ready4 – Reporting

Docs: Examples

Thu, 05 Jan 2017 00:00:00 +0000

An scientific summary of the ready4 prototype software framework and its early application in youth mental health is available as an open-access article in PharmacoEconomics.

Additional information is available from the project website of readyforwhatsnext - a modular and open source economic model of youth mental health.

Docs: Subroutine templates for authoring analysis reports

Thu, 05 Jan 2017 00:00:00 +0000

Docs: Find themed collections of ready4 modules

Wed, 28 Jan 2026 00:00:00 +0000

This below section renders a vignette article from the ready4 library. You can use the following links to:

view the vignette on the library website (adds useful hyperlinks to code blocks)
view the source file from that article, and;
edit its contents (requires a GitHub account).

library(ready4)

Motivation

To use and combine health economic model modules it is first useful to find modules that have been developed using a common framework that promotes inter-operability.

Implementation

A table summarising currently available module libraries authored with the ready4 framework within a specified GitHub organisation can be retrieved from an online dataset by using the get_libraries_tb and update_libraries_tb functions.

Use

In the below example we will search for modules from the readyforwhatsnext model. The source code for these modules all reside in the ready4-dev GitHub repository. The value supplied to the gh_repo_1L_chr argument specifies the repository in which a dataset of readyforwhatsnext module libraries is stored.

libraries_tb <- get_libraries_tb(gh_repo_1L_chr = "ready4-dev/ready4") %>% update_libraries_tb(include_1L_chr = "modules", url_stub_1L_chr = "https://ready4-dev.github.io/")

In this example, module libraries have been grouped in to the following thematic model “sections”.

libraries_tb$Section %>% unique()
#> [1] "People"   "Places"   "Programs"

We can use the print_packages function to display a HTML summary of the module libraries currently available for each section. The resulting table summarises the types of module library (e.g. those for description, developing models or predicting with models), the name and purpose of those libraries, the locations from which development and archived library code can be downloaded and details of supporting documentation for each library (e.g. website, manuals and examples).

print_packages(libraries_tb, sections_chr = "People")

Purpose	Documentation	Code	Examples
Describe and Validate Youth Mental Health Dataset Variables	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive	12, 13
Score Multi-Attribute Utility Instruments	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive	14, 15
Model Youth Choice Behaviours	Citation , Website , Citation	Dev , Archive
Implement Transfer to Utility Mapping Algorithms	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive	16
Explore and Characterise Heterogeneity in Quality of Life Data	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive
Specify Models to Solve Inverse Problems	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive	17
Transform Youth Outcomes to Health Utility Predictions	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive	18

print_packages(libraries_tb, sections_chr = "Places")

Type	Package	Purpose	Documentation	Code	Examples
		Model Australian Spatial Data	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive
		Model Spatial Features of Health Systems	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive

print_packages(libraries_tb, sections_chr = "Programs")

Type	Package	Purpose	Documentation	Code	Examples
		Undertake Health Economic Budget Impact Analysis.	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive
		Develop, Use and Share Unit Cost Datasets for Health Economic	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive	19, 20

Details of how to search for details on individual modules is described in another article.

Docs: Commands for using model modules

Wed, 28 Jan 2026 00:00:00 +0000

This below section renders a vignette article from the ready4show library. You can use the following links to:

view the vignette on the library website (adds useful hyperlinks to code blocks)
view the source file from that article, and;
edit its contents (requires a GitHub account).

library(ready4)

Motivation

To be used in health economic analyses, model modules need to be called using a programming syntax. Ideally that syntax should be relatively simple, with the name and description of each command reliably communicating the category of operations it performs.

Implementation

ready4 provides a simple syntax that is used by all model modules developed with the ready4 framework.

Use

A table that itemises ready4 commands along with examples of how these commands are used can be ingested from a periodically updated database using get_methods_tb. In the below example we will search for examples of where that syntax has been used by modules from the readyforwhatsnext model. The value supplied to the gh_repo_1L_chr argument specifies the repository in which a dataset of readyforwhatsnext module libraries is stored.

x <- get_methods_tb(gh_repo_1L_chr = "ready4-dev/ready4")

Core commands

A HTML table of ready4’s core commands and examples of the use of each command can be displayed using the print_methods function, using the return_1L_chr = "core" argument.

print_methods(x,
              return_1L_chr = "core",
              scroll_width_1L_chr = "100%")

Method	Purpose	Examples
author	Author and save files	5 , 6, 7 , 16 , 17
characterize	Characterize model module data by generating (tabular) descriptive statistics
depict	Depict (plot) features of model module data	13, 14 , 15
enhance	Enhance a model module by adding new elements
exhibit	Exhibit features of model module data by printing them to the R console	2 , 5 , 6, 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20
ingest	Ingest data	1 , 2 , 3 , 6, 13 , 14 , 15 , 16 , 17 , 18
investigate	Investigate solutions to an inverse problem	16 , 17
manufacture	Manufacture a new object
metamorphose	Metamorphose a model module to a model module of a different (non-inheriting) class	17
procure	Procure items from a dataset	5, 17
prognosticate	Prognosticate (make predictions) by solving a forward problem
ratify	Ratify that input or output data meet validity criteria	13, 17 , 19 , 20
reckon	Reckon (calculate) a value
renew	Renew (update) values	1 , 2 , 13, 14 , 15 , 16 , 17 , 19 , 20
share	Share data via an online repository	1 , 13, 14 , 15 , 16

