In ready4-dev/TTU: Implement Transfer to Utility Mapping Algorithms
knitr::opts_chunk$set(eval = F)
\blandscape
library(TTU)
Input parameters
Reporting parameters
Bibliograhic data
# authors_tb <- ready4show::ready4show_authors() %>%
# tibble::add_case(first_nm_chr = "Alejandra",
# middle_nm_chr = "Rocio",
# last_nm_chr = "Scienceace",
# title_chr = "Dr",
# qualifications_chr = "MD, PhD",
# institute_chr = "Institute_A, Institute_B",
# sequence_int = 1,
# is_corresponding_lgl = T,
# email_chr = "fake_email@fake_institute.com") %>%
# tibble::add_case(first_nm_chr = "Fionn",
# middle_nm_chr = "Seamus",
# last_nm_chr = "Researchchamp",
# title_chr = "Prof",
# qualifications_chr = "MSc, PhD",
# institute_chr = "Institute_C, Institute_B",
# sequence_int = 2,
# email_chr = "fake_email@fake_institute.com")
# institutes_tb <- ready4show::ready4show_institutes() %>%
# tibble::add_case(short_name_chr = "Institute_A", long_name_chr = "Awesome University, Shanghai") %>%
# tibble::add_case(short_name_chr = "Institute_B", long_name_chr = "August Institution, London") %>%
# tibble::add_case(short_name_chr = "Institute_C", long_name_chr = "Highly Ranked Uni, Montreal")
# header_yaml_args_ls <- ready4show::make_header_yaml_args_ls(authors_tb = authors_tb,
# institutes_tb = institutes_tb,
# title_1L_chr = "A hypothetical study using fake data for instructional purposes only",
# keywords_chr = c("this","is","a","replication","using","fake","data","do", "not","cite"))
Report formatting
# output_format_ls <- ready4show::make_output_format_ls(manuscript_outp_1L_chr = "PDF",
# manuscript_digits_1L_int = 2L,
# supplementary_outp_1L_chr = "PDF",
# supplementary_digits_1L_int = 2L)
Data parameters
Dataset
# ds_tb <- youthvars::replication_popl_tb %>%
# youthvars::transform_raw_ds_for_analysis()
Data dictionary
# dictionary_tb <- youthvars::make_final_repln_ds_dict()
Dataset metadata
# ds_descvs_ls <- make_ds_descvs_ls(candidate_predrs_chr = c("K6","PHQ9"),
# candidate_covar_nms_chr = c("d_age", "SOFAS",
# "c_p_diag_s",
# "c_clinical_staging_s"),
# cohort_descv_var_nms_chr = c("d_age",
# "d_relation_s",
# "d_studying_working",
# "c_p_diag_s",
# "c_clinical_staging_s",
# "SOFAS"),
# dictionary_tb = dictionary_tb,
# id_var_nm_1L_chr = "fkClientID",
# is_fake_1L_lgl = T,
# msrmnt_date_var_nm_1L_chr = "d_interview_date",
# round_var_nm_1L_chr = "round",
# round_vals_chr = c("Baseline", "Follow-up"),
# maui_item_pfx_1L_chr = "aqol6d_q",
# utl_wtd_var_nm_1L_chr = "aqol6d_total_w",
# utl_unwtd_var_nm_1L_chr = "aqol6d_total_c")
Candidate predictors metadata
# predictors_lup <- make_pt_TTU_predictors_lup(short_name_chr = c(ds_descvs_ls$candidate_predrs_chr, "SOFAS"),
# long_name_chr = c("K6 total score", "PHQ9 total score", "SOFAS total score"),
# min_val_dbl = 0,
# max_val_dbl = c(24,27,100),
# class_chr = "integer",
# increment_dbl = 1,
# class_fn_chr = c("youthvars::youthvars_k6",
# "youthvars::youthvars_phq9",
# "youthvars::youthvars_sofas"),
# mdl_scaling_dbl = 0.01,
# covariate_lgl = F) %>%
# TTU_predictors_lup()
Multi-Attribute Utility Instrument (MAUI) parameters
# maui_params_ls <- make_maui_params_ls(maui_domains_pfxs_1L_chr = "vD",
# maui_itm_short_nms_chr = c("Household tasks",
# "Getting around","Morbility",
# "Self care","Enjoy close rels",
# "Family rels",
# "Community involvement", "Despair",
# "Worry", "Sad", "Agitated",
# "Energy level","Control",
# "Coping","Frequency of pain",
# "Degree of pain",
# "Pain interference", "Vision",
# "Hearing", "Communication"),
# maui_scoring_fn = youthvars::add_adol6d_scores,
# short_and_long_nm = c("AQoL-6D",
# "Assessment of Quality of Life - Six Dimension"),
# utl_min_val_1L_dbl = 0.03)
Analysis parameters
