Subgroup identification from randomized clinical trial data

Abstract

We consider the problem of identifying a subgroup of patients who may have an enhanced treatment effect in a randomized clinical trial, and it is desirable that the subgroup be defined by a limited number of covariates. For this problem, the development of a standard, pre-determined strategy may help to avoid the well-known dangers of subgroup analysis. We present a method developed to find subgroups of enhanced treatment effect. This method, referred to as "Virtual Twins", involves predicting response probabilities for treatment and control "twins" for each subject. The difference in these probabilities is then used as the outcome in a classification or regression tree, which can potentially include any set of the covariates. We define a measure Q(Â) to be the difference between the treatment effect in estimated subgroup  and the marginal treatment effect. We present several methods developed to obtain an estimate of Q(Â), including estimation of Q(Â) using estimated probabilities in the original data, using estimated probabilities in newly simulated data, two cross-validation-based approaches and a bootstrap-based bias corrected approach. Results of a simulation study indicate that the Virtual Twins method noticeably outperforms logistic regression with forward selection when a true subgroup of enhanced treatment effect exists. Generally, large sample sizes or strong enhanced treatment effects are needed for subgroup estimation. As an illustration, we apply the proposed methods to data from a randomized clinical trial.

Personalised treatment assignment maximising expected benefit with smooth hinge loss

“…The reason why we compare our sHinge method with the ROWSi is that Xu et al (2015) showed by simulation that the ROWSi was superior over the method solving (9) with h k replaced by the hinge loss, which was proposed in Zhao et al (2012) except that LASSO penalty instead of L 2 penalty was used for variable selection. Xu et al (2015) also showed by simulation that ROWSi was superior over other four recently proposed methods, the interaction tree by Su, Tsai, Wang, Nickerson, and Li (2009), the virtual twins by Foster, Taylor, and Ruberg (2011), the logistic regression with LASSO penalty by Qian and Murphy (2011), and the FindIt by Imai and Ratkovic (2013).…”

Treatment evaluation for a data‐driven subgroup in adaptive enrichment designs of clinical trials

“…There is a great variety of choices for in the literature on adaptive signature designs, 9,10 subgroup selection, [25][26][27] and optimal treatment regimes. [28][29][30][31][32][33] As a simple example, can be based on a working regression model such as…”

“…Without appealing to a regression model, may be obtained from a specified parametric family 30,32,33 such as rectangles, or using nonparametric machine learning methods. 25,26,29,31 Except in Section 2.2.5, we do not assume that A = (Z 1 ) comes with an estimate of P(Y = 1|T, X).…”

Identification of predicted individual treatment effects in randomized clinical trials

“…There is growing recognition of the importance of individual heterogeneity in treatment response, which has led to a rapid growth of methodological development in the area (1,(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) …”