ready4 – Software - libraries (youthvars)

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: Add metadata to datasets of individual human records

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

This below section renders a vignette article from the youthvars 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.

library(ready4)
library(youthvars)

Youthvars provides two ready4 framework modules - YouthvarsProfile and YouthvarsSeries that form part of the ready4 economic model of youth mental health. The ready4 modules in youthvars extend the Ready4useDyad module and can be used to help describe key structural properties of youth mental health datasets.

Ingest data

To start we ingest X, a Ready4useDyad (dataset and data dictionary pair) that we can download 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)

Add metadata

If a dataset is cross-sectional or we wish to treat it as if it were (i.e., where data collection rounds are ignored) we can create Y, an instance of the YouthvarsProfile module, to add minimal metadata (the name of the unique identifier variable).

Y <- YouthvarsProfile(a_Ready4useDyad = X, id_var_nm_1L_chr = "fkClientID")

If the temporal dimension of the dataset is important, it may be therefore preferable to instead transform X into a YouthvarsSeries module instance. YouthvarsSeries objects contain all of the fields of YouthvarsProfile objects, but also include additional fields that are specific for longitudinal datasets (e.g. timepoint_var_nm_1L_chr and timepoint_vals_chr that respectively specify the data-collection timepoint variable name and values and participation_var_1L_chr that specifies the desired name of a yet to be created variable that will summarise the data-collection timepoints for which each unit record supplied data).

Z <- YouthvarsSeries(a_Ready4useDyad = X,
                     id_var_nm_1L_chr = "fkClientID",
                     participation_var_1L_chr = "participation",
                     timepoint_vals_chr = c("Baseline","Follow-up"),
                     timepoint_var_nm_1L_chr = "round")

YouthvarsProfile methods

Inspect data

We can now specify the variables that we would like to prepare descriptive statistics for by using the renew method. The variables to be profiled are specified in the profile_chr argument, the number of decimal digits (default = 3) of numeric values in the summary tables to be generated can be specified with nbr_of_digits_1L_int.

Y <- renew(Y, nbr_of_digits_1L_int = 2L, profile_chr = c("d_age","d_sexual_ori_s","d_studying_working"))

We can now view the descriptive statistics we created in the previous step.

Y %>%
  exhibit(profile_idx_int = 1L, scroll_box_args_ls = list(width = "100%"))

We can also plot the distributions of selected variables in our dataset.

depict(Y, var_nms_chr = c("c_sofas"), labels_chr = c("SOFAS"))

SOFAS total scores

YouthvarsSeries methods

Validate data

To explore longitudinal data we need to first use the ratify method to ensure that Z has been appropriately configured for methods examining datasets reporting measures at two timepoints.

Z <- ratify(Z,
            type_1L_chr = "two_timepoints")

Inspect data

We can now specify the variables that we would like to prepare descriptive statistics for using the renew method. The variables to be profiled are specified in arguments beginning with “compare_”. Use compare_ptcpn_chr to compare variables based on whether cases reported data at one or both timepoints and compare_by_time_chr to compare the summary statistics of variables by timepoints, e.g at baseline and follow-up. If you wish these comparisons to report p values, then use the compare_ptcpn_with_test_chr and compare_by_time_with_test_chr arguments.

Z <- renew(Z,
           compare_by_time_chr = c("d_age","d_sexual_ori_s","d_studying_working"),
           compare_by_time_with_test_chr = c("k6_total", "phq9_total", "bads_total"),
           compare_ptcpn_with_test_chr = c("k6_total", "phq9_total", "bads_total"))

The tables generated in the preceding step can be inspected using the exhibit method.

Z %>%
  exhibit(profile_idx_int = 1L,
          scroll_box_args_ls = list(width = "100%"))

Z %>%
  exhibit(profile_idx_int = 2L,
          scroll_box_args_ls = list(width = "100%"))

Z %>%
  exhibit(profile_idx_int = 3L,
          scroll_box_args_ls = list(width = "100%"))

The depict method can create plots, comparing numeric variables by timepoint.

depict(Z,
       type_1L_chr = "by_time",
       var_nms_chr = c("c_sofas"),
       label_fill_1L_chr = "Time",#
       labels_chr = c("SOFAS"),#
       y_label_1L_chr = "")

SOFAS total scores by data collection round

If and only if the dataset you are working with is appropriate for public dissemination (e.g. is synthetic data), you can use the following workflow for sharing it. We can share the dataset we created for this example using the share method, specifying the repository to which we wish to publish the dataset (and for which we have write permissions) in a (Ready4useRepos object).

