Description

A randomized control trial from Mayo Clinic in which both survival and longitudinal data were collected from 1974 to 1984 to study the progression of primary biliary cirrhosis.

Format

A data frame with 1945 observations on the following 16 variables.

ID

patient ID, there are 312 patients in total.

Time

survival time (in years), i.e. time to death, transplantion or censoring.

death

death indicator: 0 denotes transplantion or censoring; 1 denotes death.

obstime

time points at which the longitudinal measurements, e.g. serum bilirubin, albumin and alkaline phosphatase, are recorded.

serBilir

serum bilirubin measured at obstime (mg/dl).

albumin

albumin measured at obstime (gm/dl).

alkaline

alkaline phosphatase measured at obstime (U/litter).

platelets

platelets per cubic measured at obstime (ml/1000).

drug

drug indicator with two levels: placebo and D-penicil.

age

age of patient at study entry.

gender

gender indicator with two levels: male and female.

ascites

ascites indicator with two levels: No and Yes.

hepatom

hepatomegaly indicator with two levels: No and Yes.

start

same with obstime, starting time of the interval which contains the time of the logitudinal measurements.

stop

ending time of the interval which contains the time of the longitudinal measurements.

event

event indicator suggesting whether the event-of-interest, i.e. death, happens in the interval given by start and stop.

Source

http://lib.stat.cmu.edu/datasets/pbcseq

Fleming, T. and Harrington, D. (1991) Counting Processes and Survival Analysis. Wiley, New York.

References

Murtaugh, P. A., Dickson, E. R., Van Dam, G. M., Malincho, M., Grambsch, P. M., Langworthy, A. L., and Gips, C. H. (1994) Primary biliary cirrhosis: Prediction of short-term survival based on repeated patient visits. Hepatology 20, 126–134.

Therneau, T. and Grambsch, P. (2000) Modeling Survival Data: Extending the Cox Model. New York: Springer.

Ding, J. and Wang, J. L. (2008) Modeling longitudinal data with nonparametric multiplicative random effects jointly with survival data. Biometrics 64, 546–556.

Examples