# scndry_anlys_params_ls <- make_scndry_anlys_params(candidate_predrs_chr = c("SOFAS"),
# prefd_covars_chr = NA_character_)
# input_params_ls <- make_input_params(ds_tb,
# ds_descvs_ls = ds_descvs_ls,
# dv_ds_nm_and_url_chr = c("fakes",
# "https://doi.org/10.7910/DVN/D74QMP"),
# header_yaml_args_ls = header_yaml_args_ls,
# maui_params_ls = maui_params_ls,
# output_format_ls = output_format_ls,
# predictors_lup = predictors_lup,
# prefd_covars_chr = "SOFAS",
# prefd_mdl_types_chr = c("OLS_CLL","GLM_GSN_LOG"),
# scndry_anlys_params_ls = scndry_anlys_params_ls)
Analyse, Report and Share
Run analysis
write_analyses(input_params_ls)
Report results
input_params_ls <- write_mdl_smry_rprt(input_params_ls,
use_shareable_mdls_1L_lgl = T)
Share results
write_study_outp_ds(input_params_ls)
Create manuscript
Create study metadata
# input_params_ls <- make_study_descs_ls(input_params_ls = input_params_ls,
# background_1L_chr = "Our study is entirely fictional and has been created to illustrate TTU package functionality.",
# coi_1L_chr = "None declared.",
# conclusion_1L_chr = "If this study was real, the results would be interesting.",
# ethics_1L_chr = "The study was reviewed and granted approval by Awesome University's Human Research Ethics Committee (1111111.1).",
# funding_1L_chr = "The study was funded by Generous Benefactor.",
# sample_desc_1L_chr = "The study sample is fake data that pretends to be young people aged 12 to 25 years who attended Australian primary care services for mental health related needs between November 2019 to August 2020.",
# time_btwn_bl_and_fup_1L_chr = "three months",
# var_nm_change_lup = tibble::tibble(old_nms_chr = c("PHQ9","GAD7"),
# new_nms_chr = c("PHQ-9",
# "GAD-7")))
Render auto-generated first-draft
results_ls <- ready4show::write_manuscript(input_params_ls = input_params_ls)
Purge dataset copies
write_to_delete_ds_copies(results_ls)
\elandscape
ready4-dev/TTU documentation built on July 2, 2024, 8:12 a.m.
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knitr::opts_chunk$set(eval = F)
\blandscape
library(TTU)
Input parameters
Reporting parameters
Bibliograhic data
# authors_tb <- ready4show::ready4show_authors() %>% # tibble::add_case(first_nm_chr = "Alejandra", # middle_nm_chr = "Rocio", # last_nm_chr = "Scienceace", # title_chr = "Dr", # qualifications_chr = "MD, PhD", # institute_chr = "Institute_A, Institute_B", # sequence_int = 1, # is_corresponding_lgl = T, # email_chr = "fake_email@fake_institute.com") %>% # tibble::add_case(first_nm_chr = "Fionn", # middle_nm_chr = "Seamus", # last_nm_chr = "Researchchamp", # title_chr = "Prof", # qualifications_chr = "MSc, PhD", # institute_chr = "Institute_C, Institute_B", # sequence_int = 2, # email_chr = "fake_email@fake_institute.com")
# institutes_tb <- ready4show::ready4show_institutes() %>% # tibble::add_case(short_name_chr = "Institute_A", long_name_chr = "Awesome University, Shanghai") %>% # tibble::add_case(short_name_chr = "Institute_B", long_name_chr = "August Institution, London") %>% # tibble::add_case(short_name_chr = "Institute_C", long_name_chr = "Highly Ranked Uni, Montreal")
# header_yaml_args_ls <- ready4show::make_header_yaml_args_ls(authors_tb = authors_tb, # institutes_tb = institutes_tb, # title_1L_chr = "A hypothetical study using fake data for instructional purposes only", # keywords_chr = c("this","is","a","replication","using","fake","data","do", "not","cite"))
Report formatting
# output_format_ls <- ready4show::make_output_format_ls(manuscript_outp_1L_chr = "PDF", # manuscript_digits_1L_int = 2L, # supplementary_outp_1L_chr = "PDF", # supplementary_digits_1L_int = 2L)
Data parameters
Dataset
# ds_tb <- youthvars::replication_popl_tb %>% # youthvars::transform_raw_ds_for_analysis()
Data dictionary
# dictionary_tb <- youthvars::make_final_repln_ds_dict()
Dataset metadata