Applying commands to module “slots”

Each of the “core” commands also has a “slot” version, which applies the command to a specified slot (a named element of a module). Two of these “slot” methods can also be used for additional purposes:

procureSlot is a “getter” method - its default behaviour is to return the value of a specified slot. If the argument use_procure_mthd_1L_lgl = T is included in the method call, procureSlot will instead apply the procure method to a specified slot.
renewSlot is a “setter” method - if any value other than “use_renew_mthd” (the default) is passed to the new_val_xx argument, that value will be assigned to the specified slot.

A HTML table of slot commands and relevant examples can be displayed using the print_methods function, using the return_1L_chr = "slot" argument.

print_methods(x,
              return_1L_chr = "slot",
              scroll_width_1L_chr = "100%")

Method	Purpose	Examples
authorSlot	Apply the author method to a model module slot
characterizeSlot	Apply the characterize method to a model module slot
depictSlot	Apply the depict method to a model module slot
enhanceSlot	Apply the enhance method to a model module slot
exhibitSlot	Apply the exhibit method to a model module slot	4, 17 , 19 , 20
ingestSlot	Apply the ingest method to a model module slot
investigateSlot	Apply the investigate method to a model module slot
manufactureSlot	Apply the manufacture method to a model module slot
metamorphoseSlot	Apply the metamorphose method to a model module slot
procureSlot	Procure (get) data from a slot	4, 14 , 16 , 17
prognosticateSlot	Apply the prognosticate method to a model module slot
ratifySlot	Apply the ratify method to a model module slot
reckonSlot	Apply the reckon method to a model module slot
renewSlot	Renew (set) the values of data in a module slot	4, 14 , 17 , 19 , 20
shareSlot	Apply the share method to a model module slot

Extended commands

Finally, there are a small number of other commands that are more general extensions of the core commands. Currently, these extended commands are all variants on the author command, with each extension specifying the type of output to be authored by the method. A HTML table of the extended generics bundled with ready4 can be displayed using the print_methods function, using the return_1L_chr = "extended" argument.

print_methods(x,
              exclude_mthds_for_chr = "Ready4Module",
              return_1L_chr = "extended",
              scroll_width_1L_chr = "100%")

Method	Purpose	Examples
authorClasses	Author and document classes
authorData	Author and document datasets	4, 17
authorFunctions	Author and document functions
authorReport	Author and save a report	4

Docs: Finding modules of health economic models

Thu, 05 Jan 2017 00:00:00 +0000

Docs: Find examples of how model modules are used

Wed, 28 Jan 2026 00:00:00 +0000

This below section renders a vignette article from the ready4 library. You can use the following links to:

view the vignette on the library website (adds useful hyperlinks to code blocks)
view the source file from that article, and;
edit its contents (requires a GitHub account).

library(ready4)

Motivation

To use and combine health economic model modules it is first useful to find modules that have been developed using a common framework that promotes inter-operability.

Implementation

Use

libraries_tb <- get_libraries_tb(gh_repo_1L_chr = "ready4-dev/ready4") %>% update_libraries_tb(include_1L_chr = "modules", url_stub_1L_chr = "https://ready4-dev.github.io/")

In this example, module libraries have been grouped in to the following thematic model “sections”.

libraries_tb$Section %>% unique()
#> [1] "People"   "Places"   "Programs"

print_packages(libraries_tb, sections_chr = "People")

Purpose	Documentation	Code	Examples
Describe and Validate Youth Mental Health Dataset Variables	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive	12, 13
Score Multi-Attribute Utility Instruments	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive	14, 15
Model Youth Choice Behaviours	Citation , Website , Citation	Dev , Archive
Implement Transfer to Utility Mapping Algorithms	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive	16
Explore and Characterise Heterogeneity in Quality of Life Data	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive
Specify Models to Solve Inverse Problems	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive	17
Transform Youth Outcomes to Health Utility Predictions	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive	18

print_packages(libraries_tb, sections_chr = "Places")

Type	Package	Purpose	Documentation	Code	Examples
		Model Australian Spatial Data	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive
		Model Spatial Features of Health Systems	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive

print_packages(libraries_tb, sections_chr = "Programs")

Type	Package	Purpose	Documentation	Code	Examples
		Undertake Health Economic Budget Impact Analysis.	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive
		Develop, Use and Share Unit Cost Datasets for Health Economic	Citation , Website , Manual - Short (PDF) , Manual - Full (PDF)	Dev , Archive	19, 20

Details of how to search for details on individual modules is described in another article.

