Innovative estimation of survival using log-normal survival modelling on ACCENT database

[1]  D. Sargent,et al.  Comparison of innovative estimation of efficacy to standard using the ACCENT database. , 2011, Journal of Clinical Oncology.

[2]  A. Bardelli,et al.  Association of KRAS p.G13D mutation with outcome in patients with chemotherapy-refractory metastatic colorectal cancer treated with cetuximab. , 2010, JAMA.

[3]  R. Labianca,et al.  Defective mismatch repair as a predictive marker for lack of efficacy of fluorouracil-based adjuvant therapy in colon cancer. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[4]  R. Labianca,et al.  Evidence for cure by adjuvant therapy in colon cancer: observations based on individual patient data from 20,898 patients on 18 randomized trials. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[5]  Dongsheng Tu,et al.  K-ras mutations and benefit from cetuximab in advanced colorectal cancer. , 2008, The New England journal of medicine.

[6]  J. Ingle,et al.  Competing causes of death from a randomized trial of extended adjuvant endocrine therapy for breast cancer. , 2008, Journal of the National Cancer Institute.

[7]  Daniel J Sargent,et al.  End points for colon cancer adjuvant trials: observations and recommendations based on individual patient data from 20,898 patients enrolled onto 18 randomized trials from the ACCENT Group. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[8]  Jerald F. Lawless,et al.  Bivariate location–scale models for regression analysis, with applications to lifetime data , 2005 .

[9]  Richard L Schilsky,et al.  Cetuximab in the treatment of colorectal cancer. , 2004, Clinical advances in hematology & oncology : H&O.

[10]  Daniel J Sargent,et al.  Disease-free survival versus overall survival as a primary end point for adjuvant colon cancer studies: individual patient data from 20,898 patients on 18 randomized trials. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[11]  Laurence L. George,et al.  The Statistical Analysis of Failure Time Data , 2003, Technometrics.

[12]  J. Kalbfleisch,et al.  The Statistical Analysis of Failure Time Data: Kalbfleisch/The Statistical , 2002 .

[13]  P. Royston,et al.  Flexible parametric proportional‐hazards and proportional‐odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects , 2002, Statistics in medicine.

[14]  P. Royston,et al.  The Lognormal Distribution as a Model for Survival Time in Cancer, With an Emphasis on Prognostic Factors , 2001 .

[15]  R. Gray,et al.  Annual hazard rates of recurrence for breast cancer after primary therapy. , 1996, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[16]  P. Grambsch,et al.  Proportional hazards tests and diagnostics based on weighted residuals , 1994 .

[17]  A Ciampi,et al.  GENCOV: a Fortran program that generates randomly censored survival data with covariates. , 1989, Computer methods and programs in biomedicine.

[18]  J. Herson The statistical analysis of failure time data , 1981 .

[19]  J. Kalbfleisch,et al.  The Statistical Analysis of Failure Time Data , 1980 .

[20]  T. Eberlein Association of KRAS p.G13D Mutation With Outcome in Patients With Chemotherapy-Refractory Metastatic Colorectal Cancer Treated With Cetuximab , 2012 .

[21]  S J Pocock,et al.  Long term survival analysis: the curability of breast cancer. , 1982, Statistics in medicine.

[22]  J. Boag,et al.  Maximum Likelihood Estimates of the Proportion of Patients Cured by Cancer Therapy , 1949 .