\name{spower}
\alias{spower}
\alias{Quantile2}
\alias{print.Quantile2}
\alias{plot.Quantile2}
\alias{logrank}
\alias{Gompertz2}
\alias{Lognorm2}
\alias{Weibull2}
\title{
Simulate Power of 2-Sample Test for Survival under Complex Conditions
}
\description{
Given functions to generate random variables for survival times and censoring
times, \code{spower} simulates the power of a user-given 2-sample test for
censored data.  By default, the logrank (Cox 2-sample) test is used,
and a \code{logrank} function for comparing 2 groups is provided.  For
composing S-Plus functions to generate random survival times under
complex conditions, the \code{Quantile2} function allows the user to
specify the intervention:control hazard ratio as a function of time,
the probability of a control subject actually receiving the
intervention (dropin) as a function of time, and the probability that
an intervention subject receives only the control agent as a function of time
(non-compliance, dropout).  \code{Quantile2} returns a function that
generates either control or intervention uncensored survival times subject to
non-constant treatment effect, dropin, and dropout.  There is a \code{plot}
method for plotting the results of \code{Quantile2}, which will aid in
understanding the effects of the two types of non-compliance and
non-constant treatment effects.  \code{Quantile2} assumes that the hazard
function for either treatment group is a mixture of the control and
intervention hazard functions, with mixing proportions defined by the
dropin and dropout probabilities.  It computes hazards and survival
distributions by numerical differentiation and integration using a
grid of (by default) 7500 equally-spaced time points.

The \code{logrank} function is intended to be used with \code{spower}
but it can be used by itself.  It returns the 1 degree of freedom
chi-square statistic, with the hazard ratio estimate as an attribute.

The \code{Weibull2} function accepts as input two vectors, one
containing two times and one containing two survival probabilities, and
it solves for the scale and shape parameters of the Weibull distribution
(\code{S(t)=exp(-alpha*t^ gamma)}) which will yield those estimates.  It
creates an S-Plus function to evaluate survival probabilities from this
Weibull distribution.  \code{Weibull2} is useful in creating functions
to pass as the first argument to \code{Quantile2}.

The \code{Lognorm2} and \code{Gompertz2} functions are similar to
\code{Weibull2} except that they produce survival functions for the
log-normal and Gompertz distributions.
}
\usage{
spower(rcontrol, rinterv, rcens, nc, ni, 
       test=logrank, nsim=500, alpha=0.05, pr=TRUE)

Quantile2(scontrol, hratio, 
          dropin=function(times)0, dropout=function(times)0,
          m=7500, tmax, qtmax=.001, mplot=200, pr=TRUE, \dots)

\method{print}{Quantile2}(x, \dots)

\method{plot}{Quantile2}(x, 
     what=c('survival','hazard','both','drop','hratio','all'),
     dropsep=FALSE, lty=1:4, col=1, xlim, ylim=NULL,
     label.curves=NULL, \dots)

logrank(S, group)

Gompertz2(times, surv)
Lognorm2(times, surv)
Weibull2(times, surv)