Docs: Authoring scientific manuscripts

Wed, 28 Jan 2026 00:00:00 +0000

This below section renders a vignette article from the ready4show library. You can use the following links to:

view the vignette on the library website (adds useful hyperlinks to code blocks)
view the source file from that article, and;
edit its contents (requires a GitHub account).

Motivation

Open science workflows should ideally span an unbroken chain between data-ingest to production of a scientific summary such as a manuscript. Such extensive workflows provide an explicit means of linking all content in a scientific summary with the analysis that it reports.

Implementation

ready4show includes a number of classes and methods that help integrate manuscript authoring into a reproducible workflow. These tools are part of the ready4 framework for transparent, reusable and updatable health economic models.

Load required libraries

We first begin by loading the libraries we will require to implement this workflow.

library(ready4)
library(ready4show)
library(bookdown)
library(rticles)

By default, methods in the ready4show package will request your consent before writing files to your machine. This is the safest option. However, as there are many files that need to be written locally for this program to execute, you can overwrite this default by supplying the value “Y” to methods with a consent_1L_chr argument.

consent_1L_chr <- "" # Default value - asks for consent prior to writing each file.

Create a synopsis of the manuscript to be authored

To start with we create X, an instance of Ready4showSynopsis, a ready4 module (S4 class). We can use X to record metadata about the manuscript to be authored (including details about the study being summarised and the title and format of the intended output).

X <- Ready4showSynopsis(background_1L_chr = "Our study is entirely fictional.",
                        coi_1L_chr = "None declared.",
                        conclusion_1L_chr = "These fake results are not interesting.",
                        digits_int = 3L,
                        ethics_1L_chr = "The study was reviewed and granted approval by Awesome University's Human Research Ethics Committee (1111111.1).",
                        funding_1L_chr = "The study was funded by Generous Benefactor.",
                        interval_chr = "three months",
                        keywords_chr = c("entirely","fake","do", "not","cite"),
                        outp_formats_chr = "PDF",
                        sample_desc_1L_chr = "The study sample is fake data that pretends to be young people aged 12 to 25 years who attended Australian primary care services for mental health related needs between November 2019 to August 2020.",
                        title_1L_chr = "A hypothetical study using fake data")

Add authorship details

Authorship details can be added to slots of X that contain ready4show_authors and ready4show_instututes ready4 sub-modules.

As we can see from the below call to exhibitSlot, X was created with no authorship information.

exhibitSlot(X,
            "authors_r3",
            scroll_box_args_ls = list(width = "100%"))

First-name	Middle-name	Last-name	Title	Qualifications	Institutes	Sequence Position	Corresponding	Email	Joint-first

We can add details on each author by repeated calls to the renewSlot method.

X <- renewSlot(X,
          "authors_r3",
          first_nm_chr = "Alejandra",
          middle_nm_chr = "Rocio",
          last_nm_chr = "Scienceace",
          title_chr = "Dr",
          qualifications_chr = "MD, PhD",
          institute_chr = "Institute_A, Institute_B",
          sequence_int = 1,
          is_corresponding_lgl = T,
          email_chr = "fake_email@fake_institute.com") %>%
  renewSlot("authors_r3",
            first_nm_chr = "Fionn",
            middle_nm_chr = "Seamus",
            last_nm_chr = "Researchchamp",
            title_chr = "Prof",
            qualifications_chr = "MSc, PhD",
            institute_chr = "Institute_C, Institute_B",
            sequence_int = 2,
            email_chr = "fake_email@unreal_institute.com")

The updated authorship table can now be inspected.

X %>%
  exhibitSlot("authors_r3",
              scroll_box_args_ls = list(width = "100%"))

First-name	Middle-name	Last-name	Title	Qualifications	Institutes	Sequence Position	Corresponding	Email	Joint-first
Alejandra	Rocio	Scienceace	Dr	MD, PhD	Institute_A, Institute_B	1	TRUE	\_institute.com	NA
Fionn	Seamus	Researchchamp	Prof	MSc, PhD	Institute_C, Institute_B	2	NA	\_institute.com	NA

We now need to add additional information for each author institute.

X <- renewSlot(X,
          "institutes_r3",
          short_name_chr = "Institute_A", 
          long_name_chr = "Awesome University, Shanghai") %>%
  renewSlot("institutes_r3",
            short_name_chr = "Institute_B", 
            long_name_chr = "August Institution, London") %>%
  renewSlot("institutes_r3",
            new_val_xx = "use_renew_mthd",
            short_name_chr = "Institute_C", 
            long_name_chr = "Highly Ranked Uni, Montreal")

The updated institutes table can now be inspected.

X %>%
  exhibitSlot("institutes_r3",
              scroll_box_args_ls = list(width = "100%"))

Reference	Name
Institute_A	Awesome University, Shanghai
Institute_B	August Institution, London
Institute_C	Highly Ranked Uni, Montreal

Add correspondences

We can also add a look-up table about any changes we wish to make from the analysis code of how names of variables / parameters are presented in the manuscript text.

X <- renewSlot(X,
               "correspondences_r3",
               old_nms_chr = c("PHQ9", "GAD7"),
               new_nms_chr = c("PHQ-9", "GAD-7"))

These edits can now be inspected with a call to exhibitSlot.

