Jonathan Bartlett

Imputing missing covariates in nested case-control and case cohort studies

June 11, 2018June 3, 2017 by Jonathan Bartlett

I’m pleased to announce a new version (1.3.0) of the smcfcs package for multiple imputation of missing covariates. Thanks to Ruth Keogh at the London School of Hygiene & Tropical Medicine, this new version features two additional functions, smcfcs.casecohort and smcfcs.nestedcc. These allow for imputing of missing covariates in case cohort and nested case-control studies respectively. A paper describing the methodology is forthcoming.

The package is now on CRAN and so can be installed/updated in the usual way from R or RStudio.

There are of course various papers on the case cohort and nested case control study designs. For further reading, I’d recommend looking at Ruth’s book, co-authored with David Cox, ‘Case-Control Studies’, which contains a chapter on each design.

11/06/2018 – the corresponding paper has now been published in Biometrics.

Odds ratios, collapsibility, marginal vs. conditional, GEE vs GLMMs

May 11, 2017 by Jonathan Bartlett

Generalised estimating equations (GEEs) and generalised linear mixed models (GLMMs) are two approaches to modelling clustered or longitudinal categorical outcomes. Here I will focus on the common setting of a binary outcome. As is commonly described, the two approaches estimate different effect measures, with GEEs targeting so called marginal effects, and GLMMs targeting conditional or subject specific effects. Understanding the difference between these is potentially quite tricky I think.

‘Monte-Carlo sensitivity analysis’ recommended against

February 27, 2020April 9, 2017 by Jonathan Bartlett

McCandless & Gustafson have just published an interesting paper that is available Early View at Statistics in Medicine. They compare a conventional Bayesian analysis to so called ‘Monte-Carlo sensitivity analysis’ for the problem of assessing sensitivity of an exposure effect to unmeasured confounding.