}
\arguments{
\item{rcontrol}{
a function of \code{n} which returns \code{n} random uncensored failure times for
the control group.  \code{spower} assumes that non-compliance (dropin) has
been taken into account by this function.
}
\item{rinterv}{
similar to \code{rcontrol} but for the intervention group
}
\item{rcens}{
a function of \code{n} which returns \code{n} random censoring times.  It is
assumed that both treatment groups have the same censoring distribution.
}
\item{nc}{
number of subjects in the control group
}
\item{ni}{
number in the intervention group
}
\item{scontrol}{
a function of a time vector which returns the survival probabilities
for the control group at those times assuming that all patients are compliant
}
\item{hratio}{
a function of time which specifies the intervention:control hazard
ratio (treatment effect)
}
\item{x}{
an object of class \code{"Quantile2"} created by \code{Quantile2}
}
\item{S}{
a \code{Surv} object or other two-column matrix for right-censored survival
times
}
\item{group}{
group indicators have length equal to the number of rows in \code{S}.
}
\item{times}{
a vector of two times
}
\item{surv}{
a vector of two survival probabilities
}
\item{test}{
any function of a \code{Surv} object and a grouping variable which computes
a chi-square for a two-sample censored data test.  The default is \code{logrank}.
}
\item{nsim}{
number of simulations to perform (default=500)
}
\item{alpha}{
type I error (default=.05)
}
\item{pr}{
set to \code{FALSE} to cause \code{spower} to suppress progress notes for
simulations.
Set to \code{FALSE} to prevent \code{Quantile2} from printing \code{tmax} when it
calculates \code{tmax}.
}
\item{dropin}{
a function of time specifying the probability that a control subject
actually becomes an intervention subject at the corresponding time
}
\item{dropout}{
a function of time specifying the probability of an intervention
subject dropping out to control conditions
}
\item{m}{
number of time points used for approximating functions (default is 7500)
}
\item{tmax}{
maximum time point to use in the grid of \code{m} times.  Default is the
time such that \code{scontrol(time)} is \code{qtmax}.
}
\item{qtmax}{
survival probability corresponding to the last time point used for
approximating survival and hazard functions.  Default is \code{.001}.  For
\code{qtmax} of the time for which a simulated time is needed which
corresponds to a survival probability of less than \code{qtmax}, the
simulated value will be \code{tmax}.
}
\item{mplot}{
number of points used for approximating functions for use in plotting
(default is 200 equally spaced points)
}
\item{...}{
optional arguments passed to the \code{scontrol} function when it's
evaluated by \code{Quantile2}
}
\item{what}{
a single character constant (may be abbreviated) specifying which
functions to plot.  The default is \code{"both"} meaning both survival and
hazard functions.  Specify \code{what="drop"} to just plot the dropin and
dropout functions, \code{what="hratio"} to plot the hazard ratio functions,
or \code{"all"} to make 4 separate plots showing all functions (6 plots if
\code{dropsep=TRUE}).
}
\item{dropsep}{
set \code{dropsep=TRUE} to make \code{plot.Quantile2} separate pure and
contaminated functions onto separate plots
}
\item{lty}{
vector of line types
}
\item{col}{
vector of colors
}
\item{xlim}{
optional x-axis limits
}
\item{ylim}{
optional y-axis limits
}
\item{label.curves}{
optional list which is passed as the \code{opts} argument to \code{labcurve}.
}}
\value{
\code{spower} returns the power estimate (fraction of simulated chi-squares
greater than the alpha-critical value).  \code{Quantile2} returns an S-Plus
function of class \code{"Quantile2"} with attributes that drive the \code{plot} method.  The major
attribute is a list containing several lists.  Each of these
sub-lists contains a \code{Time} vector along with one of the following:
survival probabilities for either treatment group and with or without
contamination caused by non-compliance, hazard rates in a similar way,
intervention:control hazard ratio function with and without
contamination, and dropin and dropout functions.  \code{logrank} returns a
single chi-square statistic, and \code{Weibull2}, \code{Lognorm2} and \code{Gompertz2}
return an S function with
three arguments, only the first of which (the vector of \code{times}) is
intended to be specified by the user.
}
\section{Side Effects}{
\code{spower} prints the interation number every 10 iterations if \code{pr=TRUE}.
}
\author{
Frank Harrell
\cr
Department of Biostatistics
\cr
Vanderbilt University School of Medicine
\cr
f.harrell@vanderbilt.edu
}
\references{
Lakatos E (1988): Sample sizes based on the log-rank statistic in complex
clinical trials.  Biometrics 44:229--241 (Correction 44:923).

Cuzick J, Edwards R, Segnan N (1997): Adjusting for non-compliance and 
contamination in randomized clinical trials. Stat in Med 16:1017--1029.

Cook, T (2003): Methods for mid-course corrections in clinical trials
with survival outcomes.  Stat in Med 22:3431--3447.

