ready4 – Software - libraries (scorz)

Docs: Create a synthetic population of young people attending primary mental health services

Thu, 30 Nov 2023 00:00:00 +0000

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view the HTML in the dataset containing its outputs (includes contextual information) from that article; and;
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Introduction

This program generates a purely synthetic (i.e. fake - no trace of any real records) population that is reasonably representative of the input data we used for the utility mapping study described in the article https://doi.org/10.1101/2021.07.07.21260129.

No access to the real data is required in order to use this program - it is based on summary statistics (e.g. means and standard deviations of variables, correlation matrices). It should be noted however, that a different (and simpler) workflow can be implemented when you do have access to the source dataset (for example, by using the syn function from the synthpop package).

The output of this program is very similar but not identical to a fake dataset created by an earlier version of this program and which is saved in the “ymh_clinical_dict_r3.RDS” file from the https://doi.org/10.7910/DVN/HJXYKQ data repository.

Install required R packages

If you do not have the following packages already installed, uncomment and run the following lines.

# install.packages("faux")
# devtools::install_github("ready4-dev/ready4) 
# devtools::install_github("ready4-dev/youthvars) 
# devtools::install_github("ready4-dev/scorz) 
# devtools::install_github("ready4-dev/specific") 
# devtools::install_github("ready4-dev/TTU")
# devtools::install_github("ready4-dev/youthu")

Load the ready4 framework package.

library(ready4)

Specify parameters to generate outcome fake data

AQoL item response parameters

The first set of input data are the proportions for each allowed response for each of the twenty AQOL-6D questions at both baseline and followup.

aqol_items_prpns_tbs_ls <- list(bl_answer_props_tb = tibble::tribble(
    ~Question, ~Answer_1, ~Answer_2, ~Answer_3, ~Answer_4, ~Answer_5, ~Answer_6,
    "Q1", 0.35, 0.38, 0.16, 0.03, NA_real_,100, # Check item 5 in real data.
    "Q2", 0.28, 0.38, 0.18, 0.08, 0.04,100,
    "Q3", 0.78, 0.18, 0.03, 0.01, 0.0, 100,
    "Q4", 0.64, 0.23, 0.09, 0.0, 100, NA_real_,
    "Q5", 0.3, 0.48, 0.12, 0.05, 100, NA_real_,
    "Q6", 0.33, 0.48, 0.15, 100, NA_real_,NA_real_,
    "Q7", 0.44, 0.27, 0.11, 100, NA_real_, NA_real_,
    "Q8", 0.18, 0.29, 0.23, 0.21, 100, NA_real_,
    "Q9", 0.07, 0.27, 0.19, 0.37, 100, NA_real_,
    "Q10", 0.04, 0.15, 0.4, 0.25, 100, NA_real_,
    "Q11", 0.03, 0.13, 0.52, 0.25, 100, NA_real_,
    "Q12", 0.06, 0.21, 0.25, 0.34, 100, NA_real_,
    "Q13", 0.05, 0.25, 0.31, 0.28, 100, NA_real_,
    "Q14", 0.05, 0.3, 0.34, 0.25, 100, NA_real_,
    "Q15", 0.57, 0.25, 0.12, 100, NA_real_,NA_real_,
    "Q16", 0.48, 0.42, 0.06, 100, NA_real_, NA_real_,
    "Q17", 0.44, 0.3, 0.16, 0.07, 100, NA_real_,
    "Q18", 0.33, 0.38, 0.25, 0.04, 0.0, 100,
    "Q19", 0.33, 0.49, 0.16, 0.02, 0.0, 100,
    "Q20", 0.67, 0.21, 0.02, 100, NA_real_,NA_real_),
    fup_answer_props_tb = tibble::tribble(
    ~Question, ~Answer_1, ~Answer_2, ~Answer_3, ~Answer_4, ~Answer_5, ~Answer_6,
    "Q1", 0.51, 0.33, 0.12, 0.02, NA_real_, 100,
    "Q2", 0.36, 0.38, 0.16, 0.06, 0.02,100,
    "Q3", 0.81, 0.15, 0.04, 0.00, 0.0, 100,
    "Q4", 0.73, 0.18, 0.09, 0.0, 100, NA_real_,
    "Q5", 0.36, 0.42, 0.12, 0.05, 100, NA_real_,
    "Q6", 0.48, 0.40, 0.11, 100, NA_real_,NA_real_,
    "Q7", 0.57, 0.25, 0.09, 100, NA_real_, NA_real_,
    "Q8", 0.31, 0.33, 0.17, 0.12, 100, NA_real_,
    "Q9", 0.13, 0.35, 0.19, 0.23, 100, NA_real_,
    "Q10", 0.1, 0.21, 0.43, 0.16, 100, NA_real_,
    "Q11", 0.06, 0.25, 0.48, 0.18, 100, NA_real_,
    "Q12", 0.08, 0.27, 0.26, 0.25, 100, NA_real_,
    "Q13", 0.07, 0.37, 0.31, 0.19, 100, NA_real_,
    "Q14", 0.08, 0.37, 0.34, 0.15, 100, NA_real_,
    "Q15", 0.62, 0.23, 0.09, 100, NA_real_,NA_real_,
    "Q16", 0.52, 0.40, 0.06, 100, NA_real_, NA_real_,
    "Q17", 0.51, 0.28, 0.15, 0.06, 100, NA_real_,
    "Q18", 0.37, 0.35, 0.25, 0.03, 0.0, 100,
    "Q19", 0.43, 0.40, 0.16, 0.01, 0.0, 100,
    "Q20", 0.77, 0.21, 0.02, 100, NA_real_,NA_real_)) %>%
  youthvars::make_complete_prpns_tbs_ls()

