Upcoming seminars

Monday, June 18, 15:15

Jacob Fiksel
Johns Hopkins Bloomberg School of Public Health, Baltimore, USA
Optimized Survival Evaluation to Guide Bone Metastases Management: Developing an Improved Statistical Approach

In managing bone metastases, estimation of life expectancy is central for individualizing patient care given a range of radiotherapy (RT) treatment options. With access to larger volume and more complex patient data and statistical models, oncologists and statisticians must develop methods for optimal decision support. Approaches incorporating many covariates should identify complex interactions and effects while also managing missing data. In this talk, I discuss how a statistical learning approach, random survival forests (RSF), handles these challenges in building survival prediction models. I show how we applied RSF to develop a clinical model which predicts survival for patients with bone metastases using 26 predictor variables and outperforms two simpler, validated Cox regression models. I will conclude by introducing a simple bootstrap based procedure, which can be used for both simple and complex prediction models, to produce valid confidence interval estimates for model performance metrics using internal validation.

Thursday, June 21, 15:15

Ramon Oller Piqué
Central University of Catalonia
A nonparametric test for the association between longitudinal covariates and censored survival data

Many biomedical studies focus on the association between a longitudinal measurement and a time-to-event outcome and quantify this association by means of a longitudinal-survival joint model. In this paper we propose the LLR test, a longitudinal extension of the log-rank test statistic given by Peto and Peto (1972), to provide evidence of a plausible association between a time-to-event outcome (right- or interval-censored) and a longitudinal covariate. As joint model methods are complex and hard to interpret, a preliminar test for the association between both processes, such as LLR, is wise. The statistic LLR can be expressed in the form of a weighted difference of hazards, yielding to a broad class of weighted log-rank test statistics, LWLR, which allow to assess the association between the longitudinal covariate and the survival time stressing earlier, middle or late hazard differences through different weighting functions. The asymptotic distribution of LLR is derived by means of a permutation approach under the assumption that the underlying censoring process is identical for all individuals. A simulation study is conducted to evaluate the performance of the test statistics LLR and LWLR and shows that the empirical size is close to the significance level and that the power of the test depends on the association between the covariates and the survival time. Four data sets together with a toy example are used to illustrate the LLR test. Three of the data sets involve right-censored data and correspond to the European Randomized Screening for Prostate Cancer study (Serrat and others, 2015) and two well-known data sets given in the R package JM. The fourth data set explores the study Epidemiology of Diabetes Interventions and Complications (Sparling and others, 2006) which includes interval-censored data.

Map of CSS

You can find CSS next to the Botanical Garden, 5 minutes from Nørreport station.


Meeting room 5.2.46 is the library of the Biostatistics section, located in building 5, 2nd floor, room 46. See the map below for directions inside CSS.