Patients and Physicians Can Discuss Costs of Cancer Treatment in the Clinic.

PURPOSE: As one solution to reducing costs and medical bankruptcies, experts have suggested that patients and physicians should discuss the cost of care up front. Whether these discussions are possible in an oncology setting and what their effects on the doctor-patient relationship are is not known. METHODS: We used the National Comprehensive Cancer Network (NCCN) Guidelines and the eviti Advisor platform to show patients with metastatic breast, lung, or colorectal cancer the costs associated with their chemotherapy and/or targeted therapy options during an oncology consultation. We measured provider attitudes and assessed patient satisfaction when consultations included discussion of costs. RESULTS: We approached 107 patients; 96 (90%) enrolled onto the study, three (3%) asked if they could be interviewed at a later date, and eight (7%) did not want to participate. Only five of 18 oncologists (28%) felt comfortable discussing costs, and only one of 18 (6%) regularly asked patients about financial difficulties. The majority of patients (80%) wanted cost information, and 84% reported that these conversations would be even more important if their co-pays were to increase. In total, 72% of patients responded that no health care professional has ever discussed costs with them. The majority of patients (80%) had no negative feelings about hearing cost information. CONCLUSION: In an era of rising co-pays, patients with cancer want cost-of-treatment discussions, and these conversations do not lead to negative feelings in the majority of patients. Additional training to prepare clinicians for how to discuss costs with their patients is needed.

Semiparametric Bayesian inference for repeated fractional measurement data.

We discuss inference for repeated fractional data, with outcomes between 0 to 1, including positive probability masses on 0 and 1. The point masses at the boundaries prevent the routine use of logit and other commonly used transformations of (0, 1) data. We introduce a model augmentation with latent variables that allow for the desired positive probability at 0 and 1 in the model. A linear mixed effect model is imposed on the latent variables. We propose a Bayesian semiparametric model for the random effects distribution. Specifically, we use a Polya tree prior for the unknown random effects distribution. The proposed model can capture possible multimodality and skewness of random effect distribution. We discuss implementation of posterior inference by Markov chain Monte Carlo simulation. The proposed model is illustrated by a simulation study and a cancer study in dogs.

Semi-parametric Bayesian Inference for Multi-Season Baseball Data.

We analyze complete sequences of successes (hits, walks, and sacrifices) for a group of players from the American and National Leagues, collected over 4 seasons. The goal is to describe how players' performances vary from season to season. In particular, we wish to assess and compare the effect of available occasion-specific covariates over seasons. The data are binary sequences for each player and each season. We model dependence in the binary sequence by an autoregressive logistic model. The model includes lagged terms up to a fixed order. For each player and season we introduce a different set of autologistic regression coefficients, i.e., the regression coefficients are random effects that are specific to each season and player. We use a nonparametric approach to define a random effects distribution. The nonparametric model is defined as a mixture with a Dirichlet process prior for the mixing measure. The described model is justified by a representation theorem for order-k exchangeable sequences. Besides the repeated measurements for each season and player, multiple seasons within a given player define an additional level of repeated measurements. We introduce dependence at this level of repeated measurements by relating the season-specific random effects vectors in an autoregressive fashion. We ultimately conclude that while some covariates like the ERA of the opposing pitcher are always relevant, others like an indicator for the game being into the seventh inning may be significant only for certain seasons, and some others, like the score of the game, can safely be ignored.

A Semiparametric Bayesian Model for Repeatedly Repeated Binary Outcomes.

We discuss the analysis of data from single nucleotide polymorphism (SNP) arrays comparing tumor and normal tissues. The data consist of sequences of indicators for loss of heterozygosity (LOH) and involve three nested levels of repetition: chromosomes for a given patient, regions within chromosomes, and SNPs nested within regions. We propose to analyze these data using a semiparametric model for multi-level repeated binary data. At the top level of the hierarchy we assume a sampling model for the observed binary LOH sequences that arises from a partial exchangeability argument. This implies a mixture of Markov chains model. The mixture is defined with respect to the Markov transition probabilities. We assume a nonparametric prior for the random mixing measure. The resulting model takes the form of a semiparametric random effects model with the matrix of transition probabilities being the random effects. The model includes appropriate dependence assumptions for the two remaining levels of the hierarchy, i.e., for regions within chromosomes and for chromosomes within patient. We use the model to identify regions of increased LOH in a dataset coming from a study of treatment-related leukemia in children with an initial cancer diagnostic. The model successfully identifies the desired regions and performs well compared to other available alternatives.

Bayesian meta-analysis for longitudinal data models using multivariate mixture priors.

We propose a class of longitudinal data models with random effects that generalizes currently used models in two important ways. First, the random-effects model is a flexible mixture of multivariate normals, accommodating population heterogeneity, outliers, and nonlinearity in the regression on subject-specific covariates. Second, the model includes a hierarchical extension to allow for meta-analysis over related studies. The random-effects distributions are decomposed into one part that is common across all related studies (common measure), and one part that is specific to each study and that captures the variability intrinsic between patients within the same study. Both the common measure and the study-specific measures are parameterized as mixture-of-normals models. We carry out inference using reversible jump posterior simulation to allow a random number of terms in the mixtures. The sampler takes advantage of the small number of entertained models. The motivating application is the analysis of two studies carried out by the Cancer and Leukemia Group B (CALGB). In both studies, we record for each patient white blood cell counts (WBC) over time to characterize the toxic effects of treatment. The WBCs are modeled through a nonlinear hierarchical model that gathers the information from both studies.