For clinical trials with time-to-event data, statistical inference often employs the constant
hazard ratio assumption. When the hazards are possibly non-proportional, the hazard ratio …

In many studies with a survival outcome, it is often not feasible to fully observe the primary
event of interest. This often leads to heavy censoring and thus, difficulty in efficiently …

A semiparametric estimate of an average regression effect with right‐censored failure time
data has recently been proposed under the Cox‐type model where the regression effect β (t) …

When it comes to the medical field, and particularly pharmaceutical research, survival
analysis is frequently used with clinical trial data. Often the objective of a clinical trial is to …

In the analysis of survival data, one may encounter the problem of competing risks where
each subject may fail due to one of Kcauses, referred to as competing risks. In the competing …

Survival or time-to-event data are ubiquitous in clinical trials research. The presence of
censoring requires specialized methods for the analysis of this type of data. This chapter …

The research in this thesis focuses on methods for estimating the cumulative treatment effect
on time to an event in the setting when the treatment-specific hazards are not proportional. In …

When testing the null hypothesis that treatment arm-specific survival-time distributions are
equal, the log-rank test is asymptotically valid when the distribution of time to censoring is …

In a clinical trial with a time‐to‐event endpoint the treatment effect can be measured in
various ways. Under proportional hazards all reasonable measures (such as the hazard …

We propose a nonparametric estimate of the scale-change parameter for characterizing the
difference between two survival functions under the accelerated failure time model using an …