Outcome variable correlation parameters

First we specify the names of variables we will be creating as outcome variables.

var_names_chr <- c("aqol6d_total_w","phq9_total","bads_total",
                   "gad7_total","oasis_total","scared_total","k6_total")

The next step is to specify the correlations between outcome variables (variables assumed to be ordered as in previous step) at baseline and follow-up timepoints.

cor_mat_ls <- list(matrix(c(1,-0.78,0.72,-0.67,-0.71,-0.65,-0.67,
                               NA,1,-0.73,0.69,0.66,0.63,0.71,
                               NA,NA,1,-.57,-0.64,-0.57,-0.65,
                               NA,NA,NA,1,0.74,0.70,0.63,
                               NA,NA,NA,NA,1,0.7,0.59,
                               NA,NA,NA,NA,NA,1,0.55,
                               NA,NA,NA,NA,NA,NA,1),7,7),
                    matrix(c(1,-0.81,0.72,-0.71,-0.73,-0.64,-0.68,
                        NA,1,-0.72,0.69,0.68,0.61,0.68,
                        NA,NA,1,-0.59,-0.61,-0.51,-0.61,
                        NA,NA,NA,1,0.75,0.71,0.6,
                        NA,NA,NA,NA,1,0.68,0.59,
                        NA,NA,NA,NA,NA,1,0.52,
                        NA,NA,NA,NA,NA,NA,1),7,7))

We now specify the univariate distribution parameters for each of the outcome variables.

synth_data_spine_ls <- list(cor_mat_ls = cor_mat_ls,
                            nbr_obs_dbl = c(1068,643),
                            timepoint_nms_chr = c("BL","FUP"),
                            means_ls = list(c(0.6,12.8,78.2, 10.4,8.1,34.2,12.2),
                                            c(0.7,9.8,89.4, 7.9,6.3,28.8,9.8)),
                            sds_ls = list(c(0.2,6.6,24.8,5.7,4.7,17.9,5.8),
                                          c(0.2,6.5,24.4,5.5,4.3,17.8,5.9)),
                            missing_ls = list(c(0,4,10,6,7,7,4),
                                              c(0,5,2,2,1,2,2)),
                            min_max_ls = list(c(0.03,1),
                                              c(0,27),
                                              c(0,150),
                                              c(0,21),
                                              c(0,20),
                                              c(0,82),
                                              c(0,24)),
                            discrete_lgl = c(F,rep(T,6)),
                            var_names_chr = var_names_chr,
                            aqol_tots_var_nms_chr = c(cumulative = "aqol6d_total_c",
                                                      weighted = "aqol6d_total_w"))

Generate fake data

Create fake outcome variable datasets

We now use the parameters we have just specified to create baseline and follow-up datasets with fake data for our nominated outcome variables.

aqol_scores_pars_ls <- list(means_dbl = c(44.5,40.6), 
                            sds_dbl = c(9.9,9.8),
                            corr_dbl = -0.95)
aqol6d_adol_pop_tbs_ls <- aqol_items_prpns_tbs_ls %>%
  scorz::make_aqol6d_adol_pop_tbs_ls(aqol_scores_pars_ls = aqol_scores_pars_ls,
                                     series_names_chr =  c("bl_outcomes_tb",
                                                           "fup_outcomes_tb"),
                                     synth_data_spine_ls = synth_data_spine_ls,
                                     temporal_cors_ls = list(aqol6d_total_w = 0.85))
#> Joining with `by = join_by(id, match_var_chr)`
#> Joining with `by = join_by(id)`
#> Joining with `by = join_by(id, match_var_chr)`
#> Joining with `by = join_by(id)`