# ds_descvs_ls <- make_ds_descvs_ls(candidate_predrs_chr = c("K6","PHQ9"), # candidate_covar_nms_chr = c("d_age", "SOFAS", # "c_p_diag_s", # "c_clinical_staging_s"), # cohort_descv_var_nms_chr = c("d_age", # "d_relation_s", # "d_studying_working", # "c_p_diag_s", # "c_clinical_staging_s", # "SOFAS"), # dictionary_tb = dictionary_tb, # id_var_nm_1L_chr = "fkClientID", # is_fake_1L_lgl = T, # msrmnt_date_var_nm_1L_chr = "d_interview_date", # round_var_nm_1L_chr = "round", # round_vals_chr = c("Baseline", "Follow-up"), # maui_item_pfx_1L_chr = "aqol6d_q", # utl_wtd_var_nm_1L_chr = "aqol6d_total_w", # utl_unwtd_var_nm_1L_chr = "aqol6d_total_c")
Candidate predictors metadata
# predictors_lup <- make_pt_TTU_predictors_lup(short_name_chr = c(ds_descvs_ls$candidate_predrs_chr, "SOFAS"), # long_name_chr = c("K6 total score", "PHQ9 total score", "SOFAS total score"), # min_val_dbl = 0, # max_val_dbl = c(24,27,100), # class_chr = "integer", # increment_dbl = 1, # class_fn_chr = c("youthvars::youthvars_k6", # "youthvars::youthvars_phq9", # "youthvars::youthvars_sofas"), # mdl_scaling_dbl = 0.01, # covariate_lgl = F) %>% # TTU_predictors_lup()
Multi-Attribute Utility Instrument (MAUI) parameters
# maui_params_ls <- make_maui_params_ls(maui_domains_pfxs_1L_chr = "vD", # maui_itm_short_nms_chr = c("Household tasks", # "Getting around","Morbility", # "Self care","Enjoy close rels", # "Family rels", # "Community involvement", "Despair", # "Worry", "Sad", "Agitated", # "Energy level","Control", # "Coping","Frequency of pain", # "Degree of pain", # "Pain interference", "Vision", # "Hearing", "Communication"), # maui_scoring_fn = youthvars::add_adol6d_scores, # short_and_long_nm = c("AQoL-6D", # "Assessment of Quality of Life - Six Dimension"), # utl_min_val_1L_dbl = 0.03)
Analysis parameters
# scndry_anlys_params_ls <- make_scndry_anlys_params(candidate_predrs_chr = c("SOFAS"), # prefd_covars_chr = NA_character_)
# input_params_ls <- make_input_params(ds_tb, # ds_descvs_ls = ds_descvs_ls, # dv_ds_nm_and_url_chr = c("fakes", # "https://doi.org/10.7910/DVN/D74QMP"), # header_yaml_args_ls = header_yaml_args_ls, # maui_params_ls = maui_params_ls, # output_format_ls = output_format_ls, # predictors_lup = predictors_lup, # prefd_covars_chr = "SOFAS", # prefd_mdl_types_chr = c("OLS_CLL","GLM_GSN_LOG"), # scndry_anlys_params_ls = scndry_anlys_params_ls)
Analyse, Report and Share
Run analysis
write_analyses(input_params_ls)
Report results
input_params_ls <- write_mdl_smry_rprt(input_params_ls, use_shareable_mdls_1L_lgl = T)
Share results
write_study_outp_ds(input_params_ls)
Create manuscript
Create study metadata
# input_params_ls <- make_study_descs_ls(input_params_ls = input_params_ls, # background_1L_chr = "Our study is entirely fictional and has been created to illustrate TTU package functionality.", # coi_1L_chr = "None declared.", # conclusion_1L_chr = "If this study was real, the results would be interesting.", # ethics_1L_chr = "The study was reviewed and granted approval by Awesome University's Human Research Ethics Committee (1111111.1).", # funding_1L_chr = "The study was funded by Generous Benefactor.", # sample_desc_1L_chr = "The study sample is fake data that pretends to be young people aged 12 to 25 years who attended Australian primary care services for mental health related needs between November 2019 to August 2020.", # time_btwn_bl_and_fup_1L_chr = "three months", # var_nm_change_lup = tibble::tibble(old_nms_chr = c("PHQ9","GAD7"), # new_nms_chr = c("PHQ-9", # "GAD-7")))
Render auto-generated first-draft
results_ls <- ready4show::write_manuscript(input_params_ls = input_params_ls)
Purge dataset copies
write_to_delete_ds_copies(results_ls)
\elandscape
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