Description Usage Arguments Value References Examples
This function calculates the conditional power at an interim analysis of a
clinical trial
1 2 3 4 5 6 7 8 9 10 11 12 13 |
r |
Proportion of subjects assigned to active group |
n1 |
Number of patients / events at first interim analysis (NULL if parameter f provided) |
n2 |
Number of patients / events at final analysis (NULL if parameter f provided) |
alpha_1s |
one-sided alpha |
eff_est |
effect observed/estimated at interim |
eff_planned |
planned effect (usually alternative hypothesis, but can be modified to obtain |
eff_null |
effect corresponding with null hypothesis (e.g. 1 for hazard rates, 0 for difference) |
SE |
standard error: has to be provided if type not equal to "HR". If for instance SE =1 |
type |
if type="HR", then SE is calculated, if type="general", then SE's have to be provided |
pow |
Power, needed to calculate sample size in second part provided to obtain given power |
f |
n1/n2 at interim analysis. Only to be provided if n1 and n2 not provided |
a list of three vectors
CP_obs : conditional power, given 1) observed effect at interim, 2) total sample size,
3) Assumed true effect= "eff_est" parameter
#'
CP_planned : conditional power, given 1) observed effect at interim, 2) total sample size,
3) Assumed true effect= "eff_planned" parameter
#'
n2_inc_new : additional patients needed to obtain given power
Lan and Wittes. The B-Value: A Tool for Monitoring Data. Biometrics 1988;44:579-585
Mehta CR, Pocock SJ. Adaptive increase in sample size when interim results are promising: A practical
guide with examples. Statist. Med. 2011;30:3267–3284
1 2 3 4 5 6 | CP(r=0.5,n1=72 ,n2=180,alpha_1s=0.025,eff_est=0.7,eff_planned=0.7 ,eff_null=1,SE=NULL,
type="HR",pow=0.8)
CP(r=0.5 ,alpha_1s=0.025,eff_est=1.5,eff_planned=1.96,eff_null=0,SE=1,
type="general",pow=0.8,f=0.5)
CP(r=0.5,n1=316,n2=633,alpha_1s=0.025,eff_est=1.5,eff_planned=1.96,eff_null=0,SE=1,
type="general",pow=0.8)
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