EQUALPrognosis (version 0.1.2)

create_specific_input_parameters: Create specific input parameters

Description

This function converts the analysis details that you provide in a 'csv' file to a list that will be used for analysis. Only valid rows are included in this list. If there are invalid rows, the reasons for rejecting the rows are provided. Any revisions that make the rows valid are performed when possible.

Usage

create_specific_input_parameters(generic_input_parameters, analysis_details_path,
verbose)

Value

outcome

The outcome containing the processing details. If some corrections were made, the corrections are included in the outcome. If some rows were not valid, the reason for the row not being valid is provided.

specific_input_parameters

A list with the information for further analyses.

Arguments

generic_input_parameters

The generic input parameters that you generated with create_generic_input_parameters (recommended) or manually.

analysis_details_path

The path to the 'csv' file containing the following columns. 'name': The name of the analysis. Provide a short name. This will be displayed on graphs and will also be used for naming the analysis. The name must be unique. If the name does not follow the naming convention for R objects, a suitable name is created. 'analysis_title': The title of the analysis to be displayed. If the title is missing, the 'name' is used as the 'analysis_title'. 'develop_model': TRUE, if you want to develop a model; FALSE, if you want to use a scoring system with a predetermined threshold (if applicable). 'predetermined_model_text': You can create the model text from the mandatory and optional predictors and interactions or for finer control of the model, you can provide the model text directly. 'mandatory_predictors': Predictors that must be included in the model. These should be provided even if you provide the 'predetermined_model_text'. 'optional_predictors': Optional predictors that may be included in the model by step. These should be provided even if you provide the 'predetermined_model_text'. 'mandatory_interactions': Interactions that must be included in the model. These should be provided even if you provide the 'predetermined_model_text'. 'optional_interactions': Optional interactions that may be included in the model by step. These should be provided even if you provide the 'predetermined_model_text'. 'model_threshold_method': One of 'youden', 'topleft', 'heuristic'. Please see description in calculate_actual_predicted. 'scoring_system': Name of the pre-existing scoring system. This is ignored if develop_model is TRUE. 'predetermined_threshold': Pre-determined threshold of the pre-existing scoring system. This is mandatory when develop_model is FALSE and when the outcome_type is 'binary' or 'time-to-event'. This is ignored if develop_model is TRUE or when the outcome_type is 'quantitative'. 'higher_values_event': TRUE if higher values of the pre-existing system indicates event and FALSE otherwise. This is mandatory when develop_model is FALSE and when the outcome_type is 'binary' or 'time-to-event'. This is ignored if develop_model is TRUE or when the outcome_type is 'quantitative'.

verbose

TRUE if the outcome message must be displayed and FALSE otherwise.

Author

Kurinchi Gurusamy

Examples

library(survival)
colon$status <- factor(as.character(colon$status))
generic_input_parameters <- create_generic_input_parameters(
  general_title = "Prediction of colon cancer death", simulations = 2000,
  simulations_per_file = 20, seed = 1, df = colon, outcome_name = "status",
  outcome_type = "time-to-event", outcome_time = "time", outcome_count = FALSE,
  verbose = FALSE)$generic_input_parameters
analysis_details <- cbind.data.frame(
  name = c('age', 'single_mandatory_predictor', 'complex_models',
           'complex_models_only_optional_predictors', 'predetermined_model_text'),
  analysis_title = c('Simple cut-off based on age', 'Single mandatory predictor (rx)',
                     'Multiple mandatory and optional predictors',
                     'Multiple optional predictors only', 'Predetermined model text'),
  develop_model = c(FALSE, TRUE, TRUE, TRUE, TRUE),
  predetermined_model_text = c(NA, NA, NA, NA,
  "cph(Surv(time, status) ~ rx * age, data = df_training_complete, x = TRUE, y = TRUE)"),
  mandatory_predictors = c(NA, 'rx', 'rx; differ; perfor; adhere; extent', NA, "rx; age"),
  optional_predictors = c(NA, NA, 'sex; age; nodes', 'rx; differ; perfor', NA),
  mandatory_interactions = c(NA, NA, 'rx; differ; extent', NA, NA),
  optional_interactions = c(NA, NA, 'perfor; adhere; sex; age; nodes', 'rx; differ', NA),
  model_threshold_method = c(NA, 'youden', 'youden', 'youden', 'youden'),
  scoring_system = c('age', NA, NA, NA, NA),
  predetermined_threshold = c('60', NA, NA, NA, NA),
  higher_values_event = c(TRUE, NA, NA, NA, NA)
)
write.csv(analysis_details, paste0(tempdir(), "/analysis_details.csv"),
          row.names = FALSE, na = "")
analysis_details_path <- paste0(tempdir(), "/analysis_details.csv")
# verbose is TRUE as default. If you do not want the outcome displayed, you can
# change this to FALSE
results <- create_specific_input_parameters(
  generic_input_parameters = generic_input_parameters,
  analysis_details_path = analysis_details_path, verbose = TRUE)
specific_input_parameters <- results$specific_input_parameters