On the estimation of the effect of weight change on a health outcome using observational data, by utilising the target trial emulation framework.

Background/objectives: When studying the effect of weight change between two time points on a health outcome using observational data, two main problems arise initially (i) 'when is time zero?' and (ii) 'which confounders should we account for?' From the baseline date or the 1st follow-up (when the weight change can be measured)? Different methods have been previously used in the literature that carry different sources of bias and hence produce different results.

Methods: We utilised the target trial emulation framework and considered weight change as a hypothetical intervention. First, we used a simplified example from a hypothetical randomised trial where no modelling is required.

Modeling time-varying recruitment rates in multicenter clinical trials.

Multicenter phase II/III clinical trials are large-scale operations that often include hundreds of recruiting centers in several countries. Therefore, the operational aspects of a trial must be thoroughly planned and closely monitored to ensure better oversight and study conduct. Predicting patient recruitment plays a pivotal role in trial monitoring as it informs how many people are expected to be recruited on a given day.

Socio-economic inequalities in cancer survival: how do they translate into Number of Life-Years Lost?

Background: We aimed to investigate the impact of socio-economic inequalities in cancer survival in England on the Number of Life-Years Lost (NLYL) due to cancer.

Methods: We analysed 1.2 million patients diagnosed with one of the 23 most common cancers (92.

Identifying adults at high-risk for change in weight and BMI in England: a longitudinal, large-scale, population-based cohort study using electronic health records.

Background: Targeted obesity prevention policies would benefit from the identification of population groups with the highest risk of weight gain. The relative importance of adult age, sex, ethnicity, geographical region, and degree of social deprivation on weight gain is not known. We aimed to identify high-risk groups for changes in weight and BMI using electronic health records (EHR).