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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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: Create synthetic populations

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

ready4 – Use - synthetic populations