surgerydat | R Documentation |
Data about patients and their surgery procedure from 45 simulated hospitals
with patient arrivals in the first 400 days after the start of the study.
Patient survival times were determined using a risk-adjusted Cox proportional hazards model
with coefficients age = 0.003, BMI = 0.02 and sexmale = 0.2 and exponential baseline hazard rate
h_0(t, \lambda = 0.01) e^\mu
.
The increase in hazard rate is sampled from a normal distribution for all hospitals:
\theta \sim N(log(1), sd = 0.4)
This means that the average failure rate of hospitals in the data set
should be baseline (\theta = 0
), with some hospitals
experiencing higher and lower failure rates. True failure rate can be found
in the column exptheta
.
The arrival rate \psi
of patients at a hospital differs. The arrival rates are:
Hospitals 1-5 & 16-20: 0.5 patients per day (small hospitals)
Hospitals 6-10 & 21-25: 1 patient per day (medium sized hospitals)
Hospitals 11-15 & 26-30: 1.5 patients per day (large hospitals)
These are then respectively small, medium and large hospitals.
surgerydat
A data.frame
with 12010 rows and 9 variables:
Time of entry of patient into study (numeric)
Time from entry until failure of patient (numeric)
Censoring indicator (0 - right censored, 1 - observed) (integer)
Hospital number at which patient received treatment (integer)
True excess hazard used for generating patient survival (numeric)
Poisson arrival rate at hospital which the patient was at (numeric)
Age of the patient (numeric)
Sex of the patient (factor)
Body mass index of the patient (numeric)
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