scholarly journals Modeling survival distribution as a function of time to treatment discontinuation: A dynamic treatment regime approach

Biometrics ◽  
2018 ◽  
Vol 74 (3) ◽  
pp. 900-909 ◽  
Author(s):  
Shu Yang ◽  
Anastasios A. Tsiatis ◽  
Michael Blazing
Keyword(s):  
Treatment Regime ◽  
Treatment Discontinuation ◽  
Survival Distribution ◽  
Time To Treatment ◽  
Dynamic Treatment Regime ◽  
Dynamic Treatment

scholarly journals Power analysis in a SMART design: sample size estimation for determining the best embedded dynamic treatment regime

Biostatistics ◽  
2018 ◽  
Vol 21 (3) ◽  
pp. 432-448 ◽  
Author(s):  
William J Artman ◽  
Inbal Nahum-Shani ◽  
Tianshuang Wu ◽  
James R Mckay ◽  
Ashkan Ertefaie
Keyword(s):  
Power Analysis ◽  
Ad Hoc ◽  
Treatment Effectiveness ◽  
R Package ◽  
Treatment Regime ◽  
Size Estimation ◽  
Dynamic Treatment Regime ◽  
Dynamic Treatment ◽  
The Individual ◽  
Number Of Individuals

Summary Sequential, multiple assignment, randomized trial (SMART) designs have become increasingly popular in the field of precision medicine by providing a means for comparing more than two sequences of treatments tailored to the individual patient, i.e., dynamic treatment regime (DTR). The construction of evidence-based DTRs promises a replacement to ad hoc one-size-fits-all decisions pervasive in patient care. However, there are substantial statistical challenges in sizing SMART designs due to the correlation structure between the DTRs embedded in the design (EDTR). Since a primary goal of SMARTs is the construction of an optimal EDTR, investigators are interested in sizing SMARTs based on the ability to screen out EDTRs inferior to the optimal EDTR by a given amount which cannot be done using existing methods. In this article, we fill this gap by developing a rigorous power analysis framework that leverages the multiple comparisons with the best methodology. Our method employs Monte Carlo simulation to compute the number of individuals to enroll in an arbitrary SMART. We evaluate our method through extensive simulation studies. We illustrate our method by retrospectively computing the power in the Extending Treatment Effectiveness of Naltrexone (EXTEND) trial. An R package implementing our methodology is available to download from the Comprehensive R Archive Network.

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

2014 ◽  
Vol 11 (4) ◽  
pp. 408-417 ◽  
Author(s):  
Bibhas Chakraborty ◽  
Eric B Laber ◽  
Ying-Qi Zhao
Keyword(s):  
Treatment Regime ◽  
Data Driven ◽  
Dynamic Treatment Regime ◽  
Expected Performance ◽  
Dynamic Treatment

Model validation and selection for personalized medicine using dynamic-weighted ordinary least squares

2017 ◽  
Vol 26 (4) ◽  
pp. 1641-1653 ◽  
Author(s):  
Michael P Wallace ◽  
Erica EM Moodie ◽  
David A Stephens
Keyword(s):  
Least Squares ◽  
Ordinary Least Squares ◽  
Treatment Regime ◽  
Model Assessment ◽  
Double Robustness ◽  
Dynamic Treatment Regime ◽  
Dynamic Treatment ◽  
Optimal Dynamic ◽  
Treatment Alternatives ◽  
Least Squares Approach

Model assessment is a standard component of statistical analysis, but it has received relatively little attention within the dynamic treatment regime literature. In this paper, we focus on the dynamic-weighted ordinary least squares approach to optimal dynamic treatment regime estimation, introducing how its double-robustness property may be leveraged for model assessment, and how quasilikelihood may be used for model selection. These ideas are demonstrated through simulation studies, as well as through application to data from the sequenced treatment alternatives to relieve depression study.

scholarly journals Inverse reinforcement learning in contextual MDPs

2021 ◽  
Author(s):  
Stav Belogolovsky ◽  
Philip Korsunsky ◽  
Shie Mannor ◽  
Chen Tessler ◽  
Tom Zahavy
Keyword(s):  
Reinforcement Learning ◽  
Optimization Problem ◽  
Decision Processes ◽  
Inverse Reinforcement Learning ◽  
Convex Optimization Problem ◽  
Reward Function ◽  
Dynamic Treatment Regime ◽  
Markov Decision ◽  
Dynamic Treatment ◽  
Recorded Data

