Example 2: Predict health utility from psychological measures (PHQ-9 and GAD-7)

This tutorial illustrates the main steps for predicting AQoL-6D utility from two psychological measures using a longitudinal dataset in wide format.
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This below section renders a vignette article from the youthu library. You can use the following links to:

This vignette article is abridged and modified version of another article on predicting Quality Adjusted Life Years with youthu.

Motivation

This article illustrates how to make QALY predictions using a dataset in wide format with no health-utility measures but containing two psychological measures (GAD-7 and PHQ-9).

Install youthu

If not already installed it will be necessary to install the youthu R library. As youthu is not yet available on CRAN, it will be necessary to install it directly from its GitHub repository using an R package like remotes or devtools.

# Uncomment and run if installation is required.
# utils::install.packages("devtools") 
# devtools::install_github("ready4-dev/youthu")

Load required packages

We now load the libraries we will be using in subsequent steps. Note, both the ready4, ready4show and ready4use ready4 framework libraries will have been installed automatically when youthu was installed. The specific readyforwhatsnext module library and dplyr, purrr, stringr and tidyr CRAN libraries will have been installed at the same time.

library(ready4)
library(ready4show)
library(ready4use)
library(specific)
library(youthu)

Specify data sources

We begin by specifying the sources for our data. In this example, our data sources are online repositories.

X <- 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",
                    gh_repo_1L_chr = "ready4-dev/youthu", gh_tag_1L_chr = "v0.0.0.91125")

Inspect dataset

We can now inspect the dataset we will be using to make predictions. As this is a demonstration article we are going to create a custom synthetic dataset. Our first step in doing so is to ingest a preexisting synthetic dataset (in long format) using the method explained in another vignette article

data_tb <- ingest(X, fls_to_ingest_chr = c("ymh_phq_gad_tb"), metadata_1L_lgl = F)

Our resulting dataset has unique IDs for each participant (character class), timestamps for each data collection timepoint (Date class variables) and GAD-7 and PHQ-9 scores for each timepoint (integer class).

data_tb %>% head() %>% ready4show::print_table(caption_1L_chr = "Dataset", output_type_1L_chr = "HTML")
Dataset
fkClientID d_interview_date_t1 d_interview_date_t2 gad7_t1 gad7_t2 phq9_t1 phq9_t2
Participant_1 2020-03-22 NA 6 NA 7 NA
Participant_2 2020-06-15 NA 12 NA 13 NA
Participant_3 2020-08-20 NA 16 NA 17 NA
Participant_4 2020-05-23 2020-08-19 12 12 17 14
Participant_5 2020-04-05 2020-07-19 14 6 22 8
Participant_6 2020-06-09 NA 8 NA 8 NA

Get mapping models

We retrieve details of relevant AQoL-6D mapping models for wither of the predictors we plan on using. How these models were derived is described in a pre-print and details of model performance is included in catalogues available in an open access data repository.

mdls_lup <- get_mdls_lup(ttu_dv_dss_tb = get_ttu_dv_dss("TTU"),
                         utility_type_chr = "AQoL-6D",
                         mdl_predrs_in_ds_chr = c("GAD7 total score", "PHQ9 total score"))
mdls_lup[,c(1,2,5)] %>% 
  ready4show::print_table(caption_1L_chr = "Available models", output_type_1L_chr = "HTML")
Available models
mdl_nms_chr predrs_ls source_chr
PHQ9_1_GLM_GSN_LOG PHQ9 Primary Analysis
PHQ9_1_OLS_CLL PHQ9 Primary Analysis
GAD7_1_GLM_GSN_LOG GAD7 Primary Analysis
GAD7_1_OLS_CLL GAD7 Primary Analysis
PHQ9_SOFAS_1_GLM_GSN_LOG PHQ9 , SOFAS Primary Analysis
PHQ9_SOFAS_1_OLS_CLL PHQ9 , SOFAS Primary Analysis
GAD7_SOFAS_1_GLM_GSN_LOG GAD7 , SOFAS Primary Analysis
GAD7_SOFAS_1_OLS_CLL GAD7 , SOFAS Primary Analysis
OASIS_PHQ9_1_GLM_GSN_LOG OASIS, PHQ9 Secondary Analysis B
OASIS_PHQ9_1_OLS_CLL OASIS, PHQ9 Secondary Analysis B
GAD7_PHQ9_1_GLM_GSN_LOG GAD7, PHQ9 Secondary Analysis B
GAD7_PHQ9_1_OLS_CLL GAD7, PHQ9 Secondary Analysis B
SCARED_PHQ9_1_GLM_GSN_LOG SCARED, PHQ9 Secondary Analysis B
SCARED_PHQ9_1_OLS_CLL SCARED, PHQ9 Secondary Analysis B