Barthel FMS, Babiker A et al (2006): Evaluation of sample size and power for multi-arm survival trials allowing for non-uniform accrual, non-proportional hazards, loss to follow-up and cross-over.  Stat in Med 25:2521--2542.
}
\seealso{
\code{\link{cpower}}, \code{\link{ciapower}}, \code{\link{bpower}}, \code{\link[Design]{cph}}, \code{\link[survival]{coxph}}, \code{\link{labcurve}}
}
\examples{
# Simulate a simple 2-arm clinical trial with exponential survival so
# we can compare power simulations of logrank-Cox test with cpower()
# Hazard ratio is constant and patients enter the study uniformly
# with follow-up ranging from 1 to 3 years
# Drop-in probability is constant at .1 and drop-out probability is
# constant at .175.  Two-year survival of control patients in absence
# of drop-in is .8 (mortality=.2).  Note that hazard rate is -log(.8)/2
# Total sample size (both groups combined) is 1000
# \% mortality reduction by intervention (if no dropin or dropout) is 25
# This corresponds to a hazard ratio of 0.7283 (computed by cpower)


cpower(2, 1000, .2, 25, accrual=2, tmin=1, 
       noncomp.c=10, noncomp.i=17.5)


ranfun <- Quantile2(function(x)exp(log(.8)/2*x),
                    hratio=function(x)0.7283156,
                    dropin=function(x).1,
                    dropout=function(x).175)


rcontrol <- function(n) ranfun(n, what='control')
rinterv  <- function(n) ranfun(n, what='int')
rcens    <- function(n) runif(n, 1, 3)


set.seed(11)   # So can reproduce results
spower(rcontrol, rinterv, rcens, nc=500, ni=500, 
       test=logrank, nsim=50)  # normally use nsim=500 or 1000


# Simulate a 2-arm 5-year follow-up study for which the control group's
# survival distribution is Weibull with 1-year survival of .95 and
# 3-year survival of .7.  All subjects are followed at least one year,
# and patients enter the study with linearly increasing probability  after that
# Assume there is no chance of dropin for the first 6 months, then the
# probability increases linearly up to .15 at 5 years
# Assume there is a linearly increasing chance of dropout up to .3 at 5 years
# Assume that the treatment has no effect for the first 9 months, then
# it has a constant effect (hazard ratio of .75)


# First find the right Weibull distribution for compliant control patients
sc <- Weibull2(c(1,3), c(.95,.7))
sc


# Inverse cumulative distribution for case where all subjects are followed
# at least a years and then between a and b years the density rises
# as (time - a) ^ d is a + (b-a) * u ^ (1/(d+1))


rcens <- function(n) 1 + (5-1) * (runif(n) ^ .5)
# To check this, type hist(rcens(10000), nclass=50)


# Put it all together


f <- Quantile2(sc, 
      hratio=function(x)ifelse(x<=.75, 1, .75),
      dropin=function(x)ifelse(x<=.5, 0, .15*(x-.5)/(5-.5)),
      dropout=function(x).3*x/5)


par(mfrow=c(2,2))
# par(mfrow=c(1,1)) to make legends fit
plot(f, 'all', label.curves=list(keys='lines'))


rcontrol <- function(n) f(n, 'control')
rinterv  <- function(n) f(n, 'intervention')


set.seed(211)
spower(rcontrol, rinterv, rcens, nc=350, ni=350, 
       test=logrank, nsim=50)  # normally nsim=500 or more
par(mfrow=c(1,1))

\dontrun{
# Do an unstratified logrank test
library(survival)
# From SAS/STAT PROC LIFETEST manual, p. 1801
days <- c(179,256,262,256,255,224,225,287,319,264,237,156,270,257,242,
          157,249,180,226,268,378,355,319,256,171,325,325,217,255,256,
          291,323,253,206,206,237,211,229,234,209)
status <- c(1,1,1,1,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1,0,
            0,rep(1,19))
treatment <- c(rep(1,10), rep(2,10), rep(1,10), rep(2,10))
sex <- Cs(F,F,M,F,M,F,F,M,M,M,F,F,M,M,M,F,M,F,F,M,
          M,M,M,M,F,M,M,F,F,F,M,M,M,F,F,M,F,F,F,F)
data.frame(days, status, treatment, sex)
table(treatment, status)
logrank(Surv(days, status), treatment)  # agrees with p. 1807
# For stratified tests the picture is puzzling.
# survdiff(Surv(days,status) ~ treatment + strata(sex))$chisq
# is 7.246562, which does not agree with SAS (7.1609)
# But summary(coxph(Surv(days,status) ~ treatment + strata(sex)))
# yields 7.16 whereas summary(coxph(Surv(days,status) ~ treatment))
# yields 5.21 as the score test, not agreeing with SAS or logrank() (5.6485)
}
}
\keyword{htest}
\keyword{survival}
\concept{power}
\concept{study design}