Missing data - such as from loss-to-follow-up in longitudinal studies - can lead to bias in analyses, resulting in incorrect conclusions. Various methods have been developed to try and account for such bias due to missing data; two common approaches are Multiple Imputation (MI), where missing data are imputed numerous times, analysed and then combined together, and Inverse-Probability Weighting (IPW), where individuals with observed data are weighted so they represent the original sample. However, sometimes these approaches are insufficient by themselves - e.g., MI may not be appropriate when imputing large swathes of missingness due to potential model misspecification/increased noise, while IPW is more challenging when there is missing data in the covariates of the missingness/weighting model. Given this, there is a need to develop and explore methods which combine MI and IPW to maximise the strengths and minimise the limitations of each approach. While previous work in this area has been conducted, the current approaches cannot easily be applied to complex real-world data such as in ALSPAC and need to be combined to increase their utility to applied researchers (e.g., using MI to impute missing baseline covariate data in the IPW weighting model, followed by IPW to weight participants within a given 'block' of data, then MI again to impute missing data in the substantive analysis model).