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Analyses
- 1: Decision aids
- 2: Code to reproduce and/or replicate scientific studies
- 2.1: Model youth choices
- 2.1.1: Design a Discrete Choice Experiment
- 2.1.2: Analyse the results of a Discrete Choice Experiment
- 2.2: Create synthetic populations
- 2.3: Model health utility
- 3: Subroutine templates for authoring analysis reports
1 - Decision aids
1.1 - Predicting the spatial epidemiology of emerging mental disorders
The Springtides app reproduced below is currently deprecated, pending a new version to be released in 2023. We don’t encourage use of this app to inform decision making as the input data has become dated and the current web based version often fails if generating large / or customised geometries. The app is reproduced below purely for illustrative purposes. If you try it out, we recommend that you only select the default type of geometry (“Select from a menu of existing options”) as the web version is not configured to render all bar the simplest custom geometries. To use the app you need to first confirm your selections in the “Where” tab, before confirming selections from the “What” box, then from the “Who” box and finally the “When” box before an orange box appears that gives you the option to generate a report. When trying this app out, we recommend keeping the number of simulations low (e.g. 10) as it takes several minutes for even small numbers of runs to execute.
2 - Code to reproduce and/or replicate scientific studies
2.1 - Model youth choices
2.1.1 - Design a Discrete Choice Experiment
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2.1.2 - Analyse the results of a Discrete Choice Experiment
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2.2 - Create synthetic populations
2.2.1 - Create a synthetic population of young people attending primary mental health services
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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.
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")))
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"))2.3 - Model health utility
2.3.1 - Develop health utility mapping algorithms
This below section embeds a PDF version of an R Markdown program. The following alternative options may provide improved viewing experience, more contextual information and access to more useful code formats:
- download the PDF (recommended for enhanced readibility);
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- view the PDF along with the current development version of its R Markdown code (useful if you wish to copy or edit the code)
2.3.2 - Predict health utility
This below section embeds a PDF version of an R Markdown program. The following alternative options may provide improved viewing experience, more contextual information and access to more useful code formats:
- download the PDF (recommended for enhanced readibility);
- view the PDF in its study dataset (includes contextual information) from that article; and;
- view the PDF along with the current release of its R Markdown code (useful if you plan to run the code) and
- view the PDF along with the current development version of its R Markdown code (useful if you wish to copy or edit the code)
3 - Subroutine templates for authoring analysis reports
3.1 - Make a catalogue of utility mapping models
This below section reproduces the README file of an R Markdown sub-routine. The following alternative options may provide more contextual information and access to more useful code formats:
- view examples of the catalogues produced by this subroutine in a study dataset (the names of the relevant files all begin with “AAA_TTU_MDL_CTG”);
- view the README along with the current release of its R Markdown code (useful if you plan to run the code); and
- view the README along with the current development version of its R Markdown code (useful if you wish to copy or edit the code)
ttu_mdl_ctlg
R Markdown subroutine reporting template for creating utility mapping (transfer to utility) model catalogues. This template should be used in conjunction with the TTU R package.
3.2 - Author a template scientific manuscript
This below section reproduces the README file of an R Markdown sub-routine. The following alternative options may provide more contextual information and access to more useful code formats:
- view an illustrative example of a program to call this subroutine (useful to understand how to use it);
- view the README along with the current release of its R Markdown code (useful if you plan to run the code); and
- view the README along with the current development version of its R Markdown code (useful if you wish to copy or edit the code)
ms_tmpl: Generate a template scientific manuscript
3.3 - Author a draft scientific manuscript for a utility mapping study
This below section reproduces the README file of an R Markdown sub-routine. The following alternative options may provide more contextual information and access to more useful code formats:
Create a Draft Scientific Manuscript For A Utility Mapping Study
This sub-routine program extends the R package TTU by providing a toolkit for automatically authoring a first draft of a scientific manuscript from results generated by TTU modules.
The program is intended for use and as the last component of TTU’s reporting workflow for utility mapping modelling projects. An example of this workflow is available at: https://doi.org/10.5281/zenodo.6116077 . This program generalises a program that produced the manuscript for a real world study (https://doi.org/10.1101/2021.07.07.21260129).
The program can produce manuscripts in PDF / LaTex (example - https://dataverse.harvard.edu/api/access/datafile/4957407) and Word (example - https://dataverse.harvard.edu/api/access/datafile/4957416). It should be noted that the Word output requires some manual editing to adapt section numbering, modify table headers and resize tables to page boundaries.
Suggested citation (bibTeX):
@software{hamilton_matthew_2022_6931146, author = {Hamilton, Matthew and Gao, Caroline}, title = {{ttu_lng_ss: Create a Draft Scientific Manuscript For A Utility Mapping Study}}, month = jun, year = 2023, note = {{Matthew Hamilton and Caroline Gao (2022). ttu_lng_ss: Create a Draft Scientific Manuscript For A Utility Mapping Study. Zenodo. https://doi.org/10.5281/zenodo.5976987. Version 0.9.0.0}}, publisher = {Zenodo}, version = {0.9.0.0}, doi = {10.5281/zenodo.5976987}, url = {https://doi.org/10.5281/zenodo.5976987} }
3.4 - Make results summary for a Discrete Choice Experiment
This below section reproduces the README file of an R Markdown sub-routine. The following alternative options may provide more contextual information and access to more useful code formats:
Report results from a Discrete Choice Experiment implemented with the mychoice R package.
Suggested citation (bibTeX):
@software{hamilton_matthew_2022_6931146, author = {Hamilton, Matthew}, title = {{mychoice_results: Report results from a Discrete Choice Experiment implemented with the mychoice R package}}, month = nov, year = 2022, note = {{Matthew Hamilton (2022). mychoice_results: Report results from a Discrete Choice Experiment implemented with the mychoice R package. Zenodo. https://doi.org/10.5281/zenodo.7297904. Version 0.1.0.0}}, version = {0.1.0.0}, doi = {10.5281/zenodo.7297904}, url = {https://doi.org/10.5281/zenodo.7297904} }
