Teradata Package for R Function Reference | 17.00 - CoxHazardRatio - Teradata Package for R - Look here for syntax, methods and examples for the functions included in the Teradata Package for R.

CoxHazardRatio

Description

The CoxHazardRatio function takes as input the coefficient tbl_teradata generated by the function CoxPH (td_coxph_mle) and outputs the hazard ratios between predictive features and either their corresponding reference features or their unit differences.

Usage

  td_cox_hazard_ratio_mle (
      object = NULL,
      predicts = NULL,
      refs = NULL,
      predict.feature.names = NULL,
      predict.feature.columns = NULL,
      predict.feature.units.columns = NULL,
      ref.feature.columns = NULL,
      accumulate = NULL,
      object.sequence.column = NULL,
      predicts.sequence.column = NULL,
      refs.sequence.column = NULL,
      predicts.partition.column = "1",
      refs.partition.column = "1",
      object.order.column = NULL,
      predicts.order.column = NULL,
      refs.order.column = NULL
  )

Arguments

object	Required Argument. Specifies the coefficients tbl_teradata of the model generated by td_coxph_mle. This argument can accept either a tbl_teradata or an object of "td_coxph_mle" class.
object.order.column	Optional Argument. Specifies Order By columns for "object". Values to this argument can be provided as a vector, if multiple columns are used for ordering. Types: character OR vector of Strings (character)
predicts	Required Argument. Specifies the input tbl_teradata defining new feature values or unit changes for prediction.
predicts.partition.column	Optional Argument. Specifies Partition By columns for "predicts". Values to this argument can be provided as a vector, if multiple columns are used for partition. Default Value: "1" Types: character OR vector of Strings (character)
predicts.order.column	Optional Argument. Specifies Order By columns for "predicts". Values to this argument can be provided as a vector, if multiple columns are used for ordering. Types: character OR vector of Strings (character)
refs	Optional Argument. Specifies the input tbl_teradata containing reference feature values.
refs.partition.column	Optional Argument. Specifies Partition By columns for "refs". Values to this argument can be provided as a vector, if multiple columns are used for partition. Default Value: "1" Types: character OR vector of Strings (character)
refs.order.column	Optional Argument. Specifies Order By columns for "refs". Values to this argument can be provided as a vector, if multiple columns are used for ordering. Types: character OR vector of Strings (character)
predict.feature.names	Required Argument. Specifies the names of the features in the Cox coefficient model (the coefficient tbl_teradata generated by the td_coxph_mle function). Types: character OR vector of characters
predict.feature.columns	Optional Argument. Specifies the names of the columns that contain the values of the features in the Cox coefficient model. This argument must specify a column for each feature specified by "predict.feature.names". The ith "predict.feature.names" corresponds to the ith "predict.feature.columns" (values of the prediction variables). Types: character OR vector of Strings (character)
predict.feature.units.columns	Optional Argument. Specifies the names of the columns that contain the unit values of the features in the Cox coefficient model. This argument must specify a column for each feature specified by "predict.feature.names". The ith "predict.feature.names" corresponds to the ith "predict.feature.units.columns". Types: character OR vector of Strings (character)
ref.feature.columns	Optional Argument. Specifies the names of the columns that contain the reference values. This argument must specify a column for each feature specified by "predict.feature.names". The ith "predict.feature.names" corresponds to the ith "ref.feature.columns". The default reference values are the distinct feature-value combinations. Note: The function ignores this argument if you specify "predict.feature.units.columns". Types: character OR vector of Strings (character)
accumulate	Optional Argument. Specifies the names of the columns in the input tbl_teradata specified in "predicts" argument that the function copies to the output tbl_teradata. Types: character OR vector of Strings (character)
object.sequence.column	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "object". The argument is used to ensure deterministic results for functions which produce results that vary from run to run. Types: character OR vector of Strings (character)
predicts.sequence.column	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "predicts". The argument is used to ensure deterministic results for functions which produce results that vary from run to run. Types: character OR vector of Strings (character)
refs.sequence.column	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "refs". The argument is used to ensure deterministic results for functions which produce results that vary from run to run. Types: character OR vector of Strings (character)

Value

Function returns an object of class "td_cox_hazard_ratio_mle" which is a named list containing object of class "tbl_teradata".
Named list member can be referenced directly with the "$" operator using name: result.

