Studies with time-to-event endpoints and small sample sizes are commonly seen; however, most statistical methods are based on large sample considerations. We develop novel methods for analyzing crossover and parallel study designs with small sample sizes and time-to-event outcomes. For two-period, two-treatment (2x2) crossover designs, we propose a method in which censored values are treated as missing data and multiply imputed using pre-specified parametric failure time models. The failure times in each imputed dataset are then log-transformed and analyzed using ANCOVA. Results obtained from the imputed datasets are synthesized for point and confidence interval estimation of the treatment-ratio of geometric mean failure times using model-averaging in conjunction with Rubin's combination rule. We use simulations to illustrate the favorable operating characteristics of our method relative to two other existing methods. We apply the proposed method to study the effect of an experimental drug relative to placebo in delaying a symptomatic cardiac-related event during a 10-minute treadmill walking test. For parallel designs for comparing survival times between two groups in the setting of proportional hazards, we propose a refined generalized log-rank (RGLR) statistic by eliminating an unnecessary approximation in the development of Mehrotra and Roth's GLR approach (2001). We show across a variety of simulated scenarios that the RGLR approach provides a smaller bias than the commonly used Cox model, parametric models and the GLR approach in small samples (up to 40 subjects per group), and has notably better efficiency relative to Cox and parametric models in terms of mean squared error. The RGLR approach also consistently delivers adequate confidence interval coverage and type I error control. We further show that while the performance of the parametric model can be significantly influenced by misspecification of the true underlying survival distribution, the RGLR approach provides a consistently low bias and high relative efficiency. We apply all competing methods to data from two clinical trials studying lung cancer and bladder cancer, respectively. Finally, we further extend the RGLR method to allow for stratification, where stratum-specific estimates are first obtained using RGLR and then combined across strata for overall estimation and inference using two different weighting schemes. We show through simulations the stratified RGLR approach delivers smaller bias and higher efficiency than the commonly used stratified Cox model analysis in small samples, notably so when the assumption of a constant hazard ratio across strata is violated. A dataset is used to illustrate the utility of the proposed new method.