New methodological solutions need to be straightforwardly demonstrated and implemented

We are keenly aware that new methodological solutions need to be straightforwardly demonstrated and implemented, by ourselves and by others, in order to be taken up by the wider research community. Therefore, we often develop user-written code for use with commonly-used statistical software to accompany our work.

nstage

Design of multi-arm,multi-stage (MAMS) trials for time-to-event outcomes

Specify the design (sample size, duration, overall operating characteristics) of a multi-arm, multi-stage (MAMS) trial utilizing an intermediate outcome (I-outcome) at the intermediate stages and a definitive or primary outcome (D-outcome) at the final stage, nstagebin for binary responses.

To install from within Stata:

. ssc install nstage

Main Publication

Tutorial

Stats Software: Stata

Authors: Royston P, Barthel S, Oskooei-Choodari B, Blenkinsop A, Bratton D

Maintainer: Babak Choodari-Oskooei, b.choodari-oskooei@ucl.ac.uk

Category: Clinical Trials METHODOLOGY

 

art (Assessment of Resources for Trials)

Complex sample size calculation in randomised trials

ART is a menu- and command-driven set of programs to compute sample size or power for randomized controlled trials with a time-to-event (ARTSURV) or binary (ARTBIN) outcome measure. ART accommodates complex features including non-proportional hazards, cross-over between treatments, loss to follow-up, staggered entry, flexible patient accrual patterns, and several different 'flavors' of the logrank test. Non-inferiority designs are supported. Multiple treatment groups with joint tests are allowed. Trend tests over dose levels of a covariate are supported. Projections of power and events (ARTPEP) are provided.

To install from within Stata:

. ssc install art

Main Publication

Tutorial

Stats Software: Stata

Authors: Babiker A, Bartel S, Royston P, Marley-Zagar E

Maintainer: Ella Marley-Zagar, e.marley-zagar@ucl.ac.uk

Category: Clinical Trials METHODOLOGY

 

artcat

Sample size calculation for an ordered categorical outcome

ARTCAT calculates sample size or power for a clinical trial or similar experiment with an ordered categorical outcome, where analysis is by the proportional odds model. The command implements an existing and a new method. The existing method is that of Whitehead (1993). The new method is based on creating a weighted data set containing the expected counts per person, and analysing it with 'ologit'.

To install from within Stata:

. net from http://www.homepages.ucl.ac.uk/~rmjwiww/stata/artcat

GitHub page:

https://github.com/UCL/artcat

Main Publication

Stats Software: Stata

Authors: White I, Marley-Zagar E, Morris T, Parmar MKB, Babiker AG

Maintainer: Ian White, ian.white@ucl.ac.uk

Category: Clinical Trials METHODOLOGY

 

network

Suite of programs for network meta-analysis

Imports data and runs contrast-based network meta-analysis (using MVMETA or METAREG), assesses inconsistency, and graphs the data and results. The data in each arm of each study are assumed to be available either as binomial counts (successes/total), or as mean, standard deviation and number of individuals for a quantitative variable.

To install from within Stata:

. ssc install network

or:

. net from http://www.homepages.ucl.ac.uk/~rmjwiww/stata/meta

Main Publication

Tutorial

Stats Software: Stata

Author / Maintainer: Ian White, ian.white@ucl.ac.uk

Category: META-ANALYSIS

 

mvmeta

multivariate random-effects meta-analysis

Standard meta-analysis combines estimates of one parameter over several studies.  Multivariate meta-analysis is an extension that can combine estimates of several related parameters. MVMETA performs maximum likelihood, restricted maximum likelihood or method of moments estimation of random-effects multivariate meta-analysis models.

To install from within Stata:

. ssc install mvmeta

or:

. net from http://www.homepages.ucl.ac.uk/~rmjwiww/stata/meta

Main Publication

Stats Software: Stata

Author / Maintainer: Ian White, ian.white@ucl.ac.uk

Category: META-ANALYIS

 

rctmiss

Analysis of an RCT allowing for informatively missing outcome data

RCTMISS analyses a randomised control trial with missing outcome data under a range of assumptions about the missing data. The data and missingness are modelled jointly using either a pattern-mixture model or a selection model. Assumptions about the missing data are expressed via a sensitivity parameter delta which measures the degree of departure from missing at random.

