Estimation and visualization of heterogeneous treatment effects for multiple outcomes.

We consider two-arm comparison in clinical trials. The objective is to identify a population with characteristics that make the treatment effective. Such a population is called a subgroup. This identification can be made by estimating the treatment effect and identifying the interactions between treatments and covariates. For a single outcome, there are several ways available to identify the subgroups. There are also multiple outcomes, but they are difficult to interpret and cannot be applied to outcomes other than continuous values. In this paper, we thus propose a new method that allows for a straightforward interpretation of subgroups and deals with both continuous and binary outcomes. The proposed method introduces latent variables and adds Lasso sparsity constraints to the estimated loadings to facilitate the interpretation of the relationship between outcomes and covariates. The interpretation of the subgroups is made by visualizing treatment effects and latent variables. Since we are performing sparse estimation, we can interpret the covariates related to the treatment effects and subgroups. Finally, simulation and real data examples demonstrate the effectiveness of the proposed method.

Causal rule ensemble method for estimating heterogeneous treatment effect with consideration of prognostic effects.

We propose a novel framework based on the RuleFit method to estimate heterogeneous treatment effect in randomized clinical trials. The proposed method estimates a rule ensemble comprising a set of prognostic rules, a set of prescriptive rules, as well as the linear effects of the original predictor variables. The prescriptive rules provide an interpretable description of the heterogeneous treatment effect. By including a prognostic term in the proposed model, the selected rule is represented as an heterogeneous treatment effect that excludes other effects. We confirmed that the performance of the proposed method was equivalent to that of other ensemble learning methods through numerical simulations and demonstrated the interpretation of the proposed method using a real data application.

Clustering preference data in the presence of response-style bias.

Preference data, such as Likert scale data, are often obtained in questionnaire-based surveys. Clustering respondents based on survey items is useful for discovering latent structures. However, cluster analysis of preference data may be affected by response styles, that is, a respondent's systematic response tendencies irrespective of the item content. For example, some respondents may tend to select ratings at the ends of the scale, which is called an 'extreme response style'. A cluster of respondents with an extreme response style can be mistakenly identified as a content-based cluster. To address this problem, we propose a novel method of clustering respondents based on their indicated preferences for a set of items while correcting for response-style bias. We first introduce a new framework to detect, and correct for, response styles by generalizing the definition of response styles used in constrained dual scaling. We then simultaneously correct for response styles and perform a cluster analysis based on the corrected preference data. A simulation study shows that the proposed method yields better clustering accuracy than the existing methods do. We apply the method to empirical data from four different countries concerning social values.

Revealing Changes in Brain Functional Networks Caused by Focused-Attention Meditation Using Tucker3 Clustering.

This study examines the effects of focused-attention meditation on functional brain states in novice meditators. There are a number of feature metrics for functional brain states, such as functional connectivity, graph theoretical metrics, and amplitude of low frequency fluctuation (ALFF). It is necessary to choose appropriate metrics and also to specify the region of interests (ROIs) from a number of brain regions. Here, we use a Tucker3 clustering method, which simultaneously selects the feature vectors (graph theoretical metrics and fractional ALFF) and the ROIs that can discriminate between resting and meditative states based on the characteristics of the given data. In this study, breath-counting meditation, one of the most popular forms of focused-attention meditation, was used and brain activities during resting and meditation states were measured by functional magnetic resonance imaging. The results indicated that the clustering coefficients of the eight brain regions, Frontal Inf Oper L, Occipital Inf R, ParaHippocampal R, Cerebellum 10 R, Cingulum Mid R, Cerebellum Crus1 L, Occipital Inf L, and Paracentral Lobule R increased through the meditation. Our study also provided the framework of data-driven brain functional analysis and confirmed its effectiveness on analyzing neural basis of focused-attention meditation.