X %>%
  exhibitSlot("correspondences_r3",
              scroll_box_args_ls = list(width = "100%")) # Add Exhibit Method

Old name	New name
PHQ9	PHQ-9
GAD7	GAD-7

Specify output directory

We now update X with details of the directory to which we wish to write the manuscript we are authoring and all its supporting files.

X <- renewSlot(X,
               "a_Ready4showPaths@outp_data_dir_1L_chr",
               new_val_xx = tempdir())

Create dataset of literate programming files

Our next step is to copy a dataset of files that can implement a literate program to generate our manuscript. If you have a template you wish to work with, you can specify its local path using the a_Ready4showPaths@mkdn_source_dir_1L_chr slot of the X. Skip this step if you wish to use the default markdown dataset, which leverages popular rmarkdown toolkits such as bookdown and rticles.

## Not run
# procureSlot(X,
#             "a_Ready4showPaths@mkdn_source_dir_1L_chr",
#             new_val_xx  = "PATH TO MARKDOWN DATASET")

We create the dataset copy with the authorData method.

authorData(X, consent_1L_chr = consent_1L_chr)

Having created a local copy of the template literate program files dataset, it is now possible to manually edit the markdown files to author the manuscript. However, in this example we are skipping this step and will continue to use the unedited template in conjunction with the metadata we have specified in X. We combine the two to author a manuscript using the authorReport method.

authorReport(X, consent_1L_chr = consent_1L_chr)

If we wish, we can now ammend X and then rerun the authorReport method to generate Word and HTML versions of the manuscript.

renewSlot(X,
          "outp_formats_chr",
          new_val_xx = "Word") %>%
  authorReport(consent_1L_chr = consent_1L_chr)

renewSlot(X,
          "outp_formats_chr",
          new_val_xx = "HTML") %>%
  authorReport(consent_1L_chr = consent_1L_chr)

The outputed files are as follows:

Docs: Using model modules for repeatable analyses

Thu, 05 Jan 2017 00:00:00 +0000

Docs: Implement a utility mapping study

Fri, 20 Oct 2023 00:00:00 +0000

This below section renders a vignette article from the TTU library. You can use the following links to:

view the vignette on the library website (adds useful hyperlinks to code blocks)
view the source file from that article, and;
edit its contents (requires a GitHub account).

Note: This vignette uses fake data - it is for illustrative purposes only and should not be used to inform decision making. This vignette outlines the workflow for developing utility mapping models using longitudinal data. The workflow for developing utility mapping models is broadly similar, with some minor modifications. An example of developing models using cross-sectional data is available at https://doi.org/10.5281/zenodo.8098595 .

Motivation

Health services do not typically collect health utility data from their clients, which makes it more difficult to place an economic values on outcomes attained in these services. One strategy for addressing this gap is to use data from similar samples of patients that contain both health utility and the types of outcome measures that are collected in clinical services. The TTU package provides a toolkit for conducting and reporting a utility mapping (or Transfer to Utility) study.

Implementation

The TTU package contains modules of the ready4 youth mental health economic model that combine and extend model modules for:

labeling, validating and summarising youth mental health datasets (from the youthvars package);
scoring health utility (from the scorz package);
specifying and testing statistical models (from the specific package);
generating reproducible analysis reports (from the ready4show package); and
sharing data via online data repositories (from the ready4use package).

Additionally, TTU relies on two RMarkdown programs:

ttu_mdl_ctlg: Generate a Template Utility Mapping (Transfer to Utility) Model Catalogue (https://doi.org/10.5281/zenodo.5936870)
ttu_lng_ss: Create a Draft Scientific Manuscript For A Utility Mapping Study (https://doi.org/10.5281/zenodo.5976987)

Outputs generated by the TTU package are designed to be compatible with health economic models developed with the ready4 framework).

Workflow

Background and citation

The following workflow illustrates (using fake data) the same steps we used in a real world study, a summary of which is available at https://doi.org/10.1101/2021.07.07.21260129). Citation information for that study is:

@article {Hamilton2021.07.07.21260129,
    author = {Hamilton, Matthew P and Gao, Caroline X and Filia, Kate M and Menssink, Jana M and Sharmin, Sonia and Telford, Nic and Herrman, Helen and Hickie, Ian B and Mihalopoulos, Cathrine and Rickwood, Debra J and McGorry, Patrick D and Cotton, Sue M},
    title = {Predicting Quality Adjusted Life Years in young people attending primary mental health services},
    elocation-id = {2021.07.07.21260129},
    year = {2021},
    doi = {10.1101/2021.07.07.21260129},
    publisher = {Cold Spring Harbor Laboratory Press},
    URL = {https://www.medrxiv.org/content/early/2021/07/12/2021.07.07.21260129},
    eprint = {https://www.medrxiv.org/content/early/2021/07/12/2021.07.07.21260129.full.pdf},
    journal = {medRxiv}
}

The program applied in that study, which this workflow closely resembles is available at https://doi.org/10.5281/zenodo.6116077 and can be cited as follows:

