The work presented in this thesis aims at contributing to the field of statistical methodology for the analysis of excess risk in matched cohort studies. The project was initiated by the Danish Cancer Society Research Center and motivated by the desire to investigate long-term health consequences of childhood cancer survivors. During the last five decades, as a consequence of improved survival rates, the major concern of childhood cancer research shifted from survival to late effects related to childhood cancer diagnosis and treatment. In 2009, thanks to dedicated childhood cancer researchers and to the resourceful Nordic national registries, the Adult Life after Childhood Cancer in Scandinavia (ALiCCS) was established to improve knowledge about late effects of childhood cancer. This study has a matched cohort design where for each childhood cancer survivor, five healthy comparison subjects of the same sex, age and country were randomly selected. The statistical models introduced in this thesis exploit the matching structure of the data to get a representative estimate of the excess risk of late effects in childhood cancer survivors. Two are the methods described: the first models the excess risk in terms of excess hazard, while the second estimates the excess cumulative incidence function. Both approaches assume that the risk for a childhood cancer survivor is the sum of a cluster-specific background risk defined on the age time scale and an excess term defined on the time since exposure time scale. Estimates of the excess model parameters are obtained by pairwise comparisons between the cancer survivor and all the other matched comparison members in the same cluster. The contribution of the models introduced in this thesis on the public health area is presented by an application on the 5-year soft-tissue sarcoma survivor data from the ALiCCS study. By handling different features of registry data, such as multiple events, different time scales, right censoring and left truncation, this approach offers an easy tool to study how the excess risk develops in time and how it is affected by important risk factors, such as treatment.
Functions estimating the excess risk models were implemented in R and are publicly available.
Supervisors: Thomas Scheike, Klaus K. Andersen, Christian Dehlendorff and Jeanette Falck Winther
Evaluators: Thomas Alexander Gerds, Martin Bøgsted, Bjørn Møller.
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.
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.
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.
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.
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.