SurvRegCensCov (version 1.1)

ParamSampleCens: Maximum Likelihood Estimator of parameters from a censored sample

Description

Computes maximum likelihood estimators of the canonical parameters for several distributions, based on a censored sample.

Usage

ParamSampleCens(censdata, dist = c("normal", "logistic", "gamma", "weibull")[1], 
     null.values = c(0, 1), conf.level = 0.95, initial = NULL)

Arguments

censdata

A dataframe of two columns, describing each observed value as an interval. The left column contains either NA for left censored observations, the left bound of the interval for interval censored observations, or the observed value for non-cen

dist

Assumed distribution of the sample.

null.values

Fixed values for hypothesis tests.

conf.level

Confidence level of confidence intervals.

initial

Initial values for the maximization.

Value

• coeff
  Estimators, standard errors, confidence intervals, and 2-sided $p$-values for the null hypothesis as given in null.values. All this inference is based on maximum likelihood theory, i.e. standard errors are computed using the inverse expected Hessian at the maximum likelihood estimator.
• percent.cens
  Percentage of censored observations.
• loglik
  Log likelihood function value at the estimator.
• info.converg
  Convergence information provided by the function optim.
• info.converg.message
  Message provided by the function optim.

References

Hubeaux, S. (2013). Estimation from left- and/or interval-censored samples. Technical report, Biostatistics Oncology, F. Hoffmann-La Roche Ltd. Lynn, H. S. (2001). Maximum likelihood inference for left-censored HIV RNA data. Stat. Med., 20, 33--45.

Examples

n <- 500
prop.cens <- 0.35

## example with a left-censored Normally distributed sample
set.seed(2013)
mu <- 3.5
sigma <- 1
x <- rnorm(n, mean = mu, sd = sigma)
LOD <- qnorm(prop.cens, mean = mu, sd = sigma)
x[x <= LOD] <- LOD
index.noncens <- which(x > LOD)
left <- rep(NA, n)
left[index.noncens] <- x[index.noncens]
s1 <- cbind(left, x)

ParamSampleCens(censdata = s1)


## example with an interval-censored Normal sample
set.seed(2013)
x <- rnorm(n, mean = mu, sd = sigma)
min.value <- min(x)
LOD <- qnorm(0.35, mean = mu, sd = sigma)
x[x <= LOD] <- LOD
index.noncens <- which(x > LOD)
left <- rep(NA, n)
left[index.noncens] <- x[index.noncens]

index.cens <- which(x <= LOD)
index.interval <- index.cens[index.cens >= 250]
left[index.interval] <- min.value
s2 <- cbind(left, x)

ParamSampleCens(censdata = s2)

## compare to fitdistrplus
library(fitdistrplus)
s2 <- as.data.frame(s2)
colnames(s2) <- c("left", "right")
summary(fitdistcens(censdata = s2, distr = "norm"))