filter_dataTTS {ushr}

Prepare input data for non-parametric TTS calculations.

Description

This function prepares the raw input data for TTS interpolation. Individuals whose data do not meet specific inclusion criteria are removed (see Vignette for more details).

Usage

filter_dataTTS(data, suppression_threshold = 20, uppertime = 365,
  censor_value = 10, decline_buffer = 500, initial_buffer = 3)

Arguments

data

raw data set. Must be a data frame with the following columns: 'id' - stating the unique identifier for each subject; 'vl' - numeric vector stating the viral load measurements for each subject; 'time' - numeric vector stating the time at which each measurement was taken.

suppression_threshold

numeric value indicating the suppression threshold: measurements below this value will be assumed to represent viral suppression. Typically this would be the detection threshold of the assay. Default value is 20.

uppertime

the maximum time point to include in the analysis. Subjects who do not suppress viral load below the suppression threshold within this time will be discarded from model fitting. Units are assumed to be the same as the 'time' column. Default value is 365.

censor_value

positive numeric value indicating the maximum time point to include in the analysis. Subjects who do not suppress viral load below the detection threshold within this time will be discarded. Units are assumed to be the same as the 'time' column. Default value is 365.

decline_buffer

the maximum allowable deviation of values away from a strictly decreasing sequence in viral load. This allows for e.g. measurement noise and small fluctuations in viral load. Default value is 500.

initial_buffer

numeric (integer) value indicating the maximum number of initial observations from which the beginning of each trajectory will be chosen. Default value is 3.

Details

Steps include: 1. Setting values below the suppression threshold to half the suppression threshold (following standard practice). 2. Filtering out subjects who do not suppress viral load below the suppression threshold by a certain time. 3. Filtering out subjects who do not have a decreasing sequence of viral load (within some buffer range).

Examples

set.seed(1234567)

simulated_data <- simulate_data(nsubjects = 20)

filter_dataTTS(data = simulated_data)