Upcoming seminars

Monday, October 07, 15:15

Jan Feifel
Institute of Statistics, Ulm University
Subcohorting methods for rare time-dependent exposures in time-to-event data

Antimicrobial resistance is one of the major burdens for today’s society. The challenges for researches conducting studies on the effect of those rare exposures on the hospital stay are manifold.

For large cohort studies with rare outcomes nested case-control designs are favorable due to the efficient use of limited resources. In our setting, nested case-control designs apply but do not lead to truly reduced sample sizes, because the outcome is not rare. We, therefore, study a modified nested case-control design, which samples all exposed patients but not all unexposed ones. Here, the inclusion probability of observed events evolves over time. This new scheme improves on the classical nested case-control design where for every observed event controls are chosen at random.

We will discuss several options on how to account for past time-dependent exposure status within a nested case-control design and their related merits. It will be seen that a smart utilization of the available information at each point in time can lead to a powerful and simultaneously less expensive design. We will also sketch alternative designs, e.g. treating exposure as a left-truncation event that generates matched controls, and time-simultaneous inference of the baseline hazard using the wild bootstrap. The methods will be applied to observational data on the impact of hospital-acquired pneumonia on the length-of-stay in hospital, which is an outcome commonly used to express both the impact and the costs of such adverse events.

Monday, October 21, 15:15

Halina Frydman
NYU Stern School of Business
An Ensemble Method for Interval-Censored Time-to-Event Data

Interval-censored data analysis is important in biomedical statistics for any type of time-to-event response where the time of response is not known exactly, but rather only known to occur between two assessment times. Many clinical trials and longitudinal studies generate interval-censored data; one common example occurs in medical studies that entail periodic follow-up. In this paper, we propose a survival forest method for interval-censored data based on the conditional inference framework. We describe how this framework can be adapted to the situation of interval-censored data. We show that the tuning parameters have a non-negligible effect on the survival forest performance and guidance is provided on how to tune the parameters in a data-dependent way to improve the overall performance of the method. Using Monte Carlo simulations, we show that the proposed survival forest is at least as effective as a survival tree method when the underlying model has a tree structure, performs similarly to an interval-censored Cox proportional hazards model when the true relationship is linear, and outperforms the survival tree method and Cox model when the true relationship is nonlinear. We illustrate the application of the method on a breast cancer data.

Wednesday, November 06, 15:15

Yu Shen
Department of Biostatistics, M. D. Anderson Cancer Center, University of Texas
Estimation of Longitudinal Medical Cost Trajectory

Estimating the average monthly medical costs from disease diagnosis to a terminal event such as death for an incident cohort of patients is a topic of immense interest to researchers in health policy and health economics because patterns of average monthly costs over time reveal how medical costs vary across phases of care. The statistical challenges to estimating monthly medical costs longitudinally are multifold; the longitudinal cost trajectory (formed by plotting the average monthly costs from diagnosis to the terminal event) is likely to be nonlinear, with its shape depending on the time of the terminal event, which can be subject to right censoring. We tackle this statistically challenging topic by estimating the conditional mean cost at any month given the time of the terminal event. The longitudinal cost trajectories with different terminal event times form a bivariate surface, under some constraint. We propose to estimate this surface using bivariate penalized splines in an Expectation-Maximization algorithm that treats the censored terminal event times as missing data. We evaluate the proposed model and estimation method in simulations and apply the method to the medical cost data of an incident cohort of stage IV breast cancer patients from the Surveillance, Epidemiology and End Results–Medicare Linked Database. This is a joint work of Li, Wu, Ning, Huang, Shih and Shen.

Thursday, November 14, 15:15

Michael Væth
Department of Biostatistics, Univeristy of Aarhus

Thursday, November 28, 11:00

Alberto Cairo
Director of the Visualization Program at the Center for Computational Science, University of Miami
Visualization and Graphic Design for Scientists

When designing a data visualization, showing the data comes first. After all, the main goal of a visualization is letting the reader spot patterns and trends behind numbers. But what if the visualization we design is to be presented to a general audience? In that case we may want to think deeply about visual design elements such as typography, color, composition, and hierarchy. This talk teaches non-designers such as scientists and statisticians how to make our charts, graphs, publications, and conference posters look better.

Thursday, November 28, 15:00

Alberto Cairo
Director of the Visualization Program at the Center for Computational Science, University of Miami
How Charts Lie

We’ve all heard that a picture is worth a thousand words, but what if we don’t understand what we’re looking at?

Charts, infographics, and diagrams are ubiquitous. They are useful because they can reveal patterns and trends hidden behind the numbers we encounter in our lives. Good charts make us smarter—if we know how to read them.

However, they can also deceive us. Charts lie in a variety of ways—displaying incomplete or inaccurate data, suggesting misleading patterns, and concealing uncertainty— or are frequently misunderstood. Many of us are ill-equipped to interpret the visuals that politicians, journalists, advertisers, and even our employers present each day. This talk teaches to not only spot the lies in deceptive visuals, but also to take advantage of good ones.

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.