Event Detection Using Confidence Bounds.

edecob

The edecob package can detect sustained change in digital biomarker data. We account for noise using an autoregressive model and use confidence bounds to detect the change.

Example

library(edecob)

# Let us examine the example_data dataset
head(example_data, 3)
#>     subject study_day jump_height detect_lower detect_upper
#> 1 Subject 1         1    55.60844         -Inf     54.41227
#> 2 Subject 1         4    57.77688         -Inf     54.41227
#> 3 Subject 1         7    57.59584         -Inf     54.41227

# We apply the main fuction of the package onto our example_data
example_event <- edecob(example_data, med_win = c(-21,21), bt_tot_rep = 50,
                        min_change_dur = 50)
#> Warning in edecob(example_data, med_win = c(-21, 21), bt_tot_rep = 50,
#> min_change_dur = 50) :
#>   Removing rows where value is NA
names(example_event)
#> [1] "Subject 1"  "Subject 2"  "Subject 3"  "event_info"

# example_event contains the event data for each source
plot(example_event$`Subject 1`)
plot(example_event$`Subject 2`)
plot(example_event$`Subject 3`)

# example_event also contains a data frame containing the event information for all patients
example_event$event_info
#>           event_detected event_onset event_duration event_stop
#> Subject 1           TRUE         169             87       TRUE
#> Subject 2           TRUE         205             51       TRUE
#> Subject 3          FALSE         306             38      FALSE

# Using this data frame, we can draw a survival plot
library("survival")
plot(survfit(Surv(time = event_onset, event = event_detected) ~ 1,
             data = example_event$event_info),
     conf.int = FALSE, xlim = c(0,350), ylim = c(0,1), mark.time = TRUE,
     xlab = "Study Day", ylab = "Survival Probability", main = "Survival plot")

Methodology based on Bühlmann, P. (1998). Sieve Bootstrap for Smoothing in Nonstationary Time Series. The Annals of Statistics, 26(1), 48-83.