The Stats Geek

I’ve previously written about methods from causal inference (G-formula and G-estimation) that can be used to exploit data observed after patients experience intercurrent events (ICEs) to improve the precision (and hence statistical power) of estimates of…

Survival analysis of time-to-event outcomes is very commonly performed using Cox’s famous proportional hazards model.

We are currently recruiting for a Research Fellow position at London School of Hygiene & Tropical Medicine to work on an exciting new project that will develop machine learning based methods for handling missing data in statistical analyses.

Clinical trial estimands which make use of the so-called hypothetical strategy target the effect of one randomised treatment compared to another in a scenario where the corresponding intercurrent event does not happen.

Missing data are a common problem in statistical analyses.

The ICH E9 R1 addendum on estimands in clinical trials has made big waves in the clinical trial world in the last few years.

When you estimate a proportion and want to calculate a standard error for the estimate, you would normally do so based on assuming that the number of ‘successes’ in the sample is a draw from a binomial distribution, which counts the number of successes in…

Following a recent request from someone, I’ve extended the functionality of my R package smcfcs, which performs multiple imputation of missing covariates, compatible with a user-specified substantive or outcome.

Competing risks and the Fine & Gray model In the setting of competing risks, one approach involves modelling the effects of covariates on the so-called cause specific hazard functions for each of the causes.

Reference-based imputation methods have become a popular approach to handling missing data in clinical trials after patients experience what is nowadays referred to as an intercurrent event.