My project focuses on Methods for deterministic treatment effect estimates for clinical trials with missing data. The statistical analysis of clinical trials is often complicated by missing data. Patients may drop out of the clinical trials, which often leads to subsequent outcome measurements being missing for such individuals. Intermediate missing values may also occur when a patient misses a scheduled follow-up visit. Methods have been developed for obtaining estimates from trials which accommodate such missingness in a principled way, by making an assumption about how the missing data relate to the observed data. Because such assumptions cannot be verified from the observed data, a range of methods have been developed for assessing the sensitivity of inferences to these untestable assumptions. Nowadays such analyses are often performed using the method of multiple imputation, where each missing value is replaced multiple times by plausible values based on a statistical model. This leads to multiple completed datasets, each of which is analysed. The estimates from each dataset are then pooled. A drawback of standard multiple imputation methods is that the imputations are drawn randomly, the treatment effect estimates obtained are (to some extent) random and it will give slightly different answers depending on the computer’s random number seed. For the primary analysis of a clinical trial, which decides whether a new medical treatment is licensed by regulatory agencies (such as the MHRA) or not, this is quite undesirable and deterministic methods would be much preferable. This PhD project will investigate deterministic single imputation methods for handling missing data in clinical trials, as an alternative to multiple imputation.