Identification and efficacy of longitudinal markers for survival.

Methods for the combined analysis of survival time and longitudinal biomarker data have been developed in recent years, with most emphasis on modelling and estimation. This paper focuses on the use of longitudinal marker trajectories as individual-level surrogates for survival. A score test for association which requires only standard methods for implementation is derived for the initial identification of candidate biomarkers. Methods for assessing efficacy of markers are discussed and a measure contrasting conditional and marginal distributions is proposed. An application using prothrombin index as biomarker for survival of liver cirrhosis patients is included.

[1]  W J Boscardin,et al.  Longitudinal models for AIDS marker data , 1998, Statistical methods in medical research.

[2]  J. Ware,et al.  Random-effects models for longitudinal data. , 1982, Biometrics.

[3]  J. M. Taylor,et al.  A comparison of smoothing techniques for CD4 data measured with error in a time-dependent Cox proportional hazards model. , 1998, Statistics in medicine.

[4]  M. Crowder,et al.  A Score Test for the Multivariate Burr and Other Weibull Mixture Distributions , 1997 .

[5]  D. Commenges,et al.  Score test of homogeneity for survival data , 1995, Lifetime data analysis.

[6]  G. Molenberghs,et al.  The validation of surrogate endpoints in meta-analyses of randomized experiments. , 2000, Biostatistics.

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

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

[9]  J. Klein,et al.  Statistical Models Based On Counting Process , 1994 .

[10]  N Tygstrup,et al.  Updating prognosis and therapeutic effect evaluation in cirrhosis with Cox's multiple regression model for time-dependent variables. , 1986, Scandinavian journal of gastroenterology.

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

[12]  Glen A. Satten,et al.  Markov Chains with Measurement Error: Estimating the ‘True’ Course of a Marker of the Progression of Human Immunodeficiency Virus Disease , 1996 .

[13]  D. DeMets,et al.  Surrogate End Points in Clinical Trials: Are We Being Misled? , 1996, Annals of Internal Medicine.

[14]  R. Prentice Surrogate endpoints in clinical trials: definition and operational criteria. , 1989, Statistics in medicine.

[15]  N M Laird,et al.  Model-based approaches to analysing incomplete longitudinal and failure time data. , 1997, Statistics in medicine.

[16]  E. Christensen,et al.  Multilevel models for longitudinal variables prognostic for survival , 1996, Lifetime data analysis.

[17]  M. Wulfsohn,et al.  The Relationship of CD4 Counts over Time to Survival in Patients with AIDS: Is CD4 a Good Surrogate Marker? , 1992 .

[18]  J. Robins,et al.  Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models , 1999 .

[19]  G. Molenberghs,et al.  Criteria for the validation of surrogate endpoints in randomized experiments. , 1998, Biometrics.

[20]  D. Alberts,et al.  Surrogate end-point biomarkers as measures of colon cancer risk and their use in cancer chemoprevention trials. , 1997, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[21]  C. Begg,et al.  On the use of surrogate end points in randomized trials , 2000 .

[22]  B. Graubard,et al.  Statistical validation of intermediate endpoints for chronic diseases. , 1992, Statistics in medicine.

[23]  M. Schemper,et al.  Predictive Accuracy and Explained Variation in Cox Regression , 2000, Biometrics.