@software{hamilton_matthew_2022_6212704,
  author       = {Hamilton, Matthew and
                  Gao, Caroline},
  title        = {{Complete study program to reproduce all steps from 
                   data ingest through to results dissemination for a
                   study to map mental health measures to AQoL-6D
                   health utility}},
  month        = feb,
  year         = 2022,
  note         = {{Matthew Hamilton and Caroline Gao  (2022). 
                   Complete study program to reproduce all steps from
                   data ingest through to results dissemination for a
                   study to map mental health measures to AQoL-6D
                   health utility. Zenodo.
                   https://doi.org/10.5281/zenodo.6116077. Version
                   0.0.9.3}},
  publisher    = {Zenodo},
  version      = {0.0.9.3},
  doi          = {10.5281/zenodo.6212704},
  url          = {https://doi.org/10.5281/zenodo.6212704}
}

Load required packages

We begin by loading our required packages.

library(ready4)
library(ready4show)
library(ready4use)
library(youthvars)
library(scorz)
library(TTU)

By default, methods associated with TTU modules will request your consent before writing files to your machine. This is the safest option. However, as there are many files that need to be written locally for this program to execute, you can overwrite this default by supplying the value “Y” to methods with a consent_1L_chr argument.

consent_1L_chr <- "" # Default value - asks for consent prior to writing each file.

Add dataset metadata

We use the Ready4useDyad and Ready4useRepos modules to retrieve and ingest and to then pair a dataset and its data dictionary.

A <- Ready4useDyad(ds_tb = Ready4useRepos(dv_nm_1L_chr = "fakes", dv_ds_nm_1L_chr = "https://doi.org/10.7910/DVN/HJXYKQ", dv_server_1L_chr = "dataverse.harvard.edu") %>%
                     ingest(fls_to_ingest_chr = c("ymh_clinical_tb"), metadata_1L_lgl = F) %>% youthvars::transform_raw_ds_for_analysis(),
                   dictionary_r3 = Ready4useRepos(dv_nm_1L_chr = "TTU", dv_ds_nm_1L_chr = "https://doi.org/10.7910/DVN/DKDIB0", dv_server_1L_chr = "dataverse.harvard.edu") %>%
                     ingest(fls_to_ingest_chr = c("dictionary_r3"), metadata_1L_lgl = F)) %>%
  renew(type_1L_chr = "label")

We use the YouthvarsSeries module to supply metadata about our longitudinal dataset vignette.

A <- YouthvarsSeries(a_Ready4useDyad = A, id_var_nm_1L_chr = "fkClientID", timepoint_var_nm_1L_chr = "round",
                     timepoint_vals_chr = levels(procureSlot(A, "ds_tb")$round))

Score health utility

We next use the ScorzAqol6Adol module to score adolescent AQoL-6D health utility.

A <- TTUProject(a_ScorzProfile = ScorzAqol6Adol(a_YouthvarsProfile = A))
A <- renew(A, what_1L_chr = "utility") 
#> Joining with `by = join_by(fkClientID, match_var_chr)`

Evaluate candidate models

Over the next few steps we will use modules from the specific package to specify and assess a number of candidate utility mapping models.

Specify modelling parameters

We begin by specifying the parameters we will use in our modelling project. The initial step is to ensure the fields in A for storing parameter values are internally consistent with the data we have entered in the previous steps.

A <- renew(A, what_1L_chr = "parameters")

We next ingest a lookup table of metadata about the variables we plan to explore as candidate predictors. In this case, we are sourcing the lookup table from an online data repository.

A <- renew(A, "use_renew_mthd", fl_nm_1L_chr = "predictors_r3", type_1L_chr = "predictors_lup", 
           y_Ready4useRepos = Ready4useRepos(dv_nm_1L_chr = "TTU", dv_ds_nm_1L_chr = "https://doi.org/10.7910/DVN/DKDIB0", 
                                             dv_server_1L_chr = "dataverse.harvard.edu"),
           what_1L_chr = "parameters")

We can inspect the metadata on candidate predictors that we have just ingested.

exhibit(A, scroll_box_args_ls = list(width = "100%"))

We add additional metadata about variables in our dataset that will be used in exploratory modelling.

A <- renew(A, c(0.03,1), type_1L_chr = "range", what_1L_chr = "parameters") %>%
  renew(c("BADS","GAD7", "K6", "OASIS", "PHQ9", "SCARED"),
        type_1L_chr = "predictors_vars", what_1L_chr = "parameters") %>%
  renew(c("d_sex_birth_s", "d_age",  "d_sexual_ori_s", "d_studying_working", "c_p_diag_s", "c_clinical_staging_s", "SOFAS"),     
        type_1L_chr = "covariates", what_1L_chr = "parameters") %>%
  renew(c("d_age","Gender","d_relation_s", "d_sexual_ori_s" ,"Region", "d_studying_working", "c_p_diag_s", "c_clinical_staging_s","SOFAS"), 
        type_1L_chr = "descriptives", what_1L_chr = "parameters") %>%
  renew("d_interview_date", type_1L_chr = "temporal", what_1L_chr = "parameters")

We record that the data we are working with is fake (this step can be skipped if working with real data).

