Bayesian Solutions for Assessing Differential Effects in Biomarker Positive and Negative Subgroups.

The number of clinical trials that include a binary biomarker in design and analysis has risen due to the advent of personalised medicine. This presents challenges for medical decision makers because a drug may confer a stronger effect in the biomarker positive group, and so be approved either in this subgroup alone or in the all-comer population. We develop and evaluate Bayesian methods that can be used to assess this.

Characterizing Trends in Cancer Patients' Survival Using the JPSurv Software.

Background: Improvements in cancer survival are usually assessed by comparing survival in grouped years of diagnosis. To enhance analyses of survival trends, we present the joinpoint survival model webtool (JPSurv) that analyzes survival data by single year of diagnosis and estimates changes in survival trends and year-over-year trend measures.

Methods: We apply JPSurv to relative survival data for individuals diagnosed with female breast cancer, melanoma cancer, non-Hodgkin lymphoma (NHL), and chronic myeloid leukemia (CML) between 1975 and 2015 in the Surveillance, Epidemiology, and End Results Program.

Can We Use Survival Data from Cancer Registries to Learn about Disease Recurrence? The Case of Breast Cancer.

Population-representative risks of metastatic recurrence are not generally available because cancer registries do not collect data on recurrence. This article presents a novel method that estimates the risk of recurrence using cancer registry disease-specific survival. The method is based on an illness-death process coupled with a mixture cure model for net cancer survival.

Longitude Position in a Time Zone and Cancer Risk in the United States.

Circadian disruption is a probable human carcinogen. From the eastern to western border of a time zone, social time is equal, whereas solar time is progressively delayed, producing increased discrepancies between individuals' social and biological circadian time. Accordingly, western time zone residents experience greater circadian disruption and may be at an increased risk of cancer.

Detecting discordance enrichment among a series of two-sample genome-wide expression data sets.

Background: With the current microarray and RNA-seq technologies, two-sample genome-wide expression data have been widely collected in biological and medical studies. The related differential expression analysis and gene set enrichment analysis have been frequently conducted. Integrative analysis can be conducted when multiple data sets are available.

An efficient concordant integrative analysis of multiple large-scale two-sample expression data sets.

Motivation: We have proposed a mixture model based approach to the concordant integrative analysis of multiple large-scale two-sample expression datasets. Since the mixture model is based on the transformed differential expression test P-values (z-scores), it is generally applicable to the expression data generated by either microarray or RNA-seq platforms. The mixture model is simple with three normal distribution components for each dataset to represent down-regulation, up-regulation and no differential expression.

Concordant integrative gene set enrichment analysis of multiple large-scale two-sample expression data sets.

Background: Gene set enrichment analysis (GSEA) is an important approach to the analysis of coordinate expression changes at a pathway level. Although many statistical and computational methods have been proposed for GSEA, the issue of a concordant integrative GSEA of multiple expression data sets has not been well addressed. Among different related data sets collected for the same or similar study purposes, it is important to identify pathways or gene sets with concordant enrichment.