AbstractWe consider the task of Inverse Reinforcement Learning in Contextual Markov Decision Processes (MDPs). In this setting, contexts, which define the reward and transition kernel, are sampled from a distribution. In addition, although the reward is a function of the context, it is not provided to the agent. Instead, the agent observes demonstrations from an optimal policy. The goal is to learn the reward mapping, such that the agent will act optimally even when encountering previously unseen contexts, also known as zero-shot transfer. We formulate this problem as a non-differential convex optimization problem and propose a novel algorithm to compute its subgradients. Based on this scheme, we analyze several methods both theoretically, where we compare the sample complexity and scalability, and empirically. Most importantly, we show both theoretically and empirically that our algorithms perform zero-shot transfer (generalize to new and unseen contexts). Specifically, we present empirical experiments in a dynamic treatment regime, where the goal is to learn a reward function which explains the behavior of expert physicians based on recorded data of them treating patients diagnosed with sepsis.

A multicentre, randomized, naturalistic, open-label study between aripiprazole and standard of care in the management of community-treated schizophrenic patients Schizophrenia Trial of Aripiprazole: (STAR) study

2007 ◽  
Vol 22 (7) ◽  
pp. 433-443 ◽  
Author(s):  
Robert Kerwin ◽  
Bruno Millet ◽  
Erik Herman ◽  
Csaba M. Banki ◽  
Henrik Lublin ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Atypical Antipsychotic ◽  
Antipsychotic Medication ◽  
Clinical Global Impression ◽  
Standard Of Care ◽  
Treatment Discontinuation ◽  
Study Medication ◽  
Open Label ◽  
Time To Treatment

AbstractBackgroundNaturalistic effectiveness trials of atypical antipsychotics are needed to provide broader information on efficacy, safety, and tolerability in patients with schizophrenia treated in a community practice setting.Methodin this 26-week, open-label, multicentre study, patients with schizophrenia requiring a switch in antipsychotic medication because current medication was not well tolerated and/or clinical symptoms were not well controlled were randomized to receive aripiprazole or an atypical antipsychotic standard of care (SOC) treatment (i.e., olanzapine, quetiapine, or risperidone based on the investigator's judgment of the optimal treatment for the individual patient and the patient's prior response to antipsychotic medication). The primary objective was to compare the effectiveness of a 26-week treatment of aripiprazole versus SOC, as measured by the investigator Assessment Questionnaire (IAQ) total score at Week 26 last observation carried forward (LOCF) (primary endpoint), a validated measure that monitors relief or worsening of 10 key symptoms associated with the psychopathology of schizophrenia and side effects of antipsychotic treatment. Secondary objectives were to further assess effectiveness using the Clinical Global Impression – Global Improvement (CGI-I) and Clinical Global Impression – Severity of Illness scale, to assess time to treatment discontinuation, patient preference of medication, quality of life, and the tolerability of aripiprazole compared with SOC.ResultsAripiprazole treatment (n = 268) resulted in significantly better effectiveness than SOC treatment (n = 254; P < 0.001; Week 26 LOCF) as evidenced by the IAQ total score beginning at Week 4 (the first assessment point) and sustained through Week 26. A similar relationship was demonstrated among patients who completed the study (observed cases analysis); aripiprazole was associated with significantly better effectiveness at all time points with a greater differential effect from SOC over time. Patients treated with aripiprazole also demonstrated significantly greater improvements on the CGI-I scale (responder rate, P = 0.009 at Week 26 LOCF), as well as on quality of life (Quality of Life scale total score; P < 0.001 at Week 26). Furthermore, a significantly higher proportion of patients receiving aripiprazole rated their study medication as “much better” on the Preference of Medication Questionnaire (POM) scale than their pre-study medication compared with SOC patients (P < 0.001; Week 26). Time to treatment discontinuation and rates of discontinuation due to adverse events were similar in both treatment groups. The incidence of patients with one or more extrapyramidal symptom (e.g., akathisia, dystonia, parkinsonian events, and residual events) was higher in patients receiving aripiprazole compared with patients treated with SOC (13.5% vs. 5.6%); however, a higher proportion of patients in the SOC-treated group had clinically significant weight gain (21.2% vs. 7.3% for aripiprazole) and potentially clinically relevant elevated fasting levels of total cholesterol, low-density lipoprotein cholesterol, triglycerides, and serum prolactin compared with patients receiving aripiprazole.ConclusionsAripiprazole is an effective atypical antipsychotic for the treatment of schizophrenia, demonstrating better effectiveness than SOC agents used in this study in patients for whom a switch in antipsychotic medication was warranted.

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