A <- renew(A, T, type_1L_chr = "is_fake", what_1L_chr = "parameters")

We update A for internal consistency with the values we have previously supplied and create a local workspace to which output files will be written.

A <- renew(A, consent_1L_chr = consent_1L_chr, paths_chr = tempdir(), what_1L_chr = "project")

We now generate tables and charts that describe our dataset. These are saved in a sub-directory of our output data directory, and are available for download. One of the plots is also reproduced here.

A <- author(A, consent_1L_chr = consent_1L_chr, digits_1L_int = 3L, what_1L_chr = "descriptives")

We next compare the performance of different model types. We perform this step using the investigate method. This is the first of several times that we use this method. Each time the method is called A is updated to that the next time the method is called, a different algorithm will be used. The sequence of calls to investigate is therefore important (it should be in the same order as outlined in this example and you should not attempt to repeat a call to investigate to redo a prior step).

A <- investigate(A, consent_1L_chr = consent_1L_chr, depnt_var_max_val_1L_dbl = 0.9999, session_ls = sessionInfo())

The outputs of the previous command are saved into a sub-directory of our output directory. An example of this output is available for download). Once we inspect this output, we can then specify the preferred model types to use from this point onwards.

A <- renew(A, c("GLM_GSN_LOG", "OLS_CLL"), type_1L_chr = "models", what_1L_chr = "results")

Next we assess multiple versions of our preferred model type - one single predictor model for each of our candidate predictors and the same models with candidate covariates added.

A <- investigate(A, consent_1L_chr = consent_1L_chr)

The previous step saved output into a sub-directory of our output directory. Example output is available for download: (single predictor comparisons) and multivariate model comparisons. After reviewing this output, we can specify the covariates we wish to add to the models we will assess from this point forward.

A <- renew(A, "SOFAS", type_1L_chr = "covariates", what_1L_chr = "results")

We can now assess the multivariate models.

A <- investigate(A, consent_1L_chr = consent_1L_chr)

As a result of the previous step, more model objects and plot files have been saved to a sub-directory of our output directory. Examples of this output are available for download here and here. Once we inspect this output we can reformulate the models we finalised in the previous step so that they are suitable for modelling longitudinal change. For our primary analysis, we use a mixed model formulation of the models that we previously selected. A series of large model files are written to the local output data directory.

A <- investigate(A, consent_1L_chr = consent_1L_chr)

For our secondary analyses, we specify alternative combinations of predictors and covariates.

A <- investigate(A, consent_1L_chr = consent_1L_chr,
                 scndry_anlys_params_ls = make_scndry_anlys_params(candidate_predrs_chr = c("SOFAS"),
                                                                   candidate_covar_nms_chr = c("d_sex_birth_s", "d_age", "d_sexual_ori_s", "d_studying_working"),
                                                                   prefd_covars_chr = NA_character_) %>%
                   make_scndry_anlys_params(candidate_predrs_chr = c("SCARED","OASIS","GAD7"),
                                            candidate_covar_nms_chr = c("PHQ9", "SOFAS", "d_sex_birth_s", "d_age", "d_sexual_ori_s", "d_studying_working"),
                                            prefd_covars_chr = "PHQ9"))

Report findings

Create shareable models

The model objects created and saved in our working directory by the preceding steps are not suitable for public dissemination. They are both too large in file size and, more importantly, include copies of our source dataset. We can overcome these limitations by creating shareable versions of the models. Two types of shareable version are created - copies of the original model objects in which fake data overwrites the original source data and summary tables of model coefficients.

A <- author(A, consent_1L_chr = consent_1L_chr, what_1L_chr = "models")

Specify study reporting metadata

We update A so that we can begin use it to render and share reports.

A <- renew(A, what_1L_chr = "reporting")

We add metadata relevant to the reports that we will be generating to these fields. Note that the data we supply to the Ready4useRepos object below must relate to a repository to which we have write permissions (otherwise subsequent steps will fail).

A <- renew(A, ready4show::authors_tb, type_1L_chr = "authors", what_1L_chr = "reporting") %>%
  renew(ready4show::institutes_tb, type_1L_chr = "institutes", what_1L_chr = "reporting") %>%
  renew(c(3L,3L), type_1L_chr = "digits", what_1L_chr = "reporting") %>%
  renew(c("PDF","PDF"), type_1L_chr = "formats", what_1L_chr = "reporting") %>%
  renew("A hypothetical utility mapping study using fake data", type_1L_chr = "title", what_1L_chr = "reporting") %>%
  renew(renew(ready4show_correspondences(), old_nms_chr = c("PHQ9", "GAD7"), new_nms_chr = c("PHQ-9", "GAD-7")), type_1L_chr = "changes", what_1L_chr = "reporting") %>%
  renew(Ready4useRepos(dv_nm_1L_chr = "fakes", dv_ds_nm_1L_chr = "https://doi.org/10.7910/DVN/D74QMP", dv_server_1L_chr = "dataverse.harvard.edu"), type_1L_chr = "repos", what_1L_chr = "reporting")

Author model catalogues

We download a program for generating a catalogue of models and use it to summarising the models created under each study analysis (one primary and two secondary). The catalogues are saved locally.

