htasurv: a Stata module for performing survival analysis in economic evaluations

Jan 2, 2017

htasurv is an open source Stata module for assessing alternative parametric distributions when extrapolating survival data for use in health economic models. The function distfind loops through alternative distributions (specified by the user) and reports statistics and produces plots specified in NICE DSU TSD 14.0. The function distanalysis writes out results and useful statistics (e.g. the variance-covariance matrix) for the analyst. The module can be installed directly from github, using the github module. Once the github module is installed, installation of htasurv is:

github install sourceHEOR/htasurv

Syntax
For distfind:

distfind [varlist], dlist(string) timevar(varname) failure(varname) [GRaphs]

Where varlist are the variables in the survival model (often treatment), timevar is the variable defining the time-to-event, and failure is a binary variable for failure vs censoring (1=failure, 0=censored). If the graphs option is used, plots will be saved to curentdirectory/graphs. dlist is the list of distributions to estimate as lowercase strings (see example below). For distanalysis:

distanalysis [varlist], sdist(string) doctitle(string) [caption(string)] [fname(string)]

distanalysis will estimate the model with variables given in varlist with distribution given in sdist (full title; all lowercase). The resulting model will be written to .csv and .rtf files with file names given by doctitle. If a folder location is specified by fname, all files will be stored there (otherwise, the current directory is used).

Example use

sysuse cancer.dta, clear

global dlist “gamma weibull gompertz exponential lognormal loglogistic”

distfind age i.drug, dlist($dlist) timevar(studytime) failure(died)

distanalysis age i.drug, sdist(gompertz) doctitle(test) caption(“Gompertz”)

The software can be downloaded here. This software is released under the GNU General Public License version 3.

More Insights

NICE RWE framework 

NICE RWE framework 

Previously, we discussed the use of real-world evidence (RWE) in the reimbursement assessment of medical devices. In this latest blog, we present an overview of the RWE framework created by the National Institute for Health and Care Excellence ... Read more

The 100,000 genomes project: paving the way for faster diagnosis and treatment of rare diseases

The 100,000 genomes project: paving the way for faster diagnosis and treatment of rare diseases

What are rare diseases?  In the  European Union, a rare disease is defined as a disorder affecting ≤5 in 10,000 persons  (1). Using this definition, the global population prevalence of rare diseases is estimated to be between 3.5–‍5.9% ... Read more

Use of RWE in the reimbursement assessment of medical devices

Use of RWE in the reimbursement assessment of medical devices

Hierarchy of evidence  Different study designs are often ranked in a hierarchy of evidence based on their validity and robustness. Randomised controlled trials (RCTs) are typically considered the gold standard source of evidence, ... Read more

The growing number of small interfering RNA-based therapies approved for reimbursement in the UK

The growing number of small interfering RNA-based therapies approved for reimbursement in the UK

The RNA interference pathway  Ribonucleic acid (RNA) is an important macromolecule in cells, involved in turning genetic information encoded by genes (made up of deoxyribonucleic acid ), into proteins needed by the cell. During the processes of ... Read more