  The Kaplan-Meier estimator (KM) is widely used in medical research to
estimate the survival function from lifetime data. KM is a powerful tool to
evaluate clinical trials due to simple computational requirements, a logrank
hypothesis test, and the ability to censor patients. However, KM has several
constraints and fails to generalize to ordinal variables of interest such as
toxicity and ECOG performance. We devised Weighted Trajectory Analysis (WTA) to
combine the advantages of KM with the ability to compare treatment groups for
ordinal variables and fluctuating outcomes. To assess statistical significance,
we developed a new hypothesis test analogous to the logrank test. We
demonstrate the functionality of WTA through 1000-fold clinical trial
simulations of unique stochastic models of chemotherapy toxicity and
schizophrenia progression. At several increments of sample size and hazard
ratio, we compare the performance of WTA to both KM and Generalized Estimating
Equations (GEE). WTA generally required half the sample size to achieve
comparable power to KM; advantages over GEE include its robust non-parametric
approach and summary plot. We also apply WTA to real clinical data: the
toxicity outcomes of melanoma patients receiving immunotherapy and the disease
progression of patients with metastatic breast cancer receiving ramucirumab.
The application of WTA demonstrates that using traditional methods such as
percent incidence and KM can lead to both Type I and II errors by failing to
model illness trajectory. This article outlines a novel method for clinical
outcome assessment that extends the advantages of Kaplan-Meier estimates to
ordinal outcome variables.
