Blog Archives

Five steps for missing data with Finalfit

August 30, 2018
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Five steps for missing data with Finalfit

As a journal editor, I often receive studies in which the investigators fail to describe, analyse, or even acknowledge missing data. This is frustrating, as it is often of the utmost importance. Conclusions may (and do) change when missing data is accounted for.  A few seem to not even appreciate that in conventional regression, only … Continue reading "Five...

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Finalfit now includes bootstrap simulation for model prediction

July 12, 2018
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Finalfit now includes bootstrap simulation for model prediction

If your new to modelling in R and don’t know what this title means, you definitely want to look into doing it. I’ve always been a fan of converting model outputs to real-life quantities of interest. For example, I like to supplement a logistic regression model table with predicted probabilities for a given set of … Continue reading "Finalfit...

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Finalfit now in CRAN

June 27, 2018
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Your favourite package for getting model outputs directly into publication ready tables is now available on CRAN. They make you work for it! Thank you to all that helped. The development version will continue to be available from github.

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Finalfit, knitr and R Markdown for quick results

May 22, 2018
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Finalfit, knitr and R Markdown for quick results

Thank you for the many requests to provide some extra info on how best to get finalfit results out of RStudio, and particularly into Microsoft Word. Here is how. Make sure you are on the most up-to-date version of finalfit. What follows is for demonstration purposes and is not meant to illustrate model building. … Continue reading "Finalfit,...

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Elegant regression results tables and plots in R: the finalfit package

May 16, 2018
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Elegant regression results tables and plots in R: the finalfit package

The finafit package brings together the day-to-day functions we use to generate final results tables and plots when modelling. I spent many years repeatedly manually copying results from R analyses and built these functions to automate our standard healthcare data workflow. It is particularly useful when undertaking a large study involving multiple different regression analyses. … Continue reading "Elegant...

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Install github package on safe haven server

May 5, 2018
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I’ve had few enquires about how to install the summarizer package on a server without internet access, such as the NHS Safe Havens. Upload summarizer-master.zip from here to server. Unzip. Run this: library(devtools) source = devtools:::source_pkg("summarizer-master") install(source)

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P-values from random effects linear regression models

January 13, 2018
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 is a useful frequentist approach to hierarchical/multilevel linear regression modelling. For good reason, the model output only includes t-values and doesn’t include p-values (partly due to the difficulty in estimating the degrees of freedom, as discussed here). Yes, p-values are evil and we should continue to try and expunge them from our analyses. But I

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An alternative presentation of the ProPublica Surgeon Scorecard

July 23, 2015
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An alternative presentation of the ProPublica Surgeon Scorecard

ProPublica, an independent investigative journalism organisation, have published surgeon-level complications rates based on Medicare data. I have already highlighted problems with the reporting of the data: surgeons are described as having a “high adjusted rate of complications” if they fall in the red-zone, despite there being too little data to say whether this has happened

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RStudio and GitHub

July 13, 2015
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RStudio and GitHub

Version control has become essential for me keeping track of projects, as well as collaborating. It allows backup of scripts and easy collaboration on complex projects. RStudio works really well with Git, an open source open source distributed version control system, and GitHub, a web-based Git repository hosting service. I was always forget how to

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Bayesian statistics and clinical trial conclusions: Why the OPTIMSE study should be considered positive

February 16, 2015
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Bayesian statistics and clinical trial conclusions: Why the OPTIMSE study should be considered positive

Statistical approaches to randomised controlled trial analysis The statistical approach used in the design and analysis of the vast majority of clinical studies is often referred to as classical or frequentist. Conclusions are made on the results of hypothesis tests with generation of p-values and confidence intervals, and require that the correct conclusion be drawn

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