vignette edits

R/pkg_youthu.R

@@ -1,22 +1,15 @@
 #' youthu: Youth Outcomes to Health Utility
 #'
 #' Tools for mapping measures routinely collected in youth
-#' mental health services to AQOL 6D Health Utility. Part of the First
-#' Bounce model of primary youth mental health services.  This
+#' mental health services to Quality Adjusted Life Years (QALYs). Part of
+#' the First Bounce model of primary youth mental health services.  This
 #' development version of the youthu package has been made available as
-#' part of the process of testing and documenting the package. The tools
-#' contained in this development release are designed for use in
-#' conjunction with model objects stored in data repositories. The real
-#' model objects will be publicly released once the associated scientific
-#' manuscript is published. In the mean time, we have included links to
-#' placeholder model objects derived from synthetic data.  For this
-#' reason, this release is for demonstration purposes only and this
-#' package should not yet be used in analyses deigned to inform policy
-#' decisions. If you have any questions, please contact the authors
+#' part of the process of testing and documenting the package. If you
+#' have any questions, please contact the authors
 #' ([email protected]).  The documentation for this package
 #' has been automatically generated by the ready4fun package and is
-#' therefore quite rudimentary. Human authored documentation will follow
-#' in 2021.
+#' therefore quite rudimentary. Further human authored documentation will
+#' follow in 2021.
 #'
 #' To learn more about youthu, start with the vignettes:
 #' `browseVignettes(package = "youthu")`

README.md

@@ -9,22 +9,15 @@
 <!-- badges: end -->
 
 Tools for mapping measures routinely collected in youth
-mental health services to AQOL 6D Health Utility. Part of the First
-Bounce model of primary youth mental health services.  This
+mental health services to Quality Adjusted Life Years (QALYs). Part of
+the First Bounce model of primary youth mental health services.  This
 development version of the youthu package has been made available as
-part of the process of testing and documenting the package. The tools
-contained in this development release are designed for use in
-conjunction with model objects stored in data repositories. The real
-model objects will be publicly released once the associated scientific
-manuscript is published. In the mean time, we have included links to
-placeholder model objects derived from synthetic data.  For this
-reason, this release is for demonstration purposes only and this
-package should not yet be used in analyses deigned to inform policy
-decisions. If you have any questions, please contact the authors
+part of the process of testing and documenting the package. If you
+have any questions, please contact the authors
 ([email protected]).  The documentation for this package
 has been automatically generated by the ready4fun package and is
-therefore quite rudimentary. Human authored documentation will follow
-in 2021.
+therefore quite rudimentary. Further human authored documentation will
+follow in 2021.
 
 If you plan on testing this software you can install it by running the following commands in your R console:
 

_pkgdown.yml

@@ -1,11 +1,3 @@
-home:
-  links:
-  - text: User manual (PDF)
-    href: https://github.com/ready4-dev/youthu/releases/download/v0.0.0.9065/youthu_user_0.0.0.9065.pdf
-  - text: Developer version of usual manual (PDF)
-    href: https://github.com/ready4-dev/youthu/releases/download/v0.0.0.9065/youthu_developer_0.0.0.9065.pdf
-  - text: Project website
-    href: https://www.ready4-dev.com/
 development:
   mode: auto
 reference:

data-raw/DATASET.R

@@ -13,10 +13,9 @@ ready4fun::write_fn_type_dirs()
 ##
 # 4. Set-up package structure
 ready4fun::make_pkg_desc_ls(pkg_title_1L_chr = "Youth Outcomes to Health Utility",
-                            pkg_desc_1L_chr = "Tools for mapping measures routinely collected in youth mental health services to AQOL 6D Health Utility. Part of the First Bounce model of primary youth mental health services.
-  This development version of the youthu package has been made available as part of the process of testing and documenting the package. The tools contained in this development release are designed for use in conjunction with model objects stored in data repositories. The real model objects will be publicly released once the associated scientific manuscript is published. In the mean time, we have included links to placeholder model objects derived from synthetic data.
-  For this reason, this release is for demonstration purposes only and this package should not yet be used in analyses deigned to inform policy decisions. If you have any questions, please contact the authors ([email protected]).
-  The documentation for this package has been automatically generated by the ready4fun package and is therefore quite rudimentary. Human authored documentation will follow in 2021.",
+                            pkg_desc_1L_chr = "Tools for mapping measures routinely collected in youth mental health services to Quality Adjusted Life Years (QALYs). Part of the First Bounce model of primary youth mental health services.
+  This development version of the youthu package has been made available as part of the process of testing and documenting the package. If you have any questions, please contact the authors ([email protected]).
+  The documentation for this package has been automatically generated by the ready4fun package and is therefore quite rudimentary. Further human authored documentation will follow in 2021.",
                             authors_prsn = c(utils::person(given = "Matthew",family = "Hamilton",email = "[email protected]", role = c("aut", "cre"),comment = c(ORCID = "0000-0001-7407-9194")),
                                               utils::person(given = "Caroline",family = "Gao",email = "[email protected]", role = c("aut"),comment = c(ORCID = "0000-0002-0987-2759")),
                                               utils::person("Orygen", role = c("cph", "fnd")),