We select our preferred model and retrieve summary data about the model’s predictor variables.

predictors_lup <- get_predictors_lup(mdls_lup = mdls_lup, mdl_nm_1L_chr = "GAD7_PHQ9_1_OLS_CLL")
exhibit(predictors_lup)
Variable Description Minimum Maximum Class Increment Function Scaling Covariate
GAD7 GAD7 total score 0 21 integer 1 youthvars::youthvars_gad7 0.01 FALSE
PHQ9 PHQ9 total score 0 27 integer 1 youthvars::youthvars_phq9 0.01 FALSE

Transform prediction dataset

To be used with the mapping models available to us, our prediction dataset needs to be in long format. We perform the necessary transformation.

data_tb <- transform_ds_to_long(data_tb, predictors_chr = c("gad7", "phq9"),
                             msrmnt_date_var_nm_1L_chr = "d_interview_date", round_var_nm_1L_chr = "When")
#> Joining with `by = join_by(case_id, fkClientID, When)`
#> Joining with `by = join_by(case_id, fkClientID, When)`

We drop records where we are missing data for either GAD7 or PHQ9 at either timepoint.

data_tb <- transform_ds_to_drop_msng(data_tb, predictors_chr = c("gad7", "phq9"), 
                                      uid_var_nm_1L_chr = "fkClientID")

We now predict AQoL-6D health utility for each case with complete data.

predn_ds_ls <- make_predn_metadata_ls(data_tb,
                                      id_var_nm_1L_chr = "fkClientID",
                                      msrmnt_date_var_nm_1L_chr = "d_interview_date",
                                      predr_vars_nms_chr = c(GAD7 = "gad7", PHQ9 = "phq9"),
                                      round_var_nm_1L_chr = "When",
                                      round_bl_val_1L_chr = "t1",
                                      utl_var_nm_1L_chr = "AQoL6D_HU",
                                      mdls_lup = mdls_lup,
                                      mdl_nm_1L_chr = "GAD7_PHQ9_1_OLS_CLL")
data_tb <- add_utl_predn(data_tb, new_data_is_1L_chr = "Predicted", predn_ds_ls = predn_ds_ls)
#> Joining with `by = join_by(fkClientID, When)`

Finally, we derive QALY predictions from the health utility measures at both time-points.

data_tb <- data_tb %>% add_qalys_to_ds(predn_ds_ls = predn_ds_ls, include_predrs_1L_lgl = F, reshape_1L_lgl = T)
data_tb %>% head() %>%
  ready4show::print_table(caption_1L_chr = "Final dataset", output_type_1L_chr = "HTML",
                          scroll_box_args_ls = list(width = "100%"))
Final dataset
fkClientID d_interview_date_t1 d_interview_date_t2 gad7_t1 gad7_t2 phq9_t1 phq9_t2 AQoL6D_HU_t1 AQoL6D_HU_t2 AQoL6D_HU_change_dbl_t1 AQoL6D_HU_change_dbl_t2 duration_prd_t1 duration_prd_t2 qalys_dbl_t1 qalys_dbl_t2
Participant_10 2020-08-05 2020-11-07 15 13 17 18 0.3891806 0.6342526 0 0.2450720 0S 94d 0H 0M 0S 0 0.1316943
Participant_1000 2020-09-06 2020-12-20 13 10 13 10 0.6609298 0.2963083 0 -0.3646215 0S 105d 0H 0M 0S 0 0.1375907
Participant_1001 2020-07-05 2020-10-15 10 11 10 16 0.5324127 0.6192971 0 0.0868844 0S 102d 0H 0M 0S 0 0.1608137
Participant_1003 2020-05-18 2020-08-12 6 8 16 7 0.5630164 0.8584193 0 0.2954030 0S 86d 0H 0M 0S 0 0.1673422
Participant_1005 2020-05-09 2020-08-25 14 5 20 9 0.5090272 0.7799675 0 0.2709403 0S 108d 0H 0M 0S 0 0.1905701
Participant_1006 2020-05-29 2020-08-25 15 9 21 17 0.2969778 0.2734973 0 -0.0234805 0S 88d 0H 0M 0S 0 0.0687225
Last modified June 8, 2024: updated vignettes (77a947c)