Statistics Seminar
| Date/Time: | Friday, 13 Jan 2012 from 4:10 pm to 5:00 pm |
|---|---|
| Location: | Snedecor 3105 |
| Cost: | Free |
| URL: | www.stat.iastate.edu |
| Contact: | Jeanette La Grange |
| Phone: | 515-294-3440 |
| Channel: | College of Liberal Arts and Sciences |
| Categories: | Lectures |
| Actions: | Download iCal/vCal | Email Reminder |
Because the number of patients waiting for organ transplants exceeds the number of organs available, a better understanding of how transplantation affects the distribution of residual lifetime is needed to improve organ allocation. However, there has been little work to assess the survival benefit of transplantation from a causal perspective. Previous methods developed to estimate the causal effects of treatment in the presence of time-varying confounders have assumed that treatment assignment was independent across patients, which is not true for organ transplantation. We develop a version of G-estimation that accounts for the fact that treatment assignment is not independent across individuals to estimate the parameters of a structural nested failure time model. In addition, G-estimation for failure time models requires the use of artificial censoring, a technique where some subjects observed to fail are censored. Because artificial censoring reduces the information available and leads to non-smooth estimating equations, prior research has noted that finding the solutions to estimating equations can be difficult. We suggest some computational strategies to mitigate the problems typically encountered with artificial censoring. We derive the asymptotic properties of our estimator and confirm through simulation studies that our method leads to valid inference on the effect of transplantation on the distribution of residual lifetime. We demonstrate our method on the survival benefit of lung transplantation using data from the United Network for Organ Sharing (UNOS).