vignettes/Economic_Analysis.Rmd

@@ -16,17 +16,13 @@ knitr::opts_chunk$set(echo = TRUE)
 library(youthu)
 set.seed(1234)
 ```
-Note, this example is illustrated with fake data. It should not be used to inform decision-making.
-
-# Introduction
+This vignette illustrates the rationale for and practical decision-making utility of youthu's [QALYs prediction workflow](Prediction_With_Mdls.html). Note, this example is illustrated with fake data and should not be used to inform decision-making.
 
 ## Motivation
 The main motivation behind the youthu package is to extend the types of economic analysis that can be undertaken with both single group (e.g. pilot study, health service records) and matched groups (e.g. trial) longitudinal datasets that do not include measures of health utility. This article focuses on its application to matched group datasets.
 
-# Economic analysis of matched group datasets
-
-## Data 
-We must first import our data. In this example we will use a fake dataset.
+## Example dataset 
+First, we must first import our data. In this example we will use a fake dataset.
 
 ```{r}
 ds_tb <- make_fake_ds_two()

vignettes/Prediction_With_Mdls.Rmd

@@ -13,10 +13,16 @@ params:
 library(magrittr)
 library(youthu)
 ```
-Note, this example uses fake data - it should should not be used to inform decision making.
+This vignette outlines a workflow for:
 
-## Identifying suitable transfer to utility algorithms
-To identify datasets that contain transfer to utility models compatible with youthu (ie those developped with the [TTU package](https://ready4-dev.github.io/TTU/index.html)), you can use the following command. 
+- Searching, selecting and retrieving transfer to utility models;
+- Preparing a prediction dataset for use with a selected transfer to utility model; and
+- Applying the selected transfer to utility model to a prediction dataset to predict Quality Adjusted Life Years (QALYs).
+
+The practical value of implementing such a workflow is discussed in the [economic analysis vignette](Economic_Analysis.html) and [a scientific manuscript](https://www.medrxiv.org/content/10.1101/2021.07.07.21260129v2.full). Note, this example uses fake data - it should should not be used to inform decision making.
+
+## Search, select and retrie transfer to utility models
+To identify datasets that contain transfer to utility models compatible with youthu (ie those developped with the [TTU package](https://ready4-dev.github.io/TTU/index.html)), you can use the `get_ttu_dv_dss` function. The function searches specified dataverses (in the below example, the [TTU dataverse](https://dataverse.harvard.edu/dataverse/TTU)) for datasets containing output from the TTU package.
 
 ```{r}
 ttu_dv_dss_tb <- get_ttu_dv_dss("TTU")
@@ -25,7 +31,7 @@ ttu_dv_dss_tb <- get_ttu_dv_dss("TTU")
 The `ttu_dv_dss_tb` table summarises some pertinent details about each dataset containing TTU models found by the preceding command. These details include a link to any scientific summary (the "Article" column) associated with a dataset.
 
 ```{r ttudss, echo = F, tab.cap='Transfer to Utility Datasets', tab.id = 'ttudss', results="asis"}
-ttu_dv_dss_tb %>% 
+ttu_dv_dss_tb[,c(1:3,5)] %>% 
   ready4show::print_table(output_type_1L_chr = params$output_type_1L_chr,
                           caption_1L_chr = knitr::opts_current$get("tab.cap"),
                           use_lbls_as_col_nms_1L_lgl = T,
@@ -70,8 +76,10 @@ get_mdl_ctlg_url(mdls_lup,
                  mdl_nm_1L_chr = "PHQ9_SOFAS_1_OLS_CLL")
 ```
 
