DPpackage (version 1.1-7.4)

calgb: Cancer and Leukemia Group B (CALGB) Data

Description

Data from two studies carried out by the Cancer and Leukemia Group B (CLAGB): CALGB 8881 and CALGB 9160. In both studies, the main response was white blood cell count (WBC) for each patient over time. Mueller and Rosner (1998) used a non-linear patient specific regression model. The data consider the subject-specific regression parameters (Z1, Z2, Z3, T1, T2, B0, B1) and information on covariates.

CALGB has kindly agreed to make these data available for interested readers, subject to the following conditions: i) Any paper using these data should acknowledge CALGB for the use of the data, and ii) the paper should reference the original papers describing the studies.

Usage

data(calgb)

Format

A data frame with 98 observations on the following 12 variables.

Z1

a numeric vector giving the estimated Z1 coefficients of the logistic regression curve.

Z2

a numeric vector giving the estimated Z2 coefficients of the logistic regression curve.

Z3

a numeric vector giving the estimated Z3 coefficients of the logistic regression curve.

T1

a numeric vector giving the estimated time point where the horizontal line of the curve is defined, i.e., the curve consists of a horizontal line up to t=T1ji.

T2

a numeric vector giving the estimated time point where the logistic component of the curve is defined, i.e., the curve consist of a logistic regression curve starting at t=T2ji.

B0

a numeric vector giving the estimated B0 coefficients of the logistic regression curve.

B1

a numeric vector giving the estimated B1 coefficients of the logistic regression curve.

CTX

a numeric vector giving the dose level of cyclophosphamide.

GM

a numeric vector giving the dose level GM-CSF.

AMOF

a numeric vector giving the dose level of amifostine.

pat

a numeric vector giving the patient indicators.

study

a numeric vector giving the study indicators.

References

Mueller, P. and Rosner, G. (1998). Semiparametric PK/PD Models. In: Practical Nonparametric and Semiparametric Bayesian Statistics, Eds: D. Dey, P. Muller, D. Sinha, New York: Springer-Verlag, pp. 323-337.

Mueller, P., Quintana, F. and Rosner, G. (2004). A Method for Combining Inference over Related Nonparametric Bayesian Models. Journal of the Royal Statistical Society, Series B, 66: 735-749.

Examples

# NOT RUN {
data(calgb)
## maybe str(calgb) ; plot(calgb) ...
# }