Identifying a set that contains the best dynamic treatment regimes

Ertefaie, Ashkan; Wu, Tianshuang; Lynch, Kevin G.; Nahum‐Shani, Inbal

doi:10.1093/biostatistics/kxv025

Cited by 35 publications

(38 citation statements)

References 27 publications

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“…Furthermore, the efficiency of the proposed estimator can be improved using the corresponding doubly robust estimators, for example, those considered by Ertefaie et al. (). Following Ertefaie et al.…”

Section: Discussionmentioning

confidence: 99%

“…Following Ertefaie et al. () and Artman et al. (), the proposed sample size method can also be extended to detect the optimal treatment regime.…”

Section: Discussionmentioning

confidence: 99%

“…A special class of designs, called sequential multiple assignment randomized trial, or SMART designs (Lei, Nahum‐Shani, Lynch, Oslin, & Murphy, ; Murphy, ), are popularly used to study DTRs (Chakraborty & Moodie, ; Ertefaie, Wu, Lynch, & Nahum‐Shani, ). SMART designs involve randomizing patients to available treatment options at the initial stage, followed by rerandomizing at each subsequent stage of some or all of the patients to treatments available at that stage.…”

Section: Introductionmentioning

confidence: 99%

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SMARTp: A SMART design for nonsurgical treatments of chronic periodontitis with spatially referenced and nonrandomly missing skewed outcomes

et al. 2019

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This paper proposes dynamic treatment regimes (DTRs) as effective individualized treatment strategies for managing chronic periodontitis. The proposed DTRs are studied via SMARTp—a two‐stage sequential multiple assignment randomized trial (SMART) design. For this design, we propose a statistical analysis plan and a novel cluster‐level sample size calculation method that factors in typical features of periodontal responses such as non‐Gaussianity, spatial clustering, and nonrandom missingness. Here, each patient is viewed as a cluster, and a tooth within a patient's mouth is viewed as an individual unit inside the cluster, with the tooth‐level covariance structure described by a conditionally autoregressive structure. To accommodate possible skewness and tail behavior, the tooth‐level clinical attachment level (CAL) response is assumed to be skew‐t, with the nonrandomly missing structure captured via a shared parameter model corresponding to the missingness indicator. The proposed method considers mean comparison for the regimes with or without sharing an initial treatment, where the expected values and corresponding variances or covariance for the sample means of a pair of DTRs are derived by the inverse probability weighting and method of moments. Simulation studies are conducted to investigate the finite‐sample performance of the proposed sample size formulas under a variety of outcome‐generating scenarios. An R package SMARTp implementing our sample size formula is available at the Comprehensive R Archive Network for free download.

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Section: Discussionmentioning

confidence: 99%

“…Following Ertefaie et al. () and Artman et al. (), the proposed sample size method can also be extended to detect the optimal treatment regime.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SMARTp: A SMART design for nonsurgical treatments of chronic periodontitis with spatially referenced and nonrandomly missing skewed outcomes

et al. 2019

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“…This is because, by design, different second-stage treatments were offered to responders than to non-responders. Various methods can be employed to compare and select the best ATSs among the eight that are embedded in ExTENd (e.g., [28–30]). In previous analyses [18], ATS#1 was found to be the best among the eight embedded ATSs in terms of the probability of drinking during the second stage of treatment.…”

Section: The Sequential Multiple Assignment Randomized Trial (Smart)mentioning

confidence: 99%

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

Nahum‐Shani

Ertefaie

et al. 2017

Addiction

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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, stepped-care strategies for treating substance use disorders.

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“…Recently, Ertefaie et al . adopted the methodology in to estimate the best embedded treatment strategy using SMART data. In , Moodie et al .…”

Section: Introductionmentioning

confidence: 99%

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

2016

Statistics in Medicine

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In sequential multiple assignment randomized trials, longitudinal outcomes may be the most important outcomes of interest since this type of trials are 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, piece-wise linear, or more complicated forms. This provides more flexibility which 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 (CATIE).

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Identifying a set that contains the best dynamic treatment regimes

Cited by 35 publications

References 27 publications

SMARTp: A SMART design for nonsurgical treatments of chronic periodontitis with spatially referenced and nonrandomly missing skewed outcomes

SMARTp: A SMART design for nonsurgical treatments of chronic periodontitis with spatially referenced and nonrandomly missing skewed outcomes

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

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

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