Biweekly Mean PAP Therapy Adherence of OSA Patients over 1 Year

A simulated longitudinal dataset comprising 500 patients with obstructive sleep apnea (OSA) during their first year on CPAP therapy. The dataset contains the patient usage hours, averaged over 2-week periods.

The daily usage data underlying the downsampled dataset was simulated based on 7 different adherence patterns. The defined adherence patterns were inspired by the adherence patterns identified by Aloia et al. (2008), with slight adjustments

PAP.adh1y

Format

A data.frame comprising longitudinal data of 500 patients, each having 26 observations over a period of 1 year. Each row represents a patient observation interval (two weeks), with columns:

Patient

factor: The patient identifier, where each level represents a simulated patient.

Biweek

integer: Two-week interval index. Starts from 1.

MaxDay

integer: The last day used for the aggregation of the respective interval, integer

UsageHours

numeric: The mean hours of usage in the respective week. Greater than or equal to zero, and typically around 4-6 hours.

Group

factor: The reference group (i.e., adherence pattern) from which this patient was generated.

Source

This dataset was generated based on the cluster-specific descriptive statistics table provided in Aloia et al. (2008), with some adjustments made in order to improve cluster separation for demonstration purposes.

Aloia MS, Goodwin MS, Velicer WF, Arnedt JT, Zimmerman M, Skrekas J, Harris S, Millman RP (2008). “Time series analysis of treatment adherence patterns in individuals with obstructive sleep apnea.” Annals of Behavioral Medicine, 36(1), 44--53. ISSN 0883-6612, doi:10.1007/s12160-008-9052-9 .

Note

This dataset is only intended for demonstration purposes. While the data format will remain the same, the data content is subject to change in future versions.

See also

latrend-data

Examples

data(PAP.adh1y)

if (require("ggplot2")) {
  plotTrajectories(PAP.adh1y, id = "Patient", time = "Biweek", response = "UsageHours")

  # plot according to cluster ground truth
  plotTrajectories(
    PAP.adh1y,
    id = "Patient",
    time = "Biweek",
    response = "UsageHours",
    cluster = "Group"
  )
}