SIM-16-0472-Cure-Rate-Historical

All programming files associated with the paper titled "Bayesian Design of a Survival Trial with a Cured Fraction using Historical Data

Multivariate Recurrent Events in the Presence of Multivariate Informative Censoring with Applications to Bleeding and Transfusion Events in Myelodysplastic Syndrome

<div><p>We propose a general novel class of joint models to analyze recurrent events that has a wide variety of applications. The focus in this article is to model the bleeding and transfusion events in myelodysplastic syndrome (MDS) studies, where patients may die or withdraw from the study early due to adverse events or other reasons, such as consent withdrawal or required alternative therapy during the study. The proposed model accommodates multiple recurrent events and multivariate informative censoring through a shared random-effects model. The random-effects model captures both within-subject and within-event dependence simultaneously. We construct the likelihood function for the semiparametric joint model and develop an expectation–maximization (EM) algorithm for inference. The computational burden does not increase with the number of types of recurrent events. We utilize the MDS clinical trial data to illustrate our proposed methodology. We also conduct a number of simulations to examine the performance of the proposed model.</p> </div

SR-HARDI: Spatially Regularizing High Angular Resolution Diffusion Imaging

<p>High angular resolution diffusion imaging (HARDI) has recently been of great interest in mapping the orientation of intravoxel crossing fibers, and such orientation information allows one to infer the connectivity patterns prevalent among different brain regions and possible changes in such connectivity over time for various neurodegenerative and neuropsychiatric diseases. The aim of this article is to propose a penalized multiscale adaptive regression model (PMARM) framework to spatially and adaptively infer the orientation distribution function (ODF) of water diffusion in regions with complex fiber configurations. In PMARM, we reformulate the HARDI imaging reconstruction as a weighted regularized least-square regression (WRLSR) problem. Similarity and distance weights are introduced to account for spatial smoothness of HARDI, while preserving the unknown discontinuities (e.g., edges between white matter and gray matter) of HARDI. The <i>L</i>₁ penalty function is introduced to ensure the sparse solutions of ODFs, while a scaled <i>L</i>₁ weighted estimator is calculated to correct the bias introduced by the <i>L</i>₁ penalty at each voxel. In PMARM, we integrate the multiscale adaptive regression models, the propagation-separation method, and Lasso (least absolute shrinkage and selection operator) to adaptively estimate ODFs across voxels. Experimental results indicate that PMARM can reduce the angle detection errors on fiber crossing area and provide more accurate reconstruction than standard voxel-wise methods. Supplementary materials for this article are available online.</p

Bayesian Model Assessment in Joint Modeling of Longitudinal and Survival Data With Applications to Cancer Clinical Trials

<p>Joint models for longitudinal and survival data are routinely used in clinical trials or other studies to assess a treatment effect while accounting for longitudinal measures such as patient-reported outcomes. In the Bayesian framework, the deviance information criterion (DIC) and the logarithm of the pseudo-marginal likelihood (LPML) are two well-known Bayesian criteria for comparing joint models. However, these criteria do not provide separate assessments of each component of the joint model. In this article, we develop a novel decomposition of DIC and LPML to assess the fit of the longitudinal and survival components of the joint model, separately. Based on this decomposition, we then propose new Bayesian model assessment criteria, namely, ΔDIC and ΔLPML, to determine the importance and contribution of the longitudinal (survival) data to the model fit of the survival (longitudinal) data. Moreover, we develop an efficient Monte Carlo method for computing the conditional predictive ordinate statistics in the joint modeling setting. A simulation study is conducted to examine the empirical performance of the proposed criteria and the proposed methodology is further applied to a case study in mesothelioma. Supplementary materials for this article are available online.</p

Participants' gender and baseline age by study group.

<p>Gender and baseline age distribution by study group. Chi-squared test of independence between gender and study group yields a p-value of 0.02. ANOVA F-test for differences in mean age between study groups yields a p-value of 0.18.</p

3-dimensional map of genetic variation estimates.

<p>Views (clockwise from top left): right lateral (A), anterior (B), superior (C), inferior (F), posterior (E), and left lateral (D). Hotter colors (black</p

Genetic Variation estimates for major regional brain volumes.

<p>Genetic variation estimates, standard errors, and associated likelihood ratio tests for four aggregated volumes.</p

Genetic variation estimates and additional results for non-overlapping brain regions.

<p>Genetic variation estimates (top left; A) and the associated −log₁₀p-values from LRT (top right; B). Hotter colors (black</p

Genetic variation estimates and associated clustering results for ROI volumes.

<p>Genetic variation estimates, standard errors, associated LRT p-values, and clustering results for 93 non-overlapping ROIs.</p