6 citations · 9 across the 6 of their papers we have counts for
6 papers
Interpreting Net Survival: What We Estimate Versus What We Think We Estimate
Matthew J. Smith
Net survival is conventionally defined as ``survival if cancer were the only possible cause of death'', an estimand corresponding to cancer-specific mortality alone. The Pohar Perm…
Performance of Cross-Validated Targeted Maximum Likelihood Estimation
Matthew J. Smith, Rachael V. Phillips, Camille Maringe +1
Background: Advanced methods for causal inference, such as targeted maximum likelihood estimation (TMLE), require certain conditions for statistical inference. However, in situatio…
Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review
Matthew J. Smith, Rachael V. Phillips, Miguel Angel Luque-Fernandez +1
The Targeted Maximum Likelihood Estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least…
Multiple imputation for logistic regression models: incorporating an interaction
Matthew J. Smith, Matteo Quartagno, Edmund Njeru Njagi
Background: Multiple imputation is often used to reduce bias and gain efficiency when there is missing data. The most appropriate imputation method depends on the model the analyst…
The Delta-Method and Influence Function in Medical Statistics: a Reproducible Tutorial
Rodrigo Zepeda-Tello, Michael Schomaker, Camille Maringe +5
Approximate statistical inference via determination of the asymptotic distribution of a statistic is routinely used for inference in applied medical statistics (e.g. to estimate th…
Tutorial: Introduction to computational causal inference using reproducible Stata, R and Python code
Matthew J. Smith, Camille Maringe, Bernard Rachet +4
The purpose of many health studies is to estimate the effect of an exposure on an outcome. It is not always ethical to assign an exposure to individuals in randomised controlled tr…