Create fake descriptive variables

We now specify the names and statistical parameters of the variables we will be using in descriptive statistics. For this analysis we are not interested in capturing the joint distribution between these variables, so we only use univariate parameters.

descriptives_BL_tb <- tibble::tibble(fkClientID = aqol6d_adol_pop_tbs_ls$bl_outcomes_tb$fkClientID,
                                     round = c(1) ,
                                     d_age = rnorm(1068,18.1,3.3) %>% 
                                       purrr::map_dbl(~min(.x,25) %>% 
                                                        max(12)),
                                     d_gender = c(rep(1,653),
                                                  rep(2,359),
                                                  rep(3,39),
                                                  rep(NA_real_,17)) %>% 
                                       specific::scramble_xx() %>%
                                       factor(labels = c("Female","Male","Other")),
                                     d_sexual_ori_s = c(rep(1,738),
                                                        rep(2,289),
                                                        rep(NA_real_,41)) %>% 
                                       specific::scramble_xx() %>%
                                       factor(labels = c("Straight","Other")),
                                     Region = c(rep(1,671),
                                                rep(2,397)) %>% 
                                       specific::scramble_xx() %>%
                                       factor(labels = c("Metro","Regional")),
                                     CALD = c(rep(T,759),
                                              rep(F,169),
                                              rep(NA,140)) %>% 
                                       specific::scramble_xx(),
                                     d_studying_working = c(rep(1,405),
                                                            rep(2,167),
                                                            rep(3,305),
                                                            rep(4,159),
                                                            rep(NA_real_,32)) %>% 
                                       specific::scramble_xx() %>% 
                                       factor(labels = c("Studying only",
                                                         "Working only",
                                                         "Studying and working",
                                                         "Not studying or working")),
                                     c_p_diag_s = c(rep(1,182),
                                                    rep(2,264),
                                                    rep(3,332),
                                                    rep(4,237),
                                                    rep(NA_real_,53)) %>% 
                                       specific::scramble_xx() %>%
                         factor(labels = c("Depression", "Anxiety","Depression and Anxiety", "Other")),
                         c_clinical_staging_s = c(rep(1,625),
                                                  rep(2,326),
                                                  rep(3,86),
                                                  rep(NA_real_,31)) %>% 
                           specific::scramble_xx() %>%
                           factor(labels = c("0-1a","1b","2-4")),
                         c_sofas = c(rnorm(1068-30,65.2,9.5),
                                     rep(NA_real_,30)) %>% 
                           purrr::map_dbl(~min(.x,100) %>% 
                                            max(0)) %>% 
                           specific::scramble_xx(),
                         s_centre = NA_character_, 
                         d_agegroup = NA_character_, 
                         d_sex_birth_s = NA_character_, 
                         d_country_bir_s = NA_character_,
                         d_ATSI = NA_character_,
                         d_english_home = NA, 
                         d_english_native = NA, 
                         d_relation_s = c(rep(1,325),
                                          rep(2,426),
                                          rep(3,286),
                                          rep(NA_real_,31)) %>% 
                           specific::scramble_xx() %>%
                           factor(labels = c("REPLACE_ME_1",
                                             "REPLACE_ME_2",
                                             "REPLACE_ME_3")))  %>%
  dplyr::mutate(d_sex_birth_s = dplyr::case_when(is.na(d_gender) ~ NA_integer_,
                                                 as.integer(d_gender) %in% 
                                                   c(1L,2L) & 
                                                   runif(1068)>0.995 ~ as.integer(d_gender) %>%
                                                   purrr::map_int(~ ifelse(is.na(.x), 
                                                                           .x, 
                                                                           switch(.x,2L,1L,3L))),
                                                 as.integer(d_gender) == 3 ~ sample(c(1L,2L), 
                                                                                    1068, 
                                                                                    replace = T),
                                                 TRUE ~ as.integer(d_gender)
                                                 ) %>%
                  factor(labels = c("Female","Male")))
#> Registered S3 methods overwritten by 'ggalt':
#>   method                  from   
#>   grid.draw.absoluteGrob  ggplot2
#>   grobHeight.absoluteGrob ggplot2
#>   grobWidth.absoluteGrob  ggplot2
#>   grobX.absoluteGrob      ggplot2
#>   grobY.absoluteGrob      ggplot2
#> Registered S3 methods overwritten by 'rmutil':
#>   method         from 
#>   plot.residuals psych
#>   print.response httr
descriptives_FUP_tb <- descriptives_BL_tb %>% 
  dplyr::filter(fkClientID %in% 
                  aqol6d_adol_pop_tbs_ls$fup_outcomes_tb$fkClientID) %>%
  dplyr::mutate(round = 2,
                d_age = d_age + 0.25,
                Region = Region %>% 
                  specific::randomise_changes_in_fct_lvls(0.98),
                d_studying_working = d_studying_working %>%
                  specific::randomise_changes_in_fct_lvls(0.9),
                c_p_diag_s = c_p_diag_s %>% 
                  specific::randomise_changes_in_fct_lvls(0.90),
                c_clinical_staging_s = c_clinical_staging_s %>% 
                  specific::randomise_changes_in_fct_lvls(0.8),
                c_sofas = c_sofas + rnorm(643,4.7,10) %>% 
                         purrr::map_dbl(~min(.x,100) %>% max(0)))
bl_tb <- dplyr::inner_join(descriptives_BL_tb,
                           aqol6d_adol_pop_tbs_ls$bl_outcomes_tb) 
#> Joining with `by = join_by(fkClientID)`
fup_tb <- dplyr::inner_join(descriptives_FUP_tb,
                            aqol6d_adol_pop_tbs_ls$fup_outcomes_tb)
#> Joining with `by = join_by(fkClientID)`

