Date on Master's Thesis/Doctoral Dissertation
Bioinformatics and Biostatistics
Committee Co-Chair (if applicable)
Terson de Paleville, Daniela
The log rank test is a popular nonparametric test for comparing the marginal survival distribution of two groups. When data are organized within clusters and the size of clusters or the distribution of group membership within a cluster is related to an outcome of interest, traditional methods of data analysis can be biased. In this thesis, we develop a within-cluster group weighted log rank test to compare marginal survival time distributions between groups from clustered data, correcting for cluster size and intra-cluster group size informativeness. The performance of this new test is compared with the unweighted and cluster-weighted log rank tests via a simulation study. The simulation results suggest the new test performs appropriately under scenarios of cluster size and intra-cluster group size informativeness, and produces higher power than the two comparison tests under non-informative scenarios. The new test is then illustrated on a live data set comparing time to functional improvement in task performance from patients with spinal cord injuries.
Gregg, Mary Elizabeth, "A log rank test for clustered data under informative within-cluster group size." (2016). Electronic Theses and Dissertations. Paper 2434.