nSurvival {gsDesign} | R Documentation |
nSurvival()
is used to calculate the sample size for a clinical trial with a time-to-event endpoint. The Lachin and Foulkes (1986) method is used.
nSurvival(lambda.0, lambda.1, Ts, Tr, eta = 0, rand.ratio = 1, alpha = 0.05, beta = 0.10, sided = 2, approx = FALSE, type = c("rr", "rd"), entry = c("unif", "expo"), gamma = NA)
lambda.0, lambda.1 |
event hazard rate for placebo and treatment group respectively. |
eta |
equal dropout hazard rate for both groups. |
rand.ratio |
randomization ratio between placebo and treatment group. Default is balanced design, i.e., randomization ratio is 1. |
Ts |
maximum study duration. |
Tr |
accrual (recruitment) duration. |
alpha |
type I error rate. Default is 0.05 since 2-sided testing is default. |
beta |
type II error rate. Default is 0.10 (90% power). |
sided |
one or two-sided test? Default is two-sided test. |
approx |
logical. If TRUE , the approximation sample size
formula for risk difference is used. |
type |
type of sample size calculation: risk ratio (“rr”) or risk difference (“rd”). |
entry |
patient entry type: uniform entry ("unif" ) or exponential
entry ("expo" ). |
gamma |
rate parameter for exponential entry. NA if entry type is
"unif" (uniform). A non-zero value is supplied if entry type is
"expo" (exponential). |
nSurvival
produces the number of subjects and events for a set of
pre-specified trial parameters, such as accrual duration and follow-up
period. The calculation is based on Lachin and Foulkes method and can
be used for risk ratio or risk difference. The function also consider
non-uniform entry as well as uniform entry.
If the logical approx is TRUE
, the variance under alternative
hypothesis is used to replace the variance under null hypothesis.
For non-uniform entry. a non-zero value of gamma for exponential entry must be supplied. For positive gamma, the entry distribution is convex, whereas for negative gamma, the entry distribution is concave.
nSurvival
produces a list with the following component returned:
Method |
As input. |
Entry |
As input. |
Sample.size |
Number of subjects. |
Num.events |
Number of events. |
Hazard.p, Hazard.t |
hazard rate for placebo and treatment group. As input. |
Dropout |
as input. |
Frac.p, Frac.t |
randomization proportion for placebo and treatment. As input. |
Gamma |
as input. |
Alpha |
as input. |
Beta |
as input. |
Sided |
as input. |
Study.dura |
Study duration. |
Accrual |
Accrual period. |
Shanhong Guan shanhong_guan@merck.com
Lachin JM and Foulkes MA (1986), Evaluation of Sample Size and Power for Analyses of Survival with Allowance for Nonuniform Patient Entry, Losses to Follow-Up, Noncompliance, and Stratification. Biometrics, 42, 507-519.
# consider a trial with # 2 year maximum follow-up # 6 month uniform enrollment # Treatment/placebo hazards = 0.1/0.2 per 1 person-year # drop out hazard 0.1 per 1 person-year # alpha = 0.05 (two-sided) # power = 0.9 (default beta=.1) ss <- nSurvival(lambda.0=.2 , lambda.1=.1, eta = .1, Ts = 2, Tr = .5, sided=1, alpha=.025) # symmetric, 2-sided design with O'Brien-Fleming-like boundaries # sample size is computed based on a fixed design requiring n=100 x<-gsDesign(k = 5, test.type = 2) x # boundary plot plot(x) # power plot plot(x, plottype = 2) # total sample size ceiling(x$n.I[x$k] * ss$Sample.size) # number of events at analyses ceiling(ss$Num.events * x$n.I)