Shiny Application for expertsurv"

In expertsurv: Incorporate Expert Opinion with Parametric Survival Models

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>")

options(rmarkdown.html_vignette.check_title = FALSE)

R packages have many advantages, primarily allowing for easy sharing and documentation of code and ensuring code follows standardised conventions. Although R packages are more user friendly than a collection of functions, they still require the user to have a working knowledge of R something which most experts will not have.

As noted by [@Mikkola.2021], there is a need for tools which can embed the elicitation of expert opinion within the statistical workflow. One such tool is Shiny, an open-source R package that provides an elegant and powerful web framework for building web applications using R. Shiny can turn analyses conducted by R into interactive web applications without requiring HTML, CSS, or JavaScript knowledge [@Shiny.2023].

Learning how to interact with a webpage should be a much simpler task for experts and health economists not familiar with R. Furthermore, we can leverage R Markdown to create reproducible reports of the relevant outputs of the $\texttt{expertsurv}$ package in formats such as PDF, HTML and Word.

The tutorial below provides an overview of the steps required to elicit expert opinion on the survival at a timepoint of 20 months and incorporate these beliefs with survival data. To begin we simply run the following function $\texttt{elicit_surv}()$ which will open up a webpage.

In Figure \@ref(fig:expertsurv-1) we have the following steps to upload the data:

• Upload an excel file containing the survival data
• Select the columns referring to the time, status and arm (if jointly modelling treatment and comparator)
• Set the limit of the for the survival plot (ensuring that Choose opinion type as ''Survival at timepoint(s)''
• We assume that there are two experts who we will provide expert opinion
• Once the above parameters are defined select the ''Plot/Update Survival Curves and Expert Opinions''

knitr::include_graphics("plots/ShinyExpertSurv-1.png")

In Figure \@ref(fig:expertsurv-2) we have the following steps to elicit the expert's opinions:

• We set the timepoint at which we elicit expert opinion to 20 months.
• Experts will be asked for their beliefs on the median survival at 20 months, the lower $2.5\%$ and upper $97.5\%$ probabilities of the population survival.

Once these steps are complete, we click the ''Plot/Update Survival Curves and Expert Opinions'' button.

knitr::include_graphics("plots/ShinyExpertSurv-2.png")

In Figure \@ref(fig:expertsurv-2) we have the following steps to run the analysis:

• Several other advanced options relating to the expert opinion are available in the checkbox; these include specifying the most likely value (MLV), the choice of pooling expert opinion (linear or logarithmic pooling - default linear pooling) and the parametric distribution to fit to each individual opinion (default is best fitting).
• We can select which treatment arm relates to the expert's belief (i.e. treatment or comparator).
• Selecting Run Analysis will conduct the analysis; and it should be noted that the Bayesian analysis can take a considerable amount of time.
• Once the analysis is complete we can download the $\texttt{expertsurv}$ object which can be loaded an R session later.
• We can also download the results as a report in one of three formats (HTML, PDF or Word) as shown in Figure \@ref(fig:expertsurv-4).

knitr::include_graphics("plots/ShinyExpertSurv-3.png")

knitr::include_graphics("plots/ShinyExpertSurv-4.png")

References

expertsurv documentation built on Oct. 5, 2023, 5:09 p.m.