Software
Developing software packages to implement our methods
To ensure that our novel methodological approaches can be implemented by the wider research community, we develop code to use in the statistical software packages Stata and R.
Below is a list of our openly available software packages to implement our methods.
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
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 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. In November 2021, ARTBIN underwent a major upgrade to provide sample size calculation for a range of trial types, with extended statistical tests and methods available.
To install from within Stata:
. ssc install art
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 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
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
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
Stats Software: Stata
Author / Maintainer: Ian White, ian.white@ucl.ac.uk
Category: 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
Stats Software: Stata
Author / Maintainer: Ian White, ian.white@ucl.ac.uk
Category: META-ANALYIS
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
Stats Software: Stata
Author / Maintainer: Ian White, ian.white@ucl.ac.uk
Category: Clinical Trials METHODOLOGY
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
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
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
Author / Maintainer: David Fisher, d.fisher@ucl.ac.uk
Category: META-ANALYSIS
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
Stats Software: Stata
Authors: Cro S, Carpenter J, Kenward M
Maintainer: Suzie Cro, s.cro@imperial.ac.uk
Category: MISSING VALUES
RefBasedMI is a porting of the Stata program MIMIX into the R environment, with extra functionality including options for the Causal model and Delta adjustment.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 R, first type:
> if(!require(devtools)) install.packages(devtools)
> library(devtools)
Then, the package itself may be installed by typing:
> devtools::install_github("UCL/RefBasedMI")
Stats Software: R
Author / Maintainer: Matteo Quartagno, m.quartagno@ucl.ac.uk
Category: MISSING VALUES
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")
Stats Software: R
Authors: Quartagno M, Carpenter J
Maintainer: Matteo Quartagno, m.quartagno@ucl.ac.uk
Category: MISSING VALUES
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")
Stats Software: R
Author / Maintainer: Matteo Quartagno, m.quartagno@ucl.ac.uk
Category: Clinical Trials METHODOLOGY
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
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
Patrick Royston and Ian White have Stata software pages that contain a range of tools for statistical modeling, 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/