PhD Studentship: Covariates to include in the imputation of missing outcome data in Randomised Cont
Missing data exists in almost every clinical trial and are almost unavoidable in research. Missing data may bias the results of a study. Missing data presents statistical issues when estimating treatment effects. Patient Reported Outcome measures (PROMs) are becoming increasingly used in clinical research. PROMs typically have several dimensions and use multiple items to measure these dimensions. Missing items are more likely to occur with PROM data than with clinical data as most PROMs are self-completed and patients may refuse to answer some or all of the items. Missing PROM values may be imputed.
There are several different methods for imputing missing PROM data: Simple mean, Last Observation Carried Forward (LOCF), Horizontal mean, regression, Markov chain, hot deck, multiple linear regression imputation, predictive mean matching, multiple imputation, monotonic multiple imputation and multivariate Normal imputation. The regulators regard most imputation methods as imperfect. However, they recommend: specifying imputation methods in advance in the protocol/statistical analysis plan; imputing missing PROM items according to instrument developer’s guidelines; using several imputation methods with a sensitivity analysis of the results. It is desirable to report analyses with and without imputation and to explore different imputation techniques in order that intention to treat analysis can be performed.
For some of the imputation methods such as LOCF the randomised treatment group is effectively included in the model. For other imputation methods such as: Markov Chain; hot deck; multiple linear regression imputation; predictive mean matching; multiple imputation; monotonic multiple imputation and multivariate normal imputation the statistician has to make a decision about whether or not to include the randomised treatment group as covariate in the imputation model/method.
The proposed research plan would be to undertake a review of the statistical literature on methods for imputing missing data in RCTs and in particular whether or not to include randomised treatment group as a covariate in the imputation; followed by an audit of recently published RCTs to determine what imputation methods are commonly in used and whether or not the treatment group is included as a covariate in the imputation process. The imputation methods and their effect on statistical analysis and conclusions will be compared using the data from several RCTs with PROMs. The project would then involve some computer simulation and analysis to compare the different methods of imputation (with and without the randomised treatment group as a covariate) with the view to developing guidance on whether or not to include the treatment group as a covariate in the imputation model.
The Faculty Scholarships for Medicine, Dentistry & Health cover fees and stipend at Home/EU level. Overseas students may apply but will need to fund the fee differential between Home and Overseas rate from another source.
Candidates should possess either one of the following:
A First class honours degree in mathematics or statistics, or
An Upper Second class honours degree in mathematics or statistics and a Masters qualification