A <- author(A, consent_1L_chr = consent_1L_chr, download_tmpl_1L_lgl = T, what_1L_chr = "catalogue")

Author manuscript

We add some content about the manuscript we wish to author.

A <- renew(A, "Quality Adjusted Life Years (QALYs) are often used in economic evaluations, yet utility weights for deriving them are rarely directly measured in mental health services.", 
           type_1L_chr = "background", what_1L_chr = "reporting") %>%
  renew("None declared", type_1L_chr = "conflicts", what_1L_chr = "reporting") %>%
  renew("Nothing should be concluded from this study as it is purely hypothetical.", type_1L_chr = "conclusion", what_1L_chr = "reporting") %>%
  renew("The study was reviewed and granted approval by no-one." , type_1L_chr = "ethics", what_1L_chr = "reporting") %>%
  renew("The study was funded by no-one.", type_1L_chr = "funding", what_1L_chr = "reporting") %>%
  renew("three months", type_1L_chr = "interval", what_1L_chr = "reporting") %>%
  renew(c("anxiety", "AQoL","depression", "psychological distress", "QALYs", "utility mapping"), type_1L_chr = "keywords", what_1L_chr = "reporting") %>%
  renew("The study sample is fake data.", type_1L_chr = "sample", what_1L_chr = "reporting")

We create a brief summary of results that can be interpreted by the program that authors the manuscript.

A <- renew(A, c("AQoL-6D", "Adolescent AQoL Six Dimension"), type_1L_chr = "naming", what_1L_chr = "reporting")

A <- renew(A, "use_renew_mthd", type_1L_chr = "abstract", what_1L_chr = "reporting")

We create and save the plots that will be used in the manuscript.

A <- author(A, consent_1L_chr = consent_1L_chr, what_1L_chr = "plots")

We download a program for generating a template manuscript and run it to author a first draft of the manuscript.

A <- author(A, consent_1L_chr = consent_1L_chr, download_tmpl_1L_lgl = T, what_1L_chr = "manuscript")

We can copy the RMarkdown files that created the template manuscript to a new directory (called “Manuscript_Submission”) so that we can then manually edit those files to produce a manuscript that we can submit for publication.

A <- author(A, consent_1L_chr = consent_1L_chr, type_1L_chr = "copy", what_1L_chr = "manuscript")

At this point in the workflow, additional steps are required to adapt / author the manuscript that will be submitted for publication. However, in this example we are going to skip that step and keep working with the unedited template manuscript. If we had a finalised manuscript authoring program stored online, we could now specify the repository from which the program can be retrieved.

# Not run
# A <- renew(A, c("URL of GitHub repository with", "Program version number"), type_1L_chr = "template-manuscript", what_1L_chr = "reporting")

We can now configure the output to be generated by the manuscript authoring program. The below commands will specify a Microsoft Word format manuscript and a PDF technical appendix. Unlike the template manuscript, the figures and tables will be positioned after (and not within) the main body of the manuscript. Note that the Word version of the manuscript generated by these values will require some minor formatting edits (principally to the display of tables and numbering of sections).

A <- renew(A, F, type_1L_chr = "figures-body", what_1L_chr = "reporting") %>%
  renew(F, type_1L_chr = "tables-body", what_1L_chr = "reporting") %>%
  renew(c("Word","PDF"), type_1L_chr = "formats", what_1L_chr = "reporting")

Once any edits to the RMarkdown files for creating the submission manuscript have been finalised, we can run the following command to author the manuscript. If we are using a custom manuscript authoring program downloaded from an online repository the download_tmpl_1L_lgl argument will need to be set to T.

A <- author(A, consent_1L_chr = consent_1L_chr, download_tmpl_1L_lgl = F, type_1L_chr="submission", what_1L_chr = "manuscript")

We can now generate the Supplementary Information for the submission manuscript.

A <- author(A, consent_1L_chr = consent_1L_chr, supplement_fl_nm_1L_chr = "TA_PDF", type_1L_chr="submission", what_1L_chr = "supplement")

We can now share non-confidential elements (ie no copies of individual records) of the outputs that we have created via our study online repository. To run this step you will need write permissions to the online repository. In the below step we are sharing model catalogues, details of the utility instrument, the shareable mapping models (designed to be used in conjunction with the youthu package), our manuscript files and our supplementary information. In most real world studies the manuscript would not be shared via an online repository - the what_chr argument would need to be ammended to reflect this.

A <- share(A, types_chr = c("auto", "submission"), what_chr = c("catalogue", "instrument" ,"manuscript", "models", "supplement"))

The dataset we created in the previous step is viewable here: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/D74QMP

Tidy workspace

The preceding steps saved multiple objects (mostly R model objects) that have embedded within them copies of the source dataset. To protect the confidentiality of these records we can now purge all such copies from our output data directory.