We make some adjustments to ensure that the c_sofas variable is correlated with our aqol6d_total_w variable at both baseline and follow-up.

bl_tb <- bl_tb %>%
  dplyr::mutate(c_sofas = faux::rnorm_pre(bl_tb$aqol6d_total_w %>% 
                                            as.vector(), 
                                          mu = 65.2, 
                                          sd = 9.5, 
                                          r = 0.5, 
                                          empirical = T) %>% 
                  purrr::map_dbl(~min(.x,100) %>% max(0)))
fup_tb <- fup_tb %>%
  dplyr::mutate(c_sofas = faux::rnorm_pre(fup_tb$aqol6d_total_w %>% 
                                            as.vector(), 
                                          mu = 69.9, 
                                          sd = 10, 
                                          r = 0.5, 
                                          empirical = T) %>% 
                  purrr::map_dbl(~min(.x,100) %>% max(0)))

Combine datasets

We now add the fake outcome variables dataset to the fake descriptive variables dataset.

composite_tb <- dplyr::bind_rows(bl_tb, fup_tb) %>%
  dplyr::mutate(d_age = floor(d_age)) %>%
  dplyr::mutate(d_gender = d_gender %>% as.character() %>%
                  purrr::map_chr(~ifelse(.x=="Other",
                                         sample(c("Genderqueer/gender nonconforming/agender",
                                                              "Transgender"),1),
                                         .x)),
                s_centre = Region %>% as.character() %>%
                  purrr::map_chr(~ifelse(.x=="Metro",
                                         sample(c("Canberra","Southport","Knox"),1),
                                         "Regional Centre")),
                d_country_bir_s = CALD %>%
                  purrr::map_chr(~ifelse(.x,
                                         "Other",
                                         "Australia")), 
                       d_ATSI = CALD %>%
                  purrr::map_chr(~ifelse(.x,
                                         "Yes",
                                         "No")),
                       d_english_home = CALD %>%
                  purrr::map_chr(~ifelse(.x,
                                         "No",
                                         "Yes")), 
                       d_english_native = CALD %>%
                  purrr::map_chr(~ifelse(.x,
                                         "No",
                                         "Yes"))
                ) %>%
  dplyr::select(-CALD) %>%
  dplyr::select(-Region)
composite_tb <- composite_tb %>%
  dplyr::select(-setdiff(names(composite_tb)[startsWith(names(composite_tb),
                                                        "aqol6d_")],
                         names(composite_tb)[startsWith(names(composite_tb),
                                                        "aqol6d_q")]))
composite_tb <- composite_tb %>%
  dplyr::mutate(c_sofas = as.integer(round(c_sofas,0))) %>%
  dplyr::mutate(round = factor(round, labels = c("Baseline",
                                                 "Follow-up"))) %>%
  dplyr::mutate(d_relation_s = dplyr::case_when(d_relation_s %in% 
                                                  c("REPLACE_ME_1","REPLACE_ME_2") ~ 
                                                  "Not in a relationship",
                                                T ~ "In a relationship")) %>%
  youthu::add_dates_from_dstr(bl_start_date_dtm = Sys.Date() - lubridate::days(600),##
                              bl_end_date_dtm = Sys.Date() - lubridate::days(420),
                              duration_args_ls = list(a = 60, b = 140, mean = 90, sd = 10),
                              duration_fn = truncnorm::rtruncnorm,
                              date_var_nm_1L_chr = "d_interview_date") %>%
  dplyr::select(-duration_prd) %>%
  youthvars::transform_raw_ds_for_analysis() %>%
  dplyr::rename(phq9_total = PHQ9,
                bads_total = BADS,
                gad7_total = GAD7,
                oasis_total = OASIS,
                scared_total = SCARED,
                k6_total = K6,
                c_sofas = SOFAS) %>%
  dplyr::select(-c("d_agegroup","Gender", "CALD", "Region"))

