prepSurvivalLabels: Prep survival information (labels for all classifictation...

In novasmedley/gbmSpm: Find predictive sequential patterns for clinical glioblastoma patients

Description

Prep survival information (labels for all classifictation tasks) to events.

Usage

prepSurvivalLabels(data)

Arguments

data	dataframe, rows are clinical visits to be classified, columns are features including clinical visits ids and at least the following columns: eventID num, event id survival num, days since baseline status char, patient status at the end of data collection, 'DECEASED' or 'ALIVE'

Value

data dataframe, rows are clinical visits to be classified, columns are features including clinical visits ids and the following added columns:

• daysToDeath num, event id

• survivalIn60 char, 'dead' or 'alive' in 60 days or ~2 months

• survivalIn180 char, 'dead' or 'alive' in 180 days or ~6 months

• survivalIn270 char, 'dead' or 'alive' in 270 days or ~9 months

• survivalIn360 char, 'dead' or 'alive' in 360 days or ~12 months

Examples

data("fake_data")
t <- 'rate'
fake_tumorInfo <- fake_data$events # save tumor location and laterility strings before event cleaning
fake_demo <- fake_data$demo

fake_data$events <- cleanData(fake_data$events, tType = t)

# collect patient info for each event
# note that only eventName, iois, and eventID columns are used for SPM
fake_data <- merge(fake_data$events, fake_data$person, by='iois', all.x=T)
fake_data <- prepDemographics(fake_data, fake_demo) # prep for each event, since age does change
fake_data <- prepSurvivalLabels(fake_data) # get survival labels, these also change

novasmedley/gbmSpm documentation built on May 17, 2019, 10:39 a.m.