Semi‐supervised empirical Bayes group‐regularized factor regression

Münch, Magnus; Wiel, Mark A. van de; Vaart, Aad van der; Peeters, Carel F.W.

doi:10.1002/bimj.202100105

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…SPCR is a method using the least squares criterion, while Hirose and Imada proposed sparse factor regression with penalized likelihood 31 . Moreover, Münch et al proposed put forward factor regression based on empirical Bayes, incorporating prior knowledge using genetic data, 32 and while Menvouta, Serneels, and Verdonck proposed suggested a method to identify sparse factors that contribute to the outcomes based on sufficient dimension reduction 33 . However, these methods are considered for a single outcome and have not been used under a framework for estimating treatment effects.…”

Section: Introductionmentioning

confidence: 99%

Estimation and visualization of heterogeneous treatment effects for multiple outcomes

Yuki

Tanioka

Yadohisa

2022

Statistics in Medicine

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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.

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Section: Introductionmentioning

confidence: 99%