Examples

  
    # Get the current context/connection
    con <- td_get_context()$connection
    
    # Load example data.
    loadExampleData("coxhazardratio_example", "lc_new_reference", "lc_new_predictors")
    loadExampleData("coxph_example", "lungcancer")

    # Create object(s) of class "tbl_teradata".
    lungcancer <- tbl(con, "lungcancer")

    # Input table lc_new_predictors is a list of four patients who have been
    # diagnosed with lung cancer.
    lc_new_predictors <- tbl(con,"lc_new_predictors")

    # Generate model table.
    td_coxph_out <- td_coxph_mle(data = lungcancer,
                             feature.columns = c("trt","celltype","karno","diagtime","age","prior"),
                             time.interval.column = "time_int",
                             event.column = "status",
                             categorical.columns = c("trt","celltype","prior")
                             )


    # Example 1 - No Reference Values Provided.
    # This example calculates four hazard ratios for each patient,
    # using individual patient characteristics as a reference.
    td_cox_hazard_ratio_out1 <- td_cox_hazard_ratio_mle(object = td_coxph_out$coefficient.table,
                                            predicts = lc_new_predictors,
                                            predict.feature.names = c("trt", "celltype", "karno",
                                                                      "diagtime", "age", "prior"),
                                            predict.feature.columns = c("trt", "celltype", "karno",
                                                                        "diagtime", "age", "prior"),
                                            accumulate = c("id", "name")
                                            )

    # Example 2: Partition by Name/ID and No Reference Values
    td_cox_hazard_ratio_out2 <- td_cox_hazard_ratio_mle(object = td_coxph_out$coefficient.table,
                                            predicts = lc_new_predictors,
                                            predicts.partition.column=c("id", "name"),
                                            predict.feature.names = c("trt", "celltype", "karno",
                                                                      "diagtime", "age" ,"prior"),
                                            predict.feature.columns = c("trt", "celltype", "karno",
                                                                        "diagtime", "age", "prior"),
                                            accumulate = c("id", "name")
                                            )

    # Example 3:  Use Reference Values
    # Each of the four new patients in the table lc_new_predictors are compared with each of 
    # the attribute reference values provided in the table lc_new_reference, and a hazard ratio 
    # is calculated.

    lc_new_reference <- tbl(con,"lc_new_reference")

    td_cox_hazard_ratio_out3 <- td_cox_hazard_ratio_mle(object=td_coxph_out$coefficient.table,
                                            predicts=lc_new_predictors,
                                            refs=lc_new_reference,
                                            predict.feature.columns=c('trt', 'celltype', 'karno',
                                                                      'diagtime', 'age', 'prior'),
                                            ref.feature.columns=c('trt', 'celltype', 'karno',
                                                                  'diagtime', 'age', 'prior'),
                                            predict.feature.names=c('trt', 'celltype', 'karno',
                                                                    'diagtime', 'age', 'prior'),
                                            accumulate = c("id", "name")
                                            )

    # Example 4: Use Reference values and Partition by id
    # In this example, the new patients in the input table lc_new_predictors
    # are compared with the reference table using partition by id.
    # The hazard ratio is calculated only when the patient's id matches the reference id.

    td_cox_hazard_ratio_out4  <- td_cox_hazard_ratio_mle(object=td_coxph_out$coefficient.table,
                                            predicts=lc_new_predictors,
                                            predicts.partition.column='id',
                                            refs=lc_new_reference,
                                            refs.partition.column='id',
                                            predict.feature.columns=c('trt', 'celltype', 'karno',
                                                                      'diagtime', 'age', 'prior'),
                                            ref.feature.columns=c('trt', 'celltype', 'karno',
                                                                  'diagtime', 'age', 'prior'),
                                            predict.feature.names=c('trt', 'celltype', 'karno',
                                                                    'diagtime', 'age', 'prior'),
                                            accumulate = c("id", "name")
                                            )


     # Example 5: Use Units Values
     # This example increases the variable karno by 10%, decreases the variable age by
     # 10%, leaves the variable diagtime unchanged, and calculates the hazard ratios.

     lc_new_predictors_2 <- lc_new_predictors %>% transmute(id, name, karno = karno * 1.1, 
                                                        diagtime = diagtime * 1, age = age * (0.9))
     td_cox_hazard_ratio_out5  <- td_cox_hazard_ratio_mle(object=td_coxph_out$coefficient.table,
                                              predicts=lc_new_predictors_2,
                                              predict.feature.names=c('karno','diagtime','age'),
                                              predict.feature.units.columns=c('karno','diagtime',
                                                                              'age'),
                                              accumulate = c("id", "name")
                                              )