A <- Ready4useRepos(gh_repo_1L_chr = "ready4-dev/youthvars", # Replace with your repository 
                          gh_tag_1L_chr = "Documentation_0.0"), # (need write permissions).
A <- share(A,
           obj_to_share_xx = Z,
           fl_nm_1L_chr = "ymh_YouthvarsSeries")

Z is now available for download as the file ymh_YouthvarsSeries.RDS from the “Documentation_0.0” release of the youthvars package.

Docs: Validate variable total scores

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

This below section renders a vignette article from the youthvars 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)
library(youthvars)

Variable classes and data integrity

The youthvars package includes a number of ready4 framework sub-module classes that form part of the ready4 economic model of youth mental health. The primary use of youthvars sub-modules is to quality assure the variables used in model input and output datasets by:

facilitating automated data integrity checks that verify no impermissible values (e.g. utility scores greater than one) are present in source data, transformed data or results; and
implementing rules-based automated selection and application of appropriate methods for each dataset variable.

Included sub-module classes

The initial set of sub-module classes included in the youthvars package are one class for Assessment of Quality of Life (Adolescent) health utility and one for each of the predictors used in the utility prediction algorithms included in the related youthu package.

Assessment of Quality of Life Six Dimension (Adolescent) Health Utility

The youthvars_aqol6d_adol class is defined for numeric vectors with a minimum value of 0.03 and maximum value of 1.0.

youthvars_aqol6d_adol(0.4)
#> [1] 0.4
#> attr(,"class")
#> [1] "youthvars_aqol6d_adol" "numeric"

youthvars_aqol6d_adol(c(0.03,0.2,1))
#> [1] 0.03 0.20 1.00
#> attr(,"class")
#> [1] "youthvars_aqol6d_adol" "numeric"

Non numeric objects and values outside these ranges will produce errors.

youthvars_aqol6d_adol("0.5")
#> Error in make_new_youthvars_aqol6d_adol(x): is.numeric(x) is not TRUE

youthvars_aqol6d_adol(-0.1)
#> Error: All non-missing values in valid youthvars_aqol6d_adol object must be greater than or equal to 0.03.

youthvars_aqol6d_adol(1.2)
#> Error: All non-missing values in valid youthvars_aqol6d_adol object must be less than or equal to 1.

Child Health Utility Nine Dimension - Australian Adolescent Scoring

The youthvars_chu9d_adolaus class is defined for numeric vectors with a minimum value of -0.2118 and maximum value of 1.0.

youthvars_chu9d_adolaus(0.4)
#> [1] 0.4
#> attr(,"class")
#> [1] "youthvars_chu9d_adolaus" "numeric"

youthvars_chu9d_adolaus(c(0.03,0.2,1))
#> [1] 0.03 0.20 1.00
#> attr(,"class")
#> [1] "youthvars_chu9d_adolaus" "numeric"

Non numeric objects and values outside these ranges will produce errors.

youthvars_chu9d_adolaus("0.5")
#> Error in make_new_youthvars_chu9d_adolaus(x): is.numeric(x) is not TRUE

youthvars_chu9d_adolaus(-0.3)
#> Error: All non-missing values in valid youthvars_chu9d_adolaus object must be greater than or equal to -0.2118.

youthvars_chu9d_adolaus(1.2)
#> Error: All non-missing values in valid youthvars_chu9d_adolaus object must be less than or equal to 1.

Behavioural Activation for Depression Scale (BADS)

The youthvars_bads class is defined for integer vectors with a minimum value of 0 and maximum value of 150.

youthvars_bads(143L)
#> [1] 143
#> attr(,"class")
#> [1] "youthvars_bads" "integer"

youthvars_bads(as.integer(c(1,15,150)))
#> [1]   1  15 150
#> attr(,"class")
#> [1] "youthvars_bads" "integer"

Non-integers and values outside these ranges will produce errors.

youthvars_bads(22.5)
#> Error in make_new_youthvars_bads(x): is.integer(x) is not TRUE

youthvars_bads(-1L)
#> Error: All non-missing values in valid youthvars_bads object must be greater than or equal to 0.

youthvars_bads(160L)
#> Error: All non-missing values in valid youthvars_bads object must be less than or equal to 150.