A <- author(A, what_1L_chr = "purge")

Docs: Make a catalogue of utility mapping models

Thu, 05 Jan 2017 00:00:00 +0000

This below section reproduces the README file of an R Markdown sub-routine. The following alternative options may provide more contextual information and access to more useful code formats:

view examples of the catalogues produced by this subroutine in a study dataset (the names of the relevant files all begin with “AAA_TTU_MDL_CTG”);
view the README along with the current release of its R Markdown code (useful if you plan to run the code); and
view the README along with the current development version of its R Markdown code (useful if you wish to copy or edit the code)

ttu_mdl_ctlg

R Markdown subroutine reporting template for creating utility mapping (transfer to utility) model catalogues. This template should be used in conjunction with the TTU R package.

Docs: Author a template scientific manuscript

Thu, 05 Jan 2017 00:00:00 +0000

This below section reproduces the README file of an R Markdown sub-routine. The following alternative options may provide more contextual information and access to more useful code formats:

ms_tmpl: Generate a template scientific manuscript

Docs: Author a draft scientific manuscript for a utility mapping study

Thu, 05 Jan 2017 00:00:00 +0000

This below section reproduces the README file of an R Markdown sub-routine. The following alternative options may provide more contextual information and access to more useful code formats:

Create a Draft Scientific Manuscript For A Utility Mapping Study

This sub-routine program extends the R package TTU by providing a toolkit for automatically authoring a first draft of a scientific manuscript from results generated by TTU modules.

The program is intended for use and as the last component of TTU’s reporting workflow for utility mapping modelling projects. An example of this workflow is available at: https://doi.org/10.5281/zenodo.6116077 . This program generalises a program that produced the manuscript for a real world study (https://doi.org/10.1101/2021.07.07.21260129).

The program can produce manuscripts in PDF / LaTex (example - https://dataverse.harvard.edu/api/access/datafile/4957407) and Word (example - https://dataverse.harvard.edu/api/access/datafile/4957416). It should be noted that the Word output requires some manual editing to adapt section numbering, modify table headers and resize tables to page boundaries.

Suggested citation (bibTeX):

@software{hamilton_matthew_2022_6931146, author = {Hamilton, Matthew and Gao, Caroline}, title = {{ttu_lng_ss: Create a Draft Scientific Manuscript For A Utility Mapping Study}}, month = jun, year = 2023, note = {{Matthew Hamilton and Caroline Gao (2022). ttu_lng_ss: Create a Draft Scientific Manuscript For A Utility Mapping Study. Zenodo. https://doi.org/10.5281/zenodo.5976987. Version 0.9.0.0}}, publisher = {Zenodo}, version = {0.9.0.0}, doi = {10.5281/zenodo.5976987}, url = {https://doi.org/10.5281/zenodo.5976987} }

Docs: Make results summary for a Discrete Choice Experiment

Thu, 05 Jan 2017 00:00:00 +0000

This below section reproduces the README file of an R Markdown sub-routine. The following alternative options may provide more contextual information and access to more useful code formats:

Report results from a Discrete Choice Experiment implemented with the mychoice R package.

Suggested citation (bibTeX):

@software{hamilton_matthew_2022_6931146, author = {Hamilton, Matthew}, title = {{mychoice_results: Report results from a Discrete Choice Experiment implemented with the mychoice R package}}, month = nov, year = 2022, note = {{Matthew Hamilton (2022). mychoice_results: Report results from a Discrete Choice Experiment implemented with the mychoice R package. Zenodo. https://doi.org/10.5281/zenodo.7297904. Version 0.1.0.0}}, version = {0.1.0.0}, doi = {10.5281/zenodo.7297904}, url = {https://doi.org/10.5281/zenodo.7297904} }

ready4 – Reporting

Docs: Examples

Docs: Subroutine templates for authoring analysis reports

Docs: Find themed collections of ready4 modules

Motivation

Implementation

Use

Related content

Docs: Commands for using model modules

Motivation

Implementation

Use

Core commands

Applying commands to module “slots”

Extended commands

Docs: Finding modules of health economic models

Docs: Find examples of how model modules are used

Motivation

Implementation

Use

Related content

Docs: Authoring scientific manuscripts

Motivation

Implementation

Load required libraries

Set consent policy

Create a synopsis of the manuscript to be authored

Add authorship details

Add correspondences

Specify output directory

Create dataset of literate programming files

Docs: Using model modules for repeatable analyses

Docs: Implement a utility mapping study

Motivation

Implementation

Workflow

Background and citation

Load required packages

Set consent policy

Add dataset metadata

Score health utility

Evaluate candidate models

Specify modelling parameters

Report findings

Create shareable models

Specify study reporting metadata

Author model catalogues

Author manuscript

Share outputs

Tidy workspace

Docs: Make a catalogue of utility mapping models

ttu_mdl_ctlg

Docs: Author a template scientific manuscript

ms_tmpl: Generate a template scientific manuscript

Docs: Author a draft scientific manuscript for a utility mapping study

Create a Draft Scientific Manuscript For A Utility Mapping Study

Docs: Make results summary for a Discrete Choice Experiment