coxrt (version 1.0.3)

coxph.RT.a0: Fits Cox Regression with Adjustment for the Lack of Positivity

Description

Estimates covariate effects in a Cox proportional hazard regression from right truncated survival data for a given value of a0=P(lifetime>max(right) | Z=0). This probability reflects the chance of falling to the right of the upper bound of the support of the right truncation variable in the reference stratum where all the covariates are zero. Right truncation might result in a completely unobserved right tail of the distribution of the target lifetime. That means that it can happen there will be no "representatives" in a sample from the right tail. Ignoring this and using coxph.RT in general will result in biased estimation of regression coefficients, whereas coxph.RT.a0 allows to account for this violation.

Usage

coxph.RT.a0(formula, right, data, a0 = 0, bs = FALSE, nbs.rep = 200,
  conf.int = 0.95)

Arguments

formula

a formula object, with the response on the left of a ~ operator, and covariates on the right. The response is a target lifetime variable.

right

a right truncation variable.

data

a data frame that includes the variables used in formula and in right.

a0

probability of falling into the unobservable region in the stratum of Z=0, i.e. P(lifetime>max(right) | Z=0). By default a0=0, which is equivalent to assuming positivity.

bs

logical value: if TRUE, the bootstrap esimator of standard error, confidence interval, and confidence upper and lower limits for one-sided confidence intervals based on the bootstrap distribution are calculated. The default value is FALSE.

nbs.rep

number of bootstrap replications. The default number is 200.

conf.int

The confidence level for confidence intervals and hypotheses tests. The default level is 0.95.

Value

a list with components:

convergence	convergence code as returned by BBsolve.
	convergence > 0 means that the algorithm diverged and a solution was not found.
	BBsolve is used with a default parameters setting.
coef	a vector of estimated regression coefficients.
var	covariance matrix of regression coefficients, if the input argument bs was TRUE;
	NULL, otherwise.
n	the number of observations used to fit the model.
a0	plugged-in value of a0.
bs	if the input argument bs was TRUE, then an output list also includes an element bs
	with statistics from the bootstrap distribution of estimated coefficients:

• num.bs.rep number of successful bootsrap replications used to obtain the sample distribution

• var estimated variance of regression coefficients

• summary a data frame with a summary of bootstrap distribution that includes: coef, a vector of estimated regression coefficients; exp.coef, an exponent of regression coefficients (=hazard ratio); SE, a bootstrap estimated standard error; CI.L, a quantile estimated lower confidence limit for two-sided hypothesis \(\beta\) = 0; CI.U, a quantile estimated upper confidence limit for two-sided hypothesis \(\beta\) = 0; CI.L.H1.b.gr0, a quantile estimated the limit for one-sided hypothesis \(\beta\)\(\le 0\); CI.U.H1.b.le0, a quantile estimated the limit for one-sided hypothesis \(\beta\)\(\ge 0\).

See Also

coxph.RT, BBsolve

Examples

# NOT RUN {
# loading AIDS data set
library(gss)
data(aids)
all <- data.frame(age=aids$age, ageg=as.numeric(aids$age<=59), T=aids$incu,
R=aids$infe, hiv.mon =102-aids$infe)
all$T[all$T==0] <- 0.5 # as in Kalbfeisch and Lawless (1989)
s <- all[all$hiv.mon>60,] # select those who were infected in 1983 or later

# analysis using adjusted estimating equations for a0=0.2
sol.02 <- try(coxph.RT.a0(T~ageg, right=R, data=s, a0=0.2, bs=FALSE))
sol.02
# for a0=0
sol <- try(coxph.RT(T~ageg, right=R, data=s, bs=FALSE) )
sol$summary # print the summary of fit based on the asymptotic SE estimate


# sensitivity analysis for different values of a0
a_ <- seq(0.05, 0.55, by=0.05)
est <- NULL

for(q in 1:length(a_))
{
  sol.a <- try(coxph.RT.a0(T~ageg, right=R, data=s, a0=a_[q], bs=FALSE))
  if (sol.a$convergence!=0)
  {
    cat("a0=", a_[q], ". Error occurred in BBsolve.\n")
  } else
  {
    cat("a=", a_[q]," ", " IPW.adj.est=", sol.a$coef, "\n")
    est <- c(est, sol.a$coef)
  }
}
require(ggplot2)
res.d <- data.frame(a0=c(0, a_), beta=c(sol$coef, est))

p <- ggplot(res.d, aes(x=a0, y=beta)) +
  geom_line() + geom_point() +
  geom_hline(yintercept=0)
p + xlab(expression( paste(a[0], "=P(T>", r['*']," | z=0)" , sep="")) )+
  ylab(expression( paste(hat(beta), "(", a[0], ")" , sep="")) ) +
  scale_x_continuous(breaks=res.d$a0, labels=res.d$a0) +
  theme(axis.text.x = element_text(face="bold", angle=45),
        axis.text.y = element_text(face="bold"))

# }