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Predict time at which a targeted event count is achieved

Source: R/tEvents.R
tEvents.Rd

tEvents() is made to match input format with AHR() and to solve for the time at which the expected accumulated events is equal to an input target. Enrollment and failure rate distributions are specified as follows. The piecewise exponential distribution allows a simple method to specify a distribtuion and enrollment pattern where the enrollment, failure and dropout rates changes over time.

Usage

tEvents(
  enrollRates = tibble::tibble(Stratum = "All", duration = c(2, 2, 10), rate = c(3, 6, 9)
    * 5),
  failRates = tibble::tibble(Stratum = "All", duration = c(3, 100), failRate =
    log(2)/c(9, 18), hr = c(0.9, 0.6), dropoutRate = rep(0.001, 2)),
  targetEvents = 150,
  ratio = 1,
  interval = c(0.01, 100)
)

Arguments

enrollRates

Piecewise constant enrollment rates by stratum and time period.

failRates

Piecewise constant control group failure rates, duration for each piecewise constant period, hazard ratio for experimental vs control, and dropout rates by stratum and time period.

targetEvents

The targeted number of events to be achieved.

ratio

Experimental:Control randomization ratio.

interval

An interval that is presumed to include the time at which expected event count is equal to targetEvents.

Value

A tibble with Time (computed to match events in targetEvents), AHR (average hazard ratio), Events (targetEvents input), info (information under given scenarios), and info0 (information under related null hypothesis) for each value of totalDuration input;

Specification

Examples

# ------------------------# 
#      Example 1          #
# ------------------------#
# default
tEvents()
#> # A tibble: 1 × 5
#>    Time   AHR Events  info info0
#>   <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1  14.9 0.787   150.  36.9  37.5

# ------------------------# 
#      Example 2          #
# ------------------------#
# check that result matches a finding using AHR()
# Start by deriving an expected event count
enrollRates <- tibble::tibble(Stratum = "All", duration = c(2, 2, 10), rate = c(3, 6, 9) * 5)
failRates <- tibble::tibble(Stratum = "All", duration = c(3, 100), failRate = log(2) / c(9, 18), 
                            hr = c(.9,.6), dropoutRate = rep(.001, 2))
totalDuration <- 20
xx <- AHR(enrollRates, failRates, totalDuration)
xx
#> # A tibble: 1 × 5
#>    Time   AHR Events  info info0
#>   <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1    20 0.738   208.  51.0  52.1

# Next we check that the function confirms the timing of the final analysis.
tEvents(enrollRates, failRates, 
        targetEvents = xx$Events, interval = c(.5, 1.5) * xx$Time)
#> # A tibble: 1 × 5
#>    Time   AHR Events  info info0
#>   <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1  20.0 0.738   208.  51.0  52.1