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 Deep advantage learning for optimal dynamic treatment regime

2018 ◽  
Vol 2 (1) ◽  
pp. 80-88
Author(s):  
Shuhan Liang ◽  
Wenbin Lu ◽  
Rui Song
Keyword(s):  
Treatment Regime ◽  
Dynamic Treatment Regime ◽  
Dynamic Treatment ◽  
Optimal Dynamic

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 PDB65 Development of Optimal Dynamic Treatment Regime to Enhance Adherence of Patients with Type 2 Diabetes with Q-Learning Using Claims DATA

2020 ◽  
Vol 23 ◽  
pp. S517
Author(s):  
S. Maedera ◽  
T. Takeshima ◽  
Y. Yusa ◽  
K. Iwasaki ◽  
N. Shojima ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Claims Data ◽  
Treatment Regime ◽  
Q Learning ◽  
Dynamic Treatment Regime ◽  
Dynamic Treatment ◽  
Optimal Dynamic

The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

2019 ◽  
Author(s):  
Shinichi Nakagawa ◽  
Malgorzata Lagisz ◽  
Rose E O'Dea ◽  
Joanna Rutkowska ◽  
Yefeng Yang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Forest Plot ◽  
Point Estimates ◽  
Aggregate Effect ◽  
The Individual ◽  
Meta Analyses ◽  
Heterogeneous Effect ◽  
Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

EpiPen: An R Package to Investigate Two-Locus Epistatic Models

2014 ◽  
Vol 17 (4) ◽  
Author(s):  
Raymond K. Walters ◽  
Charles Laurin ◽  
Gitta H. Lubke
Keyword(s):  
Power Analysis ◽  
R Package ◽  
Simulation Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Epistatic Interactions ◽  
Model Interpretation ◽  
Genome Wide ◽  
Using Data ◽  
Power Analyses

Epistasis is a growing area of research in genome-wide studies, but the differences between alternative definitions of epistasis remain a source of confusion for many researchers. One problem is that models for epistasis are presented in a number of formats, some of which have difficult-to-interpret parameters. In addition, the relation between the different models is rarely explained. Existing software for testing epistatic interactions between single-nucleotide polymorphisms (SNPs) does not provide the flexibility to compare the available model parameterizations. For that reason we have developed an R package for investigating epistatic and penetrance models, EpiPen, to aid users who wish to easily compare, interpret, and utilize models for two-locus epistatic interactions. EpiPen facilitates research on SNP-SNP interactions by allowing the R user to easily convert between common parametric forms for two-locus interactions, generate data for simulation studies, and perform power analyses for the selected model with a continuous or dichotomous phenotype. The usefulness of the package for model interpretation and power analysis is illustrated using data on rheumatoid arthritis.

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