Exploring Pathway-Based Group Lasso for Cancer Survival Analysis: A Special Case of Multi-Task Learning

Motivation: The Cox proportional hazard models are widely used in the study of cancer survival. However, these models often meet challenges such as the large number of features and small sample sizes of cancer data sets. While this issue can be partially solved by applying regularization techniques such as lasso, the models still suffer from unsatisfactory predictive power and low stability.Methods: Here, we investigated two methods to improve survival models. Firstly, we leveraged the biological knowledge that groups of genes act together in pathways and regularized both at the group and gene level using latent group lasso penalty term. Secondly, we designed and applied a multi-task learning penalty that allowed us leveraging the relationship between survival models for different cancers.Results: We observed modest improvements over the simple lasso model with the inclusion of latent group lasso penalty for six of the 16 cancer types tested. The addition of a multi-task penalty, which penalized coefficients in pairs of cancers from diverging too greatly, significantly improved accuracy for a single cancer, lung squamous cell carcinoma, while having minimal effect on other cancer types.Conclusion: While the use of pathway information and multi-tasking shows some promise, these methods do not provide a substantial improvement when compared with standard methods.

Random Effects Endogeneity: A Variable Pretest

The appealing but complex Hausman and Taylor (1981) random effects (instrumental variable) estimator requires prior knowledge that certain explanatory variables in a panel are uncorrelated with the latent group effects. The purpose of this examination is to outline a tractable variable pretest that facilitates the initial sorting of regressors as likely exogenous or endogenous. The variable pretest proposed herein builds on the pretest estimator suggested by Baltagi et al (2003) by providing the necessary foundation for regressor identification. Extensions are suggested for the two-way error components construct. Keywords: Panel data, Random effects, Variable pretest, Hausman-Taylor. JEL Classification: C12, C13, C23.

Testing the Associations Between Adult Playfulness and Sensation Seeking: A SEM Analysis of Librarians and Police Officers

Playfulness is an understudied personality trait in adults. We examined the relationships between facets of adult playfulness and sensation seeking (SS) in distant vocational groups, namely, librarians (N = 339) and police officers (N = 399). First, manifest and latent group comparisons (measurement invariance [MI] analysis) showed that police officers were higher in SS than librarians, while we found no group differences for playfulness. Second, structural equation modeling (SEM) analyses showed that playfulness was widely positively related to SS, and findings were replicated across groups. However, the effects were of small to moderate size, and playfulness and SS shared between 4 and 22% variance. Our findings indicate that playfulness is not redundant with SS. Our study extends the understanding of adult playfulness by clarifying its overlap and distinctiveness from SS.

Mediation analysis for mixture Cox proportional hazards cure models

Mediation analysis aims to decompose a total effect into specific pathways and investigate the underlying causal mechanism. Although existing methods have been developed to conduct mediation analysis in the context of survival models, none of these methods accommodates the existence of a substantial proportion of subjects who never experience the event of interest, even if the follow-up is sufficiently long. In this study, we consider mediation analysis for the mixture of Cox proportional hazards cure models that cope with the cure fraction problem. Path-specific effects on restricted mean survival time and survival probability are assessed by introducing a partially latent group indicator and applying the mediation formula approach in a three-stage mediation framework. A Bayesian approach with P-splines for approximating the baseline hazard function is developed to conduct analysis. The satisfactory performance of the proposed method is verified through simulation studies. An application of the Alzheimer’s disease (AD) neuroimaging initiative dataset investigates the causal effects of APOE-[Formula: see text] allele on AD progression.

Penalized longitudinal mixed models with latent group structure, with an application in neurodegenerative diseases

SummaryLarge-scale longitudinal data are often heterogeneous, spanning latent subgroups such as disease subtypes. In this paper, we present an approach called longitudinal joint cluster regression (LJCR) for penalized mixed modelling in the latent group setting. LJCR captures latent group structure via a mixture model that includes both the multivariate distribution of the covariates and a regression model for the response. The longitudinal dynamics of each individual are modeled using a random effect intercept and slope model. Inference is done via a profile likelihood approach that can handle high-dimensional covariates via ridge penalization. LJCR is motivated by questions in neurodegenerative disease research, where latent subgroups may reflect heterogeneity with respect to disease presentation, progression and diverse subject-specific factors. We study the performance of LJCR in the context of two longitudinal datasets: a simulation study and a study of amyotrophic lateral sclerosis (ALS). LJCR allows prediction of progression as well as identification of subgroups and subgroup-specific model parameters.