Docs: Score health utility

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

This below section renders a vignette article from the scorz 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 is illustrated with fake data. The dataset explored in this example should not be used to inform decision-making. Some of the methods illustrated in this AQoL-6D vignette can also be used to score other health utility instruments - see a vignette about scoring EQ-5D.

library(ready4)
library(scorz)

AQoL-6D scoring

To derive a health utility score from the raw responses to a multi-attribute utility instrument it is necessary to implement a scoring algorithm. Scoring algorithms for the Assessment of Quality of Life Six Dimension (AQoL-6D) are publicly available in SPSS format (https://www.aqol.com.au/index.php/scoring-algorithms).

However, to include scoring algorithms in reproducible research workflows, it is desirable to have these algorithms available in open science languages such as R. The scorz package includes ready4 framework model modules of the ready4 youth mental health economic model that provide R implementations of the adult and adolescent versions of the AQoL-6D scoring algorithms.

Ingest data

To begin, we ingest an unscored dataset as an instance of the Ready4useDyad from the ready4use package. In this case we download our data from a remote repository.

X <- ready4use::Ready4useRepos(dv_nm_1L_chr = "fakes",
                               dv_ds_nm_1L_chr = "https://doi.org/10.7910/DVN/W95KED",
                               dv_server_1L_chr = "dataverse.harvard.edu") %>%
  ingest(fls_to_ingest_chr = "ymh_clinical_dyad_r4",
         metadata_1L_lgl = F)

To make the ingested dataset easier to interpret, we can add labels from the dictionary.

X <- X %>%
  renew(type_1L_chr = "label")

We can now inspect our ingested dataset using the exhibit method.

exhibit(X,
        display_1L_chr = "head",
         scroll_box_args_ls = list(width = "100%"))

Dataset
Unique client identifier	Round of data collection	Date of data collection	Age	Gender	Sex at birth	Sexual orientation	Aboriginal or Torres Strait Islander	Country Of birth	Speaks English at home	Native English speaker	Education and employment status	Relationship status	Service centre name	Primary diagnosis	Clinical stage	Kessler Psychological Distress Scale (6 Dimension)	Patient Health Questionnaire	Behavioural Activation for Depression Scale	Generalised Anxiety Disorder Scale	Overall Anxiety Severity and Impairment Scale	Screen for Child Anxiety Related Disorders	Social and Occupational Functioning Assessment Scale	Assessment of Quality of Life (6 Dimension) question 1	Assessment of Quality of Life (6 Dimension) question 2	Assessment of Quality of Life (6 Dimension) question 3	Assessment of Quality of Life (6 Dimension) question 4	Assessment of Quality of Life (6 Dimension) question 5	Assessment of Quality of Life (6 Dimension) question 6	Assessment of Quality of Life (6 Dimension) question 7	Assessment of Quality of Life (6 Dimension) question 8	Assessment of Quality of Life (6 Dimension) question 9	Assessment of Quality of Life (6 Dimension) question 10	Assessment of Quality of Life (6 Dimension) question 11	Assessment of Quality of Life (6 Dimension) question 12	Assessment of Quality of Life (6 Dimension) question 13	Assessment of Quality of Life (6 Dimension) question 14	Assessment of Quality of Life (6 Dimension) question 15	Assessment of Quality of Life (6 Dimension) question 16	Assessment of Quality of Life (6 Dimension) question 17	Assessment of Quality of Life (6 Dimension) question 18	Assessment of Quality of Life (6 Dimension) question 19	Assessment of Quality of Life (6 Dimension) question 20
Participant_1	Baseline	2020-03-22	14	Male	Male	Heterosexual	No	Australia	Yes	Yes	Not studying or working	In a relationship	Southport	Other	0-1a	8	7	96	6	6	28	69	2	3	1	2	3	1	1	2	4	3	3	4	2	4	2	2	2	2	2	1
Participant_2	Baseline	2020-06-15	19	Female	Female	Heterosexual	Yes	Other	No	No	Studying only	In a relationship	Regional Centre	Anxiety	0-1a	13	13	63	12	12	41	58	3	3	1	1	3	2	1	3	2	4	4	3	4	3	1	2	2	2	1	1
Participant_3	Baseline	2020-08-20	21	Female	Female	Other	NA	NA	NA	NA	Studying only	Not in a relationship	Canberra	Anxiety	1b	12	17	72	16	12	43	72	2	3	2	5	1	1	1	2	4	5	2	4	2	2	2	1	1	1	1	1
Participant_4	Baseline	2020-05-23	12	Female	Female	Heterosexual	Yes	Other	No	No	Not studying or working	In a relationship	Southport	Depression and Anxiety	2-4	17	17	75	12	10	51	88	1	2	1	1	3	3	1	4	4	3	3	3	4	2	1	1	2	1	3	1
Participant_5	Baseline	2020-04-05	19	Male	Male	Heterosexual	Yes	Other	No	No	Not studying or working	Not in a relationship	Southport	Depression and Anxiety	0-1a	12	22	82	14	14	51	67	2	2	1	3	5	1	1	1	1	5	4	4	3	2	1	2	1	3	2	3
Participant_6	Baseline	2020-06-09	19	Male	Male	Heterosexual	Yes	Other	No	No	Studying only	In a relationship	Regional Centre	Anxiety	1b	11	8	105	8	3	46	60	1	2	2	1	2	2	4	1	3	3	4	3	4	2	1	2	1	2	1	1

