Mark E. Boye

Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group

Explicitly modeling underlying relationships between a survival endpoint and processes that generate longitudinal measured or reported outcomes potentially could improve the efficiency of clinical trials and provide greater insight into the various dimensions of the clinical effect of interventions included in the trials. Various strategies have been proposed for using longitudinal findings to elucidate intervention effects on clinical outcomes such as survival. The application of specifically Bayesian approaches for constructing models that address longitudinal and survival outcomes explicitly has been recently addressed in the literature. We review currently available methods for carrying out joint analyses, including issues of implementation and interpretation, identify software tools that can be used to carry out the necessary calculations, and review applications of the methodology.

The economic burden of metastatic breast cancer: A systematic review of literature from developed countries

OBJECTIVE Breast cancer, the most common malignant cancer among women in Western countries, has poor prognosis following metastasis. New therapies potentially extend survival, but their value is questioned when benefits are incremental and expensive. The objective of our study was to understand the economic impact of metastatic breast cancer (MBC) and its treatment, and to evaluate the designs of these studies. METHODS We systematically reviewed the MEDLINE-indexed, English-language literature, identifying 31 articles on the economic evaluation of MBC in 10 developed countries, including studies of per-patient costs, gross national costs, and cost-effectiveness models. We also included health technology assessments (HTAs) from government and regulatory agencies. RESULTS Total per-patient costs of MBC are only available for Sweden (

Multilevel Bayesian Models for Survival Times and Longitudinal Patient-Reported Outcomes With Many Zeros

17,301-

Multicenter phase II trial of enzastaurin in patients with relapsed or refractory advanced cutaneous T-cell lymphoma

48,169 annually, depending on patient age (2005 USD)). Most economic analyses of per-patient direct costs originate from the US; across all countries, data indicate that this burden is substantial. Gross national costs of MBC are available only for the UK (cost of incident MBC cases is estimated to be

Joint Modeling of Multiple Longitudinal Patient-Reported Outcomes and Survival

22 million annually (2002 GBP)). Many cost-effectiveness analyses suggest that a number of new and established treatments are cost-effective compared to standard care in various countries, but many offer small increments in survival. The cost-effectiveness of trastuzumab, capecitabine, and nab-paclitaxel has been evaluated in many recent studies. CONCLUSION Most economic evaluations of MBC have utilized secondary rather than primary data, and have used scenarios and assumptions which may be inaccurate or outdated. The quality of evidence disseminated to decision-makers could be improved by adherence to best practices in cost-effectiveness analyses.

JMFit: A SAS macro for joint models of longitudinal and survival data

Regulatory approval of new therapies often depends on demonstrating prolonged survival. Particularly when these survival benefits are modest, consideration of therapeutic benefits to patient-reported outcomes (PROs) may add value to the traditional biomedical clinical trial endpoints. We extend a popular class of joint models for longitudinal and survival data to accommodate the excessive zeros common in PROs, building hierarchical Bayesian models that combine information from longitudinal PRO measurements and survival outcomes. The model development is motivated by a clinical trial for malignant pleural mesothelioma, a rapidly fatal form of pulmonary cancer usually associated with asbestos exposure. By separately modeling the presence and severity of PROs, using our zero-augmented beta (ZAB) likelihood, we are able to model PROs on their original scale and learn about individual-level parameters from both presence and severity of symptoms. Correlations among an individuals PROs and survival are modeled using latent random variables, adjusting the fitted trajectories to better accommodate the observed data for each individual. This work contributes to understanding the impact of treatment on two aspects of mesothelioma: patients’ subjective experience of the disease process and their progression-free survival times. We uncover important differences between outcome types that are associated with therapy (periodic, worse in both treatment groups after therapy initiation) and those that are responsive to treatment (aperiodic, gradually widening gap between treatment groups). Finally, our work raises questions for future investigation into multivariate modeling, choice of link functions, and the relative contributions of multiple data sources in joint modeling contexts.

Joint modeling of longitudinal outcomes and survival using latent growth modeling approach in a mesothelioma trial

This multicenter, single-arm, open-label non-randomized phase II trial (NCT00744991) was conducted in patients with recurrent/refractory mycosis fungoides (MF), stage IB–IVB, or Sézary syndrome (SS). A Simon two-stage design required 25 patients enrolled in stage 1 with ≥7 confirmed objective responses for expansion into stage 2. Patients were treated with oral enzastaurin (250 mg twice daily) until disease progression or intolerable toxicity. The primary endpoint was investigator-assessed response rate; secondary endpoints were time to objective response, response duration, time-to-progression, patient-reported pruritus, and safety/tolerability. Twenty-five patients were enrolled. A partial response was observed in one patient with MF. Median time-to-progression was 78 and 44 days in MF and SS, respectively. Self-reported pruritus relief and improved composite pruritus-specific symptom scores were documented in six and four patients, respectively. Enzastaurin was well tolerated with mostly grade 1–2 adverse events, mainly diarrhea and fatigue. There were two adverse event-related drug discontinuations with one possibly treatment-related.

Economic Burden of Follicular Non-Hodgkin’s Lymphoma

Researchers often include patient-reported outcomes (PROs) in Phase III clinical trials to demonstrate the value of treatment from the patients perspective. These data are collected as longitudinal repeated measures and are often censored by occurrence of a clinical event that defines a survival time. Hierarchical Bayesian models having latent individual-level trajectories provide a flexible approach to modeling such multiple outcome types simultaneously. We consider the case of many zeros in the longitudinal data motivating a mixture model, and demonstrate several approaches to modeling multiple longitudinal PROs with survival in a cancer clinical trial. These joint models may enhance Phase III analyses and better inform health care decision makers.

Assessing model fit in joint models of longitudinal and survival data with applications to cancer clinical trials

Joint models for longitudinal and survival data now have a long history of being used in clinical trials or other studies in which the goal is to assess a treatment effect while accounting for a longitudinal biomarker such as patient-reported outcomes or immune responses. Although software has been developed for fitting the joint model, no software packages are currently available for simultaneously fitting the joint model and assessing the fit of the longitudinal component and the survival component of the model separately as well as the contribution of the longitudinal data to the fit of the survival model. To fulfill this need, we develop a SAS macro, called JMFit. JMFit implements a variety of popular joint models and provides several model assessment measures including the decomposition of AIC and BIC as well as ΔAIC and ΔBIC recently developed in Zhang et al. (2014). Examples with real and simulated data are provided to illustrate the use of JMFit.

Cost-effectiveness of first-line induction and maintenance treatment sequences in non-squamous non-small cell lung cancer (NSCLC) in the U.S.

Joint modeling of longitudinal and survival data can provide more efficient and less biased estimates of treatment effects through accounting for the associations between these two data types. Sponsors of oncology clinical trials routinely and increasingly include patient-reported outcome (PRO) instruments to evaluate the effect of treatment on symptoms, functioning, and quality of life. Known publications of these trials typically do not include jointly modeled analyses and results. We formulated several joint models based on a latent growth model for longitudinal PRO data and a Cox proportional hazards model for survival data. The longitudinal and survival components were linked through either a latent growth trajectory or shared random effects. We applied these models to data from a randomized phase III oncology clinical trial in mesothelioma. We compared the results derived under different model specifications and showed that the use of joint modeling may result in improved estimates of the overall treatment effect.