Tool Name
Section content first added by: Mat Soukup
EventsNeeded
Tool Description
Section content first added by: Mat Soukup
Provides sample size calculations for the number of events needed to adequately power an event driven trial.
Engine Type (intended to run the script)
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R statistical computing environment
Engine Version
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Written using R version 2.10.1
Operating Environment
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To date, this function has only been evaluated in Microsoft Windows.
Ontology/Categorization and Keywords (tags for searching)
Section content first added by: Mat Soukup
sample size calculation, time to event, survival analysis
Author Name and Email
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Written by Xiao Ding and adapted by Mat Soukup (Mat.Soukup@fda.hhs.gov)
Create Date
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October 2011
Last Modified Date
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No modifications to date
Required Arguments
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-
power: a scalar between 0 and 1 corresponding to the power of the study (default is 0.90)
-
error: a scalar between 0 and 1 corresponding to the two-sided Type I error rate which the hypothesis will be tested (default is 0.05)
-
HR: a scalar of the assumed true hazard ratio of the two treatment groups
-
alt: a scalar or vector of the value of the alternative hypothesis defining the degree of risk (or benefit) to rule out
Optional Arguments
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None
Output / Value Returned
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Returns a list containing the number of events needed (NEN) for the specified arguments as well as the value for the maximum value the point estimate can take and still meet the alternative hypothesis margin (PE.threshold). The value of the alternative hypothesis (margin) is also returned.
Error Return Codes
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Errors are returned if the power or error arguments are not between 0 and 1.
Example Data Source
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No data source is required to use the function
Protocol / Trial Source
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This was developed to provide a simple way of calculating the number of events needed for powering an event driven safety trial designed to rule out some degree of risk.
Basic Usage Example (Call)
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EventsNeeded(power=0.9, error=0.05, HR, alt)
Sample Inputs
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#Call 1 - Power of a 90% assuming a background HR=1 to rule out a two-fold risk
EventsNeeded(power=0.9, error=0.05, HR=1, alt=2.0)
#Call 2 - Power of 90% assuming a background HR = 0.75 to show superiority
EventsNeeded(power=0.9, error=0.05, HR=0.75, alt=1.0)
#Call 3 - Power of 90% assuming a background HR = 1 to rule out several levels of risk
risk <- seq(1.25, 2.0, .25)
EventsNeeded(power=0.9, error=0.05, HR=1, alt=risk)
Sample Results
Section content first added by: Mat Soukup
#Call 1
$NEN
[1] 87.4793
$margin
[1] 2
$PE.threshold
[1] 1.315267
#Call 2
$NEN
[1] 507.8443
$margin
[1] 1
$PE.threshold
[1] 0.8403428
#Call 3
$NEN
[1] 844.0876 255.6520 134.2073 87.4793
$margin
[1] 1.25 1.50 1.75 2.00
$PE.threshold
[1] 1.092230 1.173866 1.247632 1.315267
References (e.g. scientific papers)
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None
See Also
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None