Computer program, written in Stata®, that can be used to carry out relative survival analyses. The program first estimates the net mortality rates for subjects, in each of a set of predefined intervals following the initial event, for example diagnosis of cancer, using the maximum likelihood method (Estève et al., 1990).
Fit an (excess) hazard regression model using different shapes for the baseline hazard (Weibull, piecewise constant and B-splines), with the possibility to include time-dependent and/or non-linear effect(s) of variable(s) and a random effect defined at the cluster level.
Sub-national life tables have been smoothed either by applying Ewbank’s 4-parameter model life table system to the observed mortality rates with the English Life Table 1991 as standard (archive), or by a Poisson model (published in 2009). You can perform the Ewbank procedure with your own data using our Stata® command ewblft.
This is a Stata program implementing the targeted maximum likelihood estimation for the ATE for a binary or continuous outcome and binary treatment. eltmle includes the use of a “Super Learner” called from the SuperLearner package v.2.0-21 (Polley E., et al. 2011).
When estimating the average effect of a binary treatment (or exposure), methods that incorporate propensity scores, the G-formula, or targeted maximum likelihood estimation (TMLE) are preferred over naïve regression approaches which are biased under misspecification of a parametric outcome model.
Age Standardised Net Survival Estimation
Step-by-step guidance to the age standardisation of net survival using the stns program and the International Cancer Survival Standard (ICSS) weights, including the appropriate formulae for the estimation of age standardised net survival and associated standard errors.
Funnel plots were developed to enable comparison of institutional performance whilst showing the precision of each measure. We first used them to show variation between surgeons and over time in the proportion of women referred for…
Matched case-control studies are a classical epidemiological study design. Case-control designs are used to estimate the odds ratio for a disease given exposure to a specific risk factor. The odds ratio is a good estimate of the relative risk, especially when the disease is rare.
cvAUROC implements k-fold cross-validation for the AUC for a binary outcome after fitting a logistic regression model.