scholarly journals Dynamic Treatment Regimen Estimation via Regression-Based Techniques: Introducing R Package DTRreg

2017 ◽  
Vol 80 (2) ◽  
Author(s):  
Michael P. Wallace ◽  
Erica E. M. Moodie ◽  
David A. Stephens
Keyword(s):  
Treatment Regimen ◽  
R Package ◽  
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.

Model assessment in dynamic treatment regimen estimation via double robustness

Biometrics ◽  
2016 ◽  
Vol 72 (3) ◽  
pp. 855-864 ◽  
Author(s):  
Michael P. Wallace ◽  
Erica E. M. Moodie ◽  
David A. Stephens
Keyword(s):  
Treatment Regimen ◽  
Model Assessment ◽  
Double Robustness ◽  
Dynamic Treatment

scholarly journals Individualized fluid administration for critically ill patients with sepsis with an interpretable dynamic treatment regimen model

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhongheng Zhang ◽  
Bin Zheng ◽  
Nan Liu
Keyword(s):  
Survival Time ◽  
Treatment Regimen ◽  
Fluid Management ◽  
Decision Rules ◽  
Quadratic Function ◽  
Computer Assisted ◽  
Research Database ◽  
Sequential Decision ◽  
Icu Admission ◽  
Dynamic Treatment

Abstract Fluid strategy is the key to the successful management of patients with sepsis. However, previous studies failed to consider individualized treatment strategy, and clinical trials typically included patients with sepsis as a homogeneous study population. We aimed to develop sequential decision rules for managing fluid intake in patients with sepsis by using the dynamic treatment regimen (DTR) model. A retrospective analysis of the eICU Collaborative Research Database comprising highly granular data collected from 335 units at 208 hospitals was performed. The DTR model used a backward induction algorithm to estimate the sequence of optimal rules. 22,868 patients who had sepsis according to the Acute Physiology and Chronic Health Evaluation (APACHE) IV diagnosis group were included. Optimal fluid management (liberal [> 40 ml/kg/d] versus restricted [< 40 ml/kg/d]) strategy were developed on the Day 1, 3 and 5 after ICU admission according to current states and treatment history. Important determinants of optimal fluid strategy included mean blood pressure, heart rate, previous urine output, previous fluid strategy, ICU type and mechanical ventilation. Different functional forms such as quadratic function and interaction terms were used at different stages. The proportion of subjects being inappropriately treated with liberal fluid strategy (i.e. those actually received liberal fluid strategy, but could have longer survival time if they received restricted fluid strategy) increased from day 1 to 5 (19.3% to 29.5%). The survival time could be significantly prolonged had all patients been treated with optimal fluid strategy (5.7 [2.0, 5.9] vs. 4.1 [2.0, 5.0] days; p < 0.001). With a large volume of sepsis data, we successfully computed out a sequence of dynamic fluid management strategy for sepsis patients over the first 5 days after ICU admission. The decision rules generated by the DTR model predicted a longer survival time compared to the true observed strategy, which sheds light for improving patient outcome with the aim from computer-assisted algorithm.

Dynamic treatment regimen analysis to determine which treatment sequence can prolong survival of NSCLC patients.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e19010-e19010
Author(s):  
Taro Koba ◽  
Fumio Imamura ◽  
Satoshi Morita ◽  
Masahide Mori ◽  
Kiyoshi Komuta ◽  
...  
Keyword(s):  
Overall Survival ◽  
Treatment Regimen ◽  
Performance Status ◽  
Treatment Sequence ◽  
Term Survival ◽  
Gefitinib Treatment ◽  
Dynamic Treatment ◽  
Nsclc Patients ◽  
Egfr Tki ◽  
Challenge Group

e19010^ Background: We often experience the re-challenge of EGFR-TKI on practice. However, it has not been reported which treatment sequence for EGFR-TKI re-challenge will contributes to long-term survival of NSCLC patients. Methods: We extracted information from retrospective cohort of advanced NSCLC patients with the following inclusion criteria: 1) Japanese patients who were diagnosed by October 2010 and treated with gefitinib after July 2002. 2) Performance status (PS) 0-2. 3) PR, CR, or long SD (6 months or more) by gefitinib. 4) Patients who had not received curative surgical operation or radiation therapy. The primary objective was to evaluate the effects of treatment histories on Overall Survival (OS). We also conducted a “Dynamic Treatment Regimen Analysis (DTRA)”. DTRA can be used to compare multiple treatment strategies/sequences in terms of time-to-event data like overall survival time. Results: A total of 335 NSCLC patient details were extracted. Sixty five patients experienced gefitinib re-challenge. There was a statistical difference in OS between gefitinib re-challenge group and non re-challenge group (median OS was 1272 days vs 774 days; p<0.001). We confirmed this result using DTRA, “Gefitnib-Singlet chemo-Gefitinib” treatment sequence extended survival most out of all treatment sequence. Conclusions: This study suggests that gefitinib re-challenge may have significant affects on OS in long survivors after responding gefitinib treatment. Clinical trial information: UMIN000006913. [Table: see text]

Doubly-robust dynamic treatment regimen estimation via weighted least squares

Biometrics ◽  
2015 ◽  
Vol 71 (3) ◽  
pp. 636-644 ◽  
Author(s):  
Michael P. Wallace ◽  
Erica E. M. Moodie
Keyword(s):  
Least Squares ◽  
Treatment Regimen ◽  
Weighted Least Squares ◽  
Dynamic Treatment ◽  
Doubly Robust

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

2019 ◽  
Vol 29 (7) ◽  
pp. 1891-1912
Author(s):  
Nicholas J Seewald ◽  
Kelley M Kidwell ◽  
Inbal Nahum-Shani ◽  
Tianshuang Wu ◽  
James R McKay ◽  
...  
Keyword(s):  
Randomized Trial ◽  
Sample Size ◽  
Treatment Regimen ◽  
Statistical Efficiency ◽  
Entire Study ◽  
Treatment Regimens ◽  
Longitudinal Outcome ◽  
Multiple Assignment ◽  
Dynamic Treatment ◽  
Inflation Factor

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

Sign in / Sign up

Export Citation Format

Share Document