We now add meta-data that identifies our dataset as being longitudinal using the YouthvarsSeries module of the youthvars package.

X <- youthvars::YouthvarsSeries(a_Ready4useDyad = X,
                                id_var_nm_1L_chr = "fkClientID",
                                timepoint_var_nm_1L_chr = "round",
                                timepoint_vals_chr = levels(X@ds_tb$round))

We now use the data and meta-data we have created in the previous steps to create an instance of the ScorzAqol6Adol class. This class is specifically designed to facilitate scoring of the adolescent version of the AQoL-6D instrument.

Y <- ScorzAqol6Adol(a_YouthvarsProfile = X)

By default, instances of the ScorzAqol6Adol class are created with a slot specifying a value for the prefix for AQoL-6D questionnaire item responses.

procureSlot(Y,
            slot_nm_1L_chr = "itm_prefix_1L_chr")
#> [1] "aqol6d_q"

If this default value needs to be updated to match the prefix used in your dataset, use the renewSlot method.

# Not run
# Y <- renewSlot(Y, slot_nm_1L_chr = "itm_prefix_1L_chr", new_val_xx = "new_prefix")

Calculating scores

To calculate AQoL 6D adolescent utility scores, use the renew method.

Y <- renew(Y)

Viewing the updated dataset

We can inspect our updated dataset using the exhibit method. We can see that the updated dataset now has additional variables that include the intermediate and final calculations for AQoL-6D adolescent utility scores.

exhibit(Y,
        display_1L_chr = "head",
         scroll_box_args_ls = list(width = "100%"))

