2022 Fall Meetings

Fall Semester, 2022

September 23:

We will read and discuss the paper by Wan et al. (2019).

References: Wan F, Titman AC, Jak TF. (2019). Subgroup analysis of treatment effects for misclassified biomarkers with time-to-event data. Appl. Statist., 68:1447-1463.

October 7:

PhD student Justin Clark will lead a discussion about the paper, "Introduction to the Analysis of Survival Data in the Presence of Competing Risks." Competing risks methods are very useful in clinical applications! Justin will also present a more general introduction to the survival analysis concepts covered in the paper geared towards our members who have less experience with survival analysis.

References: 

• Austin PC, Lee DS, Fine JP. (2016). Introduction to the Analysis of Survival Data in the Presence of Competing Risks. Circulation 133:601-609.

• Austin PC, Fine JP. (2017). Practical recommendations for reporting Fine-Gray model analyses for competing risk data. Stat in Med, 36(27):4391-4400.

• R vignette Multi-state models and competing risks.

• Web tool IPDfromKM.

October 18 and 21:

We'll watch the webinar (Survival Models for Spatial Data) together on the 10/18 and go over the highlights at our meeting on 10/21. Sorry for no virtual option for the webinar this time but you can register for yourself (for a fee) at https://www.eventbrite.com/e/survival-models-for-spatial-data-tickets-408149144077

References: 

• Taylor BM et al. R Package 'spatsurv', Version 1.8, Oct 14, 2022. 

• Benjamin M. Taylor and Barry S. Rowlingson (2017). spatsurv: An R Package for Bayesian Inference with Spatial Survival Models. Journal of Statistical Software, 77(4), 1-32.

November 4:

Dr. Marquis (Jue) Hou will lead a discussion on prediction for time-to-event outcomes. The reference paper addresses the risk prediction with imperfect event time and few labels on current status. The students can learn:

1. Model estimation for current status data.

2. Rank correlation derived estimation for semi-parametric transformation model.

3. Optimal combination of estimators through projection

4. Solving regression for rank deficient system.

References: 

• Hou J, Chan SF, Wang X, Cai T. (2021). Risk prediction with imperfect survival outcome information from electronic health records. Biometrics, First published: 08 November 2021. 

November 18:

PhD student Han Lu will present "A tutorial on using the survival package for multi-state models and competing risks" (slides). The tutorial will generally follow this R vignette and you are welcome to look at it before the meeting if you would like. More references and a pdf book on this topic can be found below.

References: 

• https://cran.r-project.org/web/packages/survival/vignettes/compete.pdf

• Lin DY and Wei LJ. (1989). The robust inference for the Co proportional hazards model. JASA 84 (408): 1074-1078.

• Beyersmann J, Allignol A, Schumacher M. (2012). Competing Risks and Multistate Models with R. Springer, NY.

Announcement: 

There is a free conference in the afternoon where the talk at 3:20 eastern (2:20 central) may be especially interesting since it's about survival random forests (https://ctsi.wakehealth.edu/announcements/2022/07/2022-berd-methods-conference). We are NOT planning to watch this conference together so please register and attend if you are interested.

December 2 (last meeting of Fall Semester):

Mr. Ryan Shanley, Senior Research Fellow in MCC Biostat Core, will lead a discussion on applying survival analysis methods to analyze GVHD data from the University of Minnesota Fairview BMT Database.

References: 

• El Jurdi N, Rayes A, MacMillan ML, et al. (2021). Steroid-dependent acute GVHD after allogeneic hematopoietic cell transplantation: risk factors and clinical outcomes. Blood Advances, 5(5):1352-1359.