SurvRegCensCov (version 1.1)

NormalMeanDiffCens: Maximum Likelihood Estimator for the mean difference between two censored normally distributed samples

Description

Computes estimates of the parameters of two censored Normal samples, as well as the mean difference between the two samples.

Usage

NormalMeanDiffCens(censdata1, censdata2, conf.level = 0.95, 
     null.values = c(0, 0, 1, 1))

Arguments

censdata1

Observations of first sample, format as for censdata in ParamSampleCens.

censdata2

Observations of second sample, format as for censdata in ParamSampleCens.

conf.level

Confidence level for confidence intervals.

null.values

Fixed values for hypothesis tests. Four-dimensional vector specifying the hypothesis for $\mu_1$, $\mu_2$, $\sigma_1$, $\sigma_2$.

Value

• A table with estimators and inference for the means and standard deviations of both samples, as well as the difference $\Delta$ between the mean of the first and second sample. Hypothesis tests are for the values in null.values and for the null hypothesis of no mean difference.

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

## example with interval-censored Normal samples
n <- 500
prop.cens <- 0.35
mu <- c(0, 2)
sigma <- c(1, 1)

set.seed(2013)

## Sample 1:
LOD1 <- qnorm(0.35, mean = mu[1], sd = sigma[1])
x1 <- rnorm(n, mean = mu[1], sd = sigma[1])
min.value1 <- min(x1)
x1[x1 <= LOD1] <- LOD1
index.noncens1 <- which(x1 > LOD1)
left1 <- rep(NA, n)
left1[index.noncens1] <- x1[index.noncens1]

index.cens1 <- which(x1 <= LOD1)
index.interval1 <- index.cens1[index.cens1 >= 250]
left1[index.interval1] <- min.value1
s1 <- cbind(left1, x1)

## Sample 2:
LOD2 <- qnorm(0.35, mean = mu[2], sd = sigma[2])
x2 <- rnorm(n, mean = mu[2], sd = sigma[2])
min.value2 <- min(x2)
x2[x2 <= LOD2] <- LOD2
index.noncens2 <- which(x2 > LOD2)
left2 <- rep(NA, n)
left2[index.noncens2] <- x2[index.noncens2]

index.cens2 <- which(x2 <= LOD2)
index.interval2 <- index.cens2[index.cens2 >= 250]
left2[index.interval2] <- min.value2
s2 <- cbind(left2, x2)

## inference on distribution parameters and mean difference:
NormalMeanDiffCens(censdata1 = s1, censdata2 = s2)