To install from within Stata:

. ssc install rctmiss

or:

. net from http://www.homepages.ucl.ac.uk/~rmjwiww/stata/missing

GitHub page:

https://github.com/UCL/rctmiss

Main Publication

Stats Software: Stata

Author / Maintainer: Ian White, ian.white@ucl.ac.uk

Category: Clinical Trials METHODOLOGY

 

metan

A comprehensive meta-analysis package for aggregate (summary) data

Meta-analysis is a statistical technique for combining results from multiple independent studies, with the aim of estimating a single overall effect. The routines in this package provide facilities to conduct meta-analyses of binary (event) or continuous data from two groups, or intervention effect estimates with corresponding standard errors or confidence intervals. Recent updates include a wide range of random-effects models, cumulative and influence analysis, and meta-analysis of proportions. Also included is a separate routine for constructing forest plots (‘forestplot’ command).

To install from within Stata:

. ssc install metan

GitHub page:

https://github.com/UCL/metan

Stats Software: Stata

Authors: Fisher D, Harris R, Bradburn M, Deeks J, Harbord R, Altman D, Steichen T, Sterne J, Higgins J

Maintainer: David Fisher, d.fisher@ucl.ac.uk

Category: META-ANALYSIS

 

ipdmetan

A routine for performing two-stage IPD meta-analysis

Two-stage meta-analysis works by first fitting a specified model to the data within each of a series of studies in turn. The coefficient of interest (typically the treatment effect coefficient) is then extracted, and is passed to 'metan' for analysis. Aggregate data may also be included from an external dataset, to be analysed alongside the IPD. Also included is the package 'ipdover' which is designed to be used for producing subgroup plots from within a single randomized trial.

To install from within Stata:

. ssc install ipdmetan

Main Publication

Stats Software: Stata

Author / Maintainer: David Fisher, d.fisher@ucl.ac.uk

Category: META-ANALYSIS

 

mimix

Reference-based multiple imputation, sensitivity analysis of longitudinal trials with protocol deviation

MIMIX imputes missing numerical outcomes for a longitudinal trial with protocol deviation under distinct treatment arm-based assumptions for the unobserved data, following the general algorithm of Carpenter, Roger, and Kenward (2013; see "Main Publication" link below).

To install from within Stata:

. ssc install mimix

Main Publication

Stats Software: Stata

Authors: Cro S, Carpenter J, Kenward M

Maintainer: Suzie Cro, s.cro@imperial.ac.uk

Category: MISSING VALUES

 

jomo

Multilevel joint modelling multiple imputation

Similarly to Schafer's package 'pan', 'jomo' is a package for multilevel joint modelling multiple imputation. Novel aspects of 'jomo' are the possibility of handling binary and categorical data through latent normal variables, the option to use cluster-specific covariance matrices and to impute compatibly with the substantive model.

To install from within R:

> install.packages("jomo")

Main Publication

Stats Software: R

Authors: Quartagno M, Carpenter J

Maintainer: Matteo Quartagno, m.quartagno@ucl.ac.uk

Category: MISSING VALUES

 

dani

Tools to help design and analysis of non-inferiority and durations trials

The 'dani' package provides tools to help the design and analysis of resilient non-inferiority trials. These include functions for sample size calculations and analyses of trials, with either a risk difference, risk ratio or arc-sine difference margin, and a function to run simulations to design a trial with the methods described in Quartagno et al. (2019).

To install from within R:

> install.packages("dani")

Main Publication

Stats Software: R

Author / Maintainer: Matteo Quartagno, m.quartagno@ucl.ac.uk

Category: Clinical Trials METHODOLOGY

 

bcss

Create graphs to show how baseline data (prospective or retrospective) affects sample size for cluster randomised trials

The 'bcss' package displays graphs examining the impact of varying the amount of prospective/retrospective baseline data collection on the number of clusters required in a cluster randomised trial with different cluster autocorrelation and intra-cluster correlation values.

To install from within Stata:

. ssc install bcss

GitHub page:

https://github.com/UCL/bcss

Main Publication

Stats Software: Stata (Static charts); R (interactive)

Authors: Copas A, Marley-Zagar E, McGrath K

Maintainers: Ella Marley-Zagar, e.marley-zagar@ucl.ac.uk

Category: Clinical Trials METHODOLOGY

  

Other Stata software

Patrick Royston and Ian White have Stata software pages which contain a range of tools for statistical modelling, handling missing data, performing meta-analysis etc.

To access Patrick Royston’s page from within Stata:

. net from http://www.homepages.ucl.ac.uk/~ucakjpr/stata/

To access Ian White’s page from within Stata: 

. net from http://www.homepages.ucl.ac.uk/~rmjwiww/stata/