msmtools (version 2.0.0)

polish: Remove observations with different states occurring at the same time

Description

Fast algorithm to get rid of transitions to different states occurring at the same exact time in an augmented data structure as computed by augment (see 'Details').

Usage

polish(
  data,
  data_key,
  pattern,
  time,
  check_NA = FALSE,
  convert = FALSE,
  verbose = TRUE
)

Arguments

data

A data.table or data.frame object in longitudinal format where each row represents an observation in which the exact starting and ending time of the process are known and recorded. If data is a data.frame, then augment internally casts it to a data.table.

data_key

A keying variable which augment uses to define a key for data. This represents the subject ID (see setkey).

pattern

Either an integer, a factor or a character with 2 or 3 unique values which provides the ID status at the end of the study. pattern has a predefined structure. When 2 values are detected, they must be in the format: 0 = "alive", 1 = "dead". When 3 values are detected, then the format must be: 0 = "alive", 1 = "dead during a transition", 2 = "dead after a transition has ended" (see 'Details').

time

The target time variable to check duplicates. By default it is set to 'augmented_int'.

check_NA

If TRUE, then arguments data_key, pattern, and time are looked up for any missing data and if the function finds any, it stops with error. Default is FALSE.

convert

If TRUE, then the returned object is automatically converted to the class data.frame. This is done in place and comes at very low cost both from running time and memory consumption (see setDF).

verbose

If FALSE, all information produced by print, cat and message are suppressed. Default is TRUE.

Details

The function finds all those cases where two subsequent events for a given subject land on different states but occur at the same time. When this happens, the whole subject, as identified by data_key, is removed from the data. The total number of subjects to be removed is printed out in order to be more informative.

See Also

augment

Examples

# NOT RUN {
# loading data
data( hosp )

# augmenting longitudinal data
hosp_aug = augment( data = hosp, data_key = subj, n_events = adm_number,
                    pattern = label_3, t_start = dateIN, t_end = dateOUT,
                    t_cens = dateCENS )

# cleaning any targeted occurrence
hosp_aug_clean = polish( data = hosp_aug, data_key = subj, pattern = label_3 )

# }