Dataset
Unique client identifier	Round of data collection	Date of data collection	Age	Gender	Sex at birth	Sexual orientation	Aboriginal or Torres Strait Islander	Country Of birth	Speaks English at home	Native English speaker	Education and employment status	Relationship status	Service centre name	Primary diagnosis	Clinical stage	Kessler Psychological Distress Scale (6 Dimension)	Patient Health Questionnaire	Behavioural Activation for Depression Scale	Generalised Anxiety Disorder Scale	Overall Anxiety Severity and Impairment Scale	Screen for Child Anxiety Related Disorders	Social and Occupational Functioning Assessment Scale	Assessment of Quality of Life (6 Dimension) question 1	Assessment of Quality of Life (6 Dimension) question 2	Assessment of Quality of Life (6 Dimension) question 3	Assessment of Quality of Life (6 Dimension) question 4	Assessment of Quality of Life (6 Dimension) question 5	Assessment of Quality of Life (6 Dimension) question 6	Assessment of Quality of Life (6 Dimension) question 7	Assessment of Quality of Life (6 Dimension) question 8	Assessment of Quality of Life (6 Dimension) question 9	Assessment of Quality of Life (6 Dimension) question 10	Assessment of Quality of Life (6 Dimension) question 11	Assessment of Quality of Life (6 Dimension) question 12	Assessment of Quality of Life (6 Dimension) question 13	Assessment of Quality of Life (6 Dimension) question 14	Assessment of Quality of Life (6 Dimension) question 15	Assessment of Quality of Life (6 Dimension) question 16	Assessment of Quality of Life (6 Dimension) question 17	Assessment of Quality of Life (6 Dimension) question 18	Assessment of Quality of Life (6 Dimension) question 19	Assessment of Quality of Life (6 Dimension) question 20	Assessment of Quality of Life (6 Dimension) item disvalue1	Assessment of Quality of Life (6 Dimension) item disvalue2	Assessment of Quality of Life (6 Dimension) item disvalue3	Assessment of Quality of Life (6 Dimension) item disvalue4	Assessment of Quality of Life (6 Dimension) item disvalue5	Assessment of Quality of Life (6 Dimension) item disvalue6	Assessment of Quality of Life (6 Dimension) item disvalue7	Assessment of Quality of Life (6 Dimension) item disvalue8	Assessment of Quality of Life (6 Dimension) item disvalue9	Assessment of Quality of Life (6 Dimension) item disvalue10	Assessment of Quality of Life (6 Dimension) item disvalue11	Assessment of Quality of Life (6 Dimension) item disvalue12	Assessment of Quality of Life (6 Dimension) item disvalue13	Assessment of Quality of Life (6 Dimension) item disvalue14	Assessment of Quality of Life (6 Dimension) item disvalue15	Assessment of Quality of Life (6 Dimension) item disvalue16	Assessment of Quality of Life (6 Dimension) item disvalue17	Assessment of Quality of Life (6 Dimension) item disvalue18	Assessment of Quality of Life (6 Dimension) item disvalue19	Assessment of Quality of Life (6 Dimension) item disvalue20	Disvalue Score for Dimension 1 - Independent Living	Disvalue Score for Dimension 2 - Relationships	Disvalue Score for Dimension 3 - Mental Health	Disvalue Score for Dimension 4 - Coping	Disvalue Score for Dimension 5 - Pain	Disvalue Score for Dimension 6 - Senses	Adult Score Dimension 1 - Independent Living	Adult Score Dimension 2 - Relationships	Adult Score Dimension 3 - Mental Health	Adult Score Dimension 4 - Coping	Adult Score Dimension 5 - Pain	Adult Score Dimension 6 - Senses	Overall score on a 0-1 disvalue scale	Overall score on a life-death disutility scale	AQoL-6D Adolescent Disutility Score (Untransformed)	AQoL-6D Adolescent Disutility Score (Transformed)	Instrument utility score	Instrument utility score rotated	AQOL-6D (weighted total)	AQOL-6D (unweighted total)
Participant_1	Baseline	2020-03-22	14	Male	Male	Heterosexual	No	Australia	Yes	Yes	Not studying or working	In a relationship	Southport	Other	0-1a	8	7	96	6	6	28	69	2	3	1	2	3	1	1	2	4	3	3	4	2	4	2	2	2	2	2	1	0.073	0.240	0.000	0.040	0.461	0.000	0.000	0.133	0.824	0.330	0.368	0.722	0.055	0.826	0.133	0.2	0.072	0.033	0.024	0.000	0.19334101	0.2964368	0.7312060	0.7708396	0.2619285	0.03009428	0.8066590	0.7035632	0.2687940	0.2291604	0.7380715	0.9699057	0.6436897	0.7286568	0.55838936	0.55838936	0.4416106	0.5078265	0.5698492	46
Participant_10	Baseline	2020-08-05	15	Female	Female	Other	Yes	Other	No	No	Studying and working	Not in a relationship	Canberra	Other	0-1a	11	17	34	13	15	38	60	1	2	2	3	5	1	3	3	4	4	3	4	3	3	1	2	2	3	2	1	0.000	0.033	0.041	0.297	1.000	0.000	0.648	0.392	0.824	0.784	0.368	0.722	0.382	0.423	0.000	0.2	0.072	0.223	0.024	0.000	0.27064870	0.7770111	0.8683514	0.6579841	0.1935407	0.13938313	0.7293513	0.2229889	0.1316486	0.3420159	0.8064593	0.8606169	0.7541542	0.8537026	0.74739738	0.74739738	0.2526026	0.3413671	0.3916050	52
Participant_10	Follow-up	2020-11-07	15	Female	Female	Other	Yes	Other	No	No	Not studying or working	Not in a relationship	Regional Centre	Depression	1b	7	17	95	14	10	48	64	2	3	2	1	2	2	2	2	2	3	3	5	3	2	3	1	2	2	3	2	0.073	0.240	0.041	0.000	0.074	0.193	0.197	0.133	0.142	0.330	0.368	1.000	0.382	0.057	0.642	0.0	0.072	0.033	0.205	0.187	0.18835933	0.2602305	0.5155772	0.5858738	0.4342728	0.21476953	0.8116407	0.7397695	0.4844228	0.4141262	0.5657272	0.7852305	0.6473112	0.7327563	0.56418597	0.56418597	0.4358140	0.5027214	0.5645345	47
Participant_100	Baseline	2020-07-19	25	Female	Female	Other	Yes	Other	No	No	Working only	In a relationship	Canberra	Depression and Anxiety	0-1a	7	0	120	3	0	21	76	1	1	1	1	2	1	2	2	2	2	2	2	5	3	2	1	3	1	1	1	0.000	0.000	0.000	0.000	0.074	0.000	0.197	0.133	0.142	0.097	0.064	0.056	1.000	0.423	0.133	0.0	0.338	0.000	0.000	0.000	0.00000000	0.1433888	0.2505682	0.7769222	0.2866694	0.00000000	1.0000000	0.8566112	0.7494318	0.2230778	0.7133306	1.0000000	0.4558633	0.5160373	0.29587849	0.29587849	0.7041215	0.7390198	0.7978085	36
Participant_1000	Baseline	2020-09-06	16	Male	Male	Heterosexual	Yes	Other	No	No	Not studying or working	Not in a relationship	Canberra	Anxiety	0-1a	0	0	128	0	0	0	71	2	1	1	1	1	2	1	2	1	2	2	1	2	3	1	1	1	2	1	1	0.073	0.000	0.000	0.000	0.000	0.193	0.000	0.133	0.000	0.097	0.064	0.000	0.055	0.423	0.000	0.0	0.000	0.033	0.000	0.000	0.02813508	0.1346642	0.1819574	0.3514811	0.0000000	0.01916297	0.9718649	0.8653358	0.8180426	0.6485189	1.0000000	0.9808370	0.2379252	0.2693314	0.08939064	0.08939064	0.9106094	0.9208737	0.9511345	29
Participant_1000	Follow-up	2020-12-20	16	Male	Male	Heterosexual	Yes	Other	No	No	Not studying or working	Not in a relationship	Southport	Anxiety	1b	5	0	117	5	1	14	71	2	2	1	1	1	1	2	1	3	1	2	3	2	2	1	1	1	1	2	1	0.073	0.033	0.000	0.000	0.000	0.000	0.197	0.000	0.392	0.000	0.064	0.338	0.055	0.057	0.000	0.0	0.000	0.000	0.024	0.000	0.04719190	0.1002056	0.2658587	0.2080310	0.0000000	0.01111253	0.9528081	0.8997944	0.7341413	0.7919690	1.0000000	0.9888875	0.2228889	0.2523102	0.07926885	0.07926885	0.9207312	0.9297879	0.9576133	31