-## Prepare prediction dataset
-First import and inspect the dataset you will be using for prediction. In the below example we use fake data.
+## Prepare a prediction dataset for use with a selected transfer to utility model
+
+### Import data
+You can now import and inspect the dataset you plan on using for prediction. In the below example we use fake data.
 
 ```{r }
 data_tb <- make_fake_ds_one()
@@ -86,12 +94,20 @@ data_tb %>%
 ```
 
 ### Confirm dataset can be used as a prediction dataset
-The prediction dataset must contain variables that correspond to all the predictors of the model you intend to apply. The values of each predictor variable must conform to the corresponding parameter as described in the model predictor metadata, which can be accessed with a call to the `get_predictors_lup` function.
+The prediction dataset must contain variables that correspond to all the predictors of the model you intend to apply. The allowable range and required class of each predictor variable are described in the `min_val_dbl`,	`max_val_dbl` and	`class_chr` columns model predictors lookup table, which can be accessed with a call to the `get_predictors_lup` function.
 
 ```{r}
-get_predictors_lup(mdls_lup = mdls_lup,
-                   mdl_nm_1L_chr = "PHQ9_SOFAS_1_OLS_CLL")
+predictors_lup <- get_predictors_lup(mdls_lup = mdls_lup,
+                                     mdl_nm_1L_chr = "PHQ9_SOFAS_1_OLS_CLL")
+```
+```{r predluptb, echo = F, tab.cap='Model predictors lookup table', tab.id = 'predluptb', results="asis"}
+predictors_lup %>% 
+  ready4show::print_table(output_type_1L_chr = params$output_type_1L_chr,
+                          caption_1L_chr = knitr::opts_current$get("tab.cap"),
+                          mkdn_tbl_ref_1L_chr = paste0("tab:",knitr::opts_current$get("tab.id")),
+                          add_to_row_ls = NULL) 
 ```
+
 The prediction dataset must also include both a unique client identifier variable and a measurement time-point identifier variable (which must be a `factor`{.R} with two levels). The dataset also needs to be in long format (ie where measures at different time-points for the same individual are stacked on top of each other in separate rows). We can confirm these conditions hold by creating a dataset metadata object using the `make_predn_metadata_ls` function. In creating the metadata object, the function checks that the dataset can be used in conjunction with the model specified at the `mdl_nm_1L_chr` argument. If the prediction dataset uses different variable names for the predictors to those specified in the `predictors_lup`{.R} lookup table, a named vector detailing the correspondence between the two sets of variable names needs to be passed to the `predr_vars_nms_chr` argument. Finally, if you wish to specify a preferred variable name to use for the predicted utility values when applying the model, you can do this by passing this name to the `utl_var_nm_1L_chr` argument.
 
 ```{r}
@@ -107,8 +123,9 @@ predn_ds_ls <- make_predn_metadata_ls(data_tb,
 ```
 
 
-## Predict utility
+## Apply the selected transfer to utility model to a prediction dataset to predict Quality Adjusted Life Years (QALYs)
 
+### Predict health utility at baseline and follow-up timepoints
 To generate utility predictions we use the `add_utl_predn`{.R} function. The function needs to be supplied with the prediction dataset (the value passed to argument `data_tb`) and the validated prediction metadata object we created in the previous step. By default the function chooses a constrained simulation approach, based on a table of model coefficients when making predictions. You can override this by adding additional arguments `new_data_is_1L_chr = "Predicted"`, `force_min_max_1L_lgl = T` and (if the source dataset makes available downloadable models) `make_from_tbl_1L_lgl = F`. However, before doing so it is strongly recommended that you consult the model catalogue (see above) to understand how such a decision may affect the validity of the predicted values that will be generated.
 
 ```{r }
@@ -124,13 +141,14 @@ data_tb %>%
                           mkdn_tbl_ref_1L_chr = paste0("tab:",knitr::opts_current$get("tab.id")),
                           add_to_row_ls = NULL) 
 ```
+
 Our health utility predictions are now available for use and are summarised below.
 
 ```{r }
 summary(data_tb$AQoL6D_HU)
 ```
 
-## Calculate QALYs
+### Calculate QALYs
 The last step is to calculate Quality Adjusted Life Years, using a method assuming a linear rate of change between timepoints.
 
 ```{r}