BAYESIAN METHODS FOR JOINT MODELING OF LONGITUDINAL AND SURVIVAL DATA WITH APPLICATIONS TO CANCER VACCINE TRIALS

Vaccines have received a great deal of attention recently as potential therapies in cancer clinical trials. One reason for this is that they are much less toxic than chemotherapies and potentially less expensive. However, little is currently known about the biologic activity of vaccines and whether they are associated with clinical outcome. The antibody immune measures IgG and IgM have been proposed as potential useful measures in melanoma clinical trials because of their observed association with clinical outcome in pilot studies. To better understand the role of the IgG and IgM antibodies for a particular vaccine, we examine a case study in melanoma and investigate the association between clinical outcome and an individual's antibody (IgG and IgM titers) history over time. The Cox proportional hazards model is used to study the relationship between the antibody titers as a time varying covariate and survival. We develop a Bayesian joint model for multivariate longitudinal and survival data and give its biologic motivation. Various scientific features of the model are discussed and interpreted. In addition, we present a model assessment tool called the multivariate L measure that allows us to formally compare different models. A detailed analysis of a recent phase II melanoma vaccine clinical trial conducted by the Eastern Cooperative Oncology Group is presented.

[1]  J G Ibrahim,et al.  Predictive variable selection for the multivariate linear model. , 1997, Biometrics.

[2]  Purushottam W. Laud,et al.  A Predictive Approach to the Analysis of Designed Experiments , 1994 .

[3]  J. Ibrahim,et al.  A Bayesian semiparametric joint hierarchical model for longitudinal and survival data. , 2003, Biometrics.

[4]  Joseph G. Ibrahim,et al.  Criterion-based methods for Bayesian model assessment , 2001 .

[5]  P. Mertens,et al.  A predictive approach to the analysis of intonation in discourse in French , 2006 .

[6]  D. Thomas,et al.  Simultaneously modelling censored survival data and repeatedly measured covariates: a Gibbs sampling approach. , 1996, Statistics in medicine.

[7]  S L Zeger,et al.  The Evaluation of Multiple Surrogate Endpoints , 2001, Biometrics.

[8]  Bradley P. Carlin,et al.  Markov Chain Monte Carlo conver-gence diagnostics: a comparative review , 1996 .

[9]  R Henderson,et al.  Joint modelling of longitudinal measurements and event time data. , 2000, Biostatistics.

[10]  Marie Davidian,et al.  An estimator for the proportional hazards model with multiple longitudinal covariates measured with error. , 2002, Biostatistics.

[11]  Victor DeGruttola,et al.  Modeling The Relationship Between Progression Of CD4-Lymphocyte Count And Survival Time , 1992 .

[12]  Yan Wang,et al.  Jointly Modeling Longitudinal and Event Time Data With Application to Acquired Immunodeficiency Syndrome , 2001 .

[13]  V. Sondak,et al.  High- and low-dose interferon alfa-2b in high-risk melanoma: first analysis of intergroup trial E1690/S9111/C9190. , 2000, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[14]  Jeremy M. G. Taylor,et al.  A Stochastic Model for Analysis of Longitudinal AIDS Data , 1994 .

[15]  M. Wulfsohn,et al.  Modeling the Relationship of Survival to Longitudinal Data Measured with Error. Applications to Survival and CD4 Counts in Patients with AIDS , 1995 .

[16]  V. De Gruttola,et al.  Modelling progression of CD4-lymphocyte count and its relationship to survival time. , 1994, Biometrics.

[17]  P. Heagerty,et al.  Misspecified maximum likelihood estimates and generalised linear mixed models , 2001 .

[18]  Purushottam W. Laud,et al.  Predictive Model Selection , 1995 .

[19]  S. Zeger,et al.  Joint analysis of longitudinal data comprising repeated measures and times to events , 2001 .

[20]  M. Wulfsohn,et al.  A joint model for survival and longitudinal data measured with error. , 1997, Biometrics.