Creating summary plots

To create plots, we use the depict method.

We can create a list of summary plots by timepoint for all individual items.

plot_ls <- depict(Y, type_1L_chr = "item_by_time")

We can then select a desired item’s summary plot by using its index number.

plot_ls[[1]]

AQoL-6D Item 1 scores by data-collection round

Alternatively, we can generate individual plots by passing the item index number to the var_idcs_int argument of depict.

depict(Y, type_1L_chr = "item_by_time", var_idcs_int = 2L)

AQoL-6D Item 2 scores by data-collection round

We can also plot domain scores by time.

depict(Y, type_1L_chr = "domain_by_time", var_idcs_int = 1L)

AQoL-6D Independet Living Domain weighted scores by data-collection round

Total AQoL-6D scores can also be plotted using the same approach, where var_idcs_int = 1L is used to plot the weighted total distribution and var_idcs_int = 2L is used for plotting the unweighted total.

depict(Y, type_1L_chr = "total_by_time", var_idcs_int = 1L)

AQoL-6D item total weighted scores by data-collection round

Composite plots can be generated as well, though these are not currently optimised to reliably produce quality plots suitable for publication.

depict(Y, type_1L_chr = "comp_item_by_time")

AQoL-6D item responses by data-collection round

depict(Y, type_1L_chr = "comp_domain_by_time")

AQoL-6D weighted domain scores by data-collection round

We can now publicly share our scored dataset and its associated metadata, using Ready4useRepos and its share method as described in a vignette from the ready4use package.

Z <- ready4use::Ready4useRepos(gh_repo_1L_chr = "ready4-dev/scorz", # Replace with details of your repo.
                               gh_tag_1L_chr = "Documentation_0.0") # You must have write permissions.
Z <- share(Z,
           obj_to_share_xx = Y,
           fl_nm_1L_chr = "ymh_ScorzAqol6Adol")

Y is now available for download as the file ymh_ScorzAqol6Adol.RDS from the “Documentation_0.0” release of the scorz package.