Generalised Anxiety Disorder Scale (GAD-7)

The youthvars_gad7 class is defined for integer vectors with a minimum value of 0 and a maximum value of 21.

youthvars_gad7(15L)
#> [1] 15
#> attr(,"class")
#> [1] "youthvars_gad7" "integer"

youthvars_gad7(as.integer(c(0,14,21)))
#> [1]  0 14 21
#> attr(,"class")
#> [1] "youthvars_gad7" "integer"

Non-integers and values outside these ranges will produce errors.

youthvars_gad7(14.6)
#> Error in make_new_youthvars_gad7(x): is.integer(x) is not TRUE

youthvars_gad7(-1L)
#> Error: All non-missing values in valid youthvars_gad7 object must be greater than or equal to 0.

youthvars_gad7(22L)
#> Error: All non-missing values in valid youthvars_gad7 object must be less than or equal to 21.

Kessler Psychological Distress Scale (K6) - Australian Scoring System

The youthvars_k6_aus class is defined for integer vectors with a minimum value of 6 and a maximum value of 30.

youthvars_k6_aus(21L)
#> [1] 21
#> attr(,"class")
#> [1] "youthvars_k6_aus" "integer"

youthvars_k6_aus(as.integer(c(6,13,25)))
#> [1]  6 13 25
#> attr(,"class")
#> [1] "youthvars_k6_aus" "integer"

Non-integers and values outside these ranges will produce errors.

youthvars_k6_aus(11.2)
#> Error in make_new_youthvars_k6_aus(x): is.integer(x) is not TRUE

youthvars_k6_aus(1L)
#> Error: All non-missing values in valid youthvars_k6_aus object must be greater than or equal to 6.

youthvars_k6_aus(31L)
#> Error: All non-missing values in valid youthvars_k6_aus object must be less than or equal to 30.

Kessler Psychological Distress Scale (K6) - US Scoring System

The youthvars_k6 class is defined for integer vectors with a minimum value of 0 and a maximum value of 24.

youthvars_k6(21L)
#> [1] 21
#> attr(,"class")
#> [1] "youthvars_k6" "integer"

youthvars_k6(as.integer(c(0,13,24)))
#> [1]  0 13 24
#> attr(,"class")
#> [1] "youthvars_k6" "integer"

Non-integers and values outside these ranges will produce errors.

youthvars_k6(11.2)
#> Error in make_new_youthvars_k6(x): is.integer(x) is not TRUE

youthvars_k6(-1L)
#> Error: All non-missing values in valid youthvars_k6 object must be greater than or equal to 0.

youthvars_k6(25L)
#> Error: All non-missing values in valid youthvars_k6 object must be less than or equal to 24.

Kessler Psychological Distress Scale (K10) - Australian Scoring System

The youthvars_k10_aus class is defined for integer vectors with a minimum value of 10 and a maximum value of 50.

youthvars_k10_aus(21L)
#> [1] 21
#> attr(,"class")
#> [1] "youthvars_k10_aus" "integer"

youthvars_k10_aus(as.integer(c(13,25,41)))
#> [1] 13 25 41
#> attr(,"class")
#> [1] "youthvars_k10_aus" "integer"

Non-integers and values outside these ranges will produce errors.

youthvars_k10_aus(11.2)
#> Error in make_new_youthvars_k10_aus(x): is.integer(x) is not TRUE

youthvars_k10_aus(9L)
#> Error: All non-missing values in valid youthvars_k10_aus object must be greater than or equal to 10.

youthvars_k10_aus(51L)
#> Error: All non-missing values in valid youthvars_k10_aus object must be less than or equal to 50.

Kessler Psychological Distress Scale (K10) - US Scoring System

The youthvars_k10 class is defined for integer vectors with a minimum value of 0 and a maximum value of 40.

youthvars_k10(21L)
#> [1] 21
#> attr(,"class")
#> [1] "youthvars_k10" "integer"

youthvars_k10(as.integer(c(0,13,34)))
#> [1]  0 13 34
#> attr(,"class")
#> [1] "youthvars_k10" "integer"

Non-integers and values outside these ranges will produce errors.

youthvars_k10(11.2)
#> Error in make_new_youthvars_k10(x): is.integer(x) is not TRUE

youthvars_k10(-1L)
#> Error: All non-missing values in valid youthvars_k10 object must be greater than or equal to 0.

youthvars_k10(41L)
#> Error: All non-missing values in valid youthvars_k10 object must be less than or equal to 40.

