance therapy. Moreover, their method does not allow auxiliary variables to be included in the model nor does it account for treatment effects that are not constant over time. In this article, we propose a model that assumes proportionality across covariates within each treatment regime but not across treatment regimes. Comparisons among treatment regimes are performed by testing the log ratio of the estimated cumulative hazards. The ratio of the cumulative hazard across treatment regimes is estimated using a weighted Breslow-type statistic. A simulation study was conducted to evaluate the performance of the estimators and proposed tests.

Weighted log-rank statistic to compare shared-path adaptive treatment strategies

Adaptive treatment strategies (ATSs) more closely mimic the reality of a physician's prescription process where the physician prescribes a medication to his/her patient, and based on that patient's response to the medication, modifies the treatment. Two-stage randomization designs, more generally, sequential multiple assignment randomization trial designs, are useful to assess ATSs where the interest is in comparing the entire sequence of treatments, including the patient's intermediate response. In this paper, we introduce the notion of shared-path and separate-path ATSs and propose a weighted log-rank statistic to compare overall survival distributions of multiple two-stage ATSs, some of which may be shared-path. Large sample properties of the statistic are derived and the type I error rate and power of the test are compared with the standard log-rank test through simulation.

Experimental design and primary data analysis methods for comparing adaptive interventions

In recent years, research in the area of intervention development has been shifting from the traditional fixed-intervention approach to adaptive interventions, which allow greater individualization and adaptation of intervention options (i.e., intervention type and/or dosage) over time. Adaptive interventions are operationalized via a sequence of decision rules that specify how intervention options should be adapted to an individual's characteristics and changing needs, with the general aim to optimize the long-term effectiveness of the intervention. Here, we review adaptive interventions, discussing the potential contribution of this concept to research in the behavioral and social sciences. We then propose the sequential multiple assignment randomized trial (SMART), an experimental design useful for addressing research questions that inform the construction of high-quality adaptive interventions. To clarify the SMART approach and its advantages, we compare SMART with other experimental approaches. We also provide methods for analyzing data from SMART to address primary research questions that inform the construction of a high-quality adaptive intervention.

Evaluating Joint Effects of Induction-Salvage Treatment Regimes on Overall Survival in Acute Leukemia

Typical oncology practice often includes not only an initial, frontline treatment, but also subsequent treatments given if the initial treatment fails. The physician chooses a treatment at each stage based on the patient's baseline covariates and history of previous treatments and outcomes. Such sequentially adaptive medical decision-making processes are known as dynamic treatment regimes, treatment policies, or multi-stage adaptive treatment strategies. Conventional analyses in terms of frontline treatments that ignore subsequent treatments may be misleading, because they actually are an evaluation of more than front-line treatment effects on outcome. We are motivated by data from a randomized trial of four combination chemotherapies given as frontline treatments to patients with acute leukemia. Most patients in the trial also received a second-line treatment, chosen adaptively and subjectively rather than by randomization, either because the initial treatment was ineffective or the patient's cancer later recurred. We evaluate effects on overall survival time of the 16 two-stage strategies that actually were used. Our methods include a likelihood-based regression approach in which the transition times of all possible multi-stage outcome paths are modeled, and estimating equations with inverse probability of treatment weighting to correct for bias. While the two approaches give different numerical estimates of mean survival time, they lead to the same substantive conclusions when comparing the two-stage regimes.

Evaluating the Effect of Early Versus Late ARV Regimen Change if Failure on an Initial Regimen: Results From the AIDS Clinical Trials Group Study A5095

The current goal of initial antiretroviral (ARV) therapy is suppression of plasma human immunodeficiency virus (HIV)-1 RNA levels to below 200 copies per milliliter. A proportion of HIV-infected patients who initiate antiretroviral therapy in clinical practice or antiretroviral clinical trials either fail to suppress HIV-1 RNA or have HIV-1 RNA levels rebound on therapy. Frequently, these patients have sustained CD4 cell counts responses and limited or no clinical symptoms and, therefore, have potentially limited indications for altering therapy which they may be tolerating well despite increased viral replication. On the other hand, increased viral replication on therapy leads to selection of resistance mutations to the antiretroviral agents comprising their therapy and potentially cross-resistance to other agents in the same class decreasing the likelihood of response to subsequent antiretroviral therapy. The optimal time to switch antiretroviral therapy to ensure sustained virologic suppression and prevent clinical events in patients who have rebound in their HIV-1 RNA, yet are stable, is not known. Randomized clinical trials to compare early versus delayed switching have been difficult to design and more difficult to enroll. In some clinical trials, such as the AIDS Clinical Trials Group (ACTG) Study A5095, patients randomized to initial antiretroviral treatment combinations, who fail to suppress HIV-1 RNA or have a rebound of HIV-1 RNA on therapy are allowed to switch from the initial ARV regimen to a new regimen, based on clinician and patient decisions. We delineate a statistical framework to estimate the effect of early versus late regimen change using data from ACTG A5095 in the context of two-stage designs.In causal inference, a large class of doubly robust estimators are derived through semiparametric theory with applications to missing data problems. This class of estimators is motivated through geometric arguments and relies on large samples for good performance. By now, several authors have noted that a doubly robust estimator may be suboptimal when the outcome model is misspecified even if it is semiparametric efficient when the outcome regression model is correctly specified. Through auxiliary variables, two-stage designs, and within the contextual backdrop of our scientific problem and clinical study, we propose improved doubly robust, locally efficient estimators of a population mean and average causal effect for early versus delayed switching to second-line ARV treatment regimens. Our analysis of the ACTG A5095 data further demonstrates how methods that use auxiliary variables can improve over methods that ignore them. Using the methods developed here, we conclude that patients who switch within 8 weeks of virologic failure have better clinical outcomes, on average, than patients who delay switching to a new second-line ARV regimen after failing on the initial regimen. Ordinary statistical methods fail to find such differences. This article has online supplementary material.

Targeted maximum likelihood estimation for dynamic treatment regimes in sequentially randomized controlled trials

Sequential Randomized Controlled Trials (SRCTs) are rapidly becoming essential tools in the search for optimized treatment regimes in ongoing treatment settings. Analyzing data for multiple time-point treatments with a view toward optimal treatment regimes is of interest in many types of afflictions: HIV infection, Attention Deficit Hyperactivity Disorder in children, leukemia, prostate cancer, renal failure, and many others. Methods for analyzing data from SRCTs exist but they are either inefficient or suffer from the drawbacks of estimating equation methodology. We describe an estimation procedure, targeted maximum likelihood estimation (TMLE), which has been fully developed and implemented in point treatment settings, including time to event outcomes, binary outcomes and continuous outcomes. Here we develop and implement TMLE in the SRCT setting. As in the former settings, the TMLE procedure is targeted toward a pre-specified parameter of the distribution of the observed data, and thereby achieves important bias reduction in estimation of that parameter. As with the so-called Augmented Inverse Probability of Censoring Weight (A-IPCW) estimator, TMLE is double-robust and locally efficient. We report simulation results corresponding to two data-generating distributions from a longitudinal data structure.