Overall Anxiety Severity and Impairment Scale (OASIS)

The youthvars_oasis class is defined for integer vectors with a minimum value of 0 and a maximum value of 20.

youthvars_oasis(15L)
#> [1] 15
#> attr(,"class")
#> [1] "youthvars_oasis" "integer"

youthvars_oasis(as.integer(c(0,12,20)))
#> [1]  0 12 20
#> attr(,"class")
#> [1] "youthvars_oasis" "integer"

Non-integers and values outside these ranges will produce errors.

youthvars_oasis(14.2)
#> Error in make_new_youthvars_oasis(x): is.integer(x) is not TRUE

youthvars_oasis(-1L)
#> Error: All non-missing values in valid youthvars_oasis object must be greater than or equal to 0.

youthvars_oasis(21L)
#> Error: All non-missing values in valid youthvars_oasis object must be less than or equal to 20.

Patient Health Questionnaire (PHQ-9)

The youthvars_phq9 class is defined for integer vectors with a minimum value of 0 and a maximum value of 27.

youthvars_phq9(11L)
#> [1] 11
#> attr(,"class")
#> [1] "youthvars_phq9" "integer"

youthvars_phq9(as.integer(c(0,13,27)))
#> [1]  0 13 27
#> attr(,"class")
#> [1] "youthvars_phq9" "integer"

Non-integers and values outside these ranges will produce errors.

youthvars_phq9(15.2)
#> Error in make_new_youthvars_phq9(x): is.integer(x) is not TRUE

youthvars_phq9(-1L)
#> Error: All non-missing values in valid youthvars_phq9 object must be greater than or equal to 0.

youthvars_phq9(28L)
#> Error: All non-missing values in valid youthvars_phq9 object must be less than or equal to 27.

The youthvars_scared class is defined for integer vectors with a minimum value of 0 and a maximum value of 82.

youthvars_scared(77L)
#> [1] 77
#> attr(,"class")
#> [1] "youthvars_scared" "integer"

youthvars_scared(as.integer(c(0,42,82)))
#> [1]  0 42 82
#> attr(,"class")
#> [1] "youthvars_scared" "integer"

Non-integers and values outside these ranges will produce errors.

youthvars_scared(33.2)
#> Error in make_new_youthvars_scared(x): is.integer(x) is not TRUE

youthvars_scared(-1L)
#> Error: All non-missing values in valid youthvars_scared object must be greater than or equal to 0.

youthvars_scared(83)
#> Error in make_new_youthvars_scared(x): is.integer(x) is not TRUE

The youthvars_sofas class is defined for integer vectors with a minimum value of 0 and a maximum value of 100.

youthvars_sofas(44L)
#> [1] 44
#> attr(,"class")
#> [1] "youthvars_sofas" "integer"

youthvars_sofas(as.integer(c(0,23,89)))
#> [1]  0 23 89
#> attr(,"class")
#> [1] "youthvars_sofas" "integer"

Non-integers and values outside these ranges will produce errors.

youthvars_sofas(73.2)
#> Error in make_new_youthvars_sofas(x): is.integer(x) is not TRUE

youthvars_sofas(-1L)
#> Error: All non-missing values in valid youthvars_sofas object must be greater than or equal to 0.

youthvars_sofas(103L)
#> Error: All non-missing values in valid youthvars_sofas object must be less than or equal to 100.

ready4 – Software - libraries (youthvars)

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

Introduction

Install required R packages

Specify parameters to generate outcome fake data

AQoL item response parameters

Outcome variable correlation parameters

Generate fake data

Create fake outcome variable datasets

Create fake descriptive variables

Combine datasets

Docs: Add metadata to datasets of individual human records

Ingest data

Add metadata

YouthvarsProfile methods

Inspect data

YouthvarsSeries methods

Validate data

Inspect data

Share data

Docs: Validate variable total scores

Variable classes and data integrity

Included sub-module classes

Assessment of Quality of Life Six Dimension (Adolescent) Health Utility

Child Health Utility Nine Dimension - Australian Adolescent Scoring

Behavioural Activation for Depression Scale (BADS)

Generalised Anxiety Disorder Scale (GAD-7)

Kessler Psychological Distress Scale (K6) - Australian Scoring System

Kessler Psychological Distress Scale (K6) - US Scoring System

Kessler Psychological Distress Scale (K10) - Australian Scoring System

Kessler Psychological Distress Scale (K10) - US Scoring System

Overall Anxiety Severity and Impairment Scale (OASIS)

Patient Health Questionnaire (PHQ-9)

Screen for Child Anxiety Related Disorders (SCARED)

Social and Occupational Functioning Assessment Scale (SOFAS)