The survival time we will work with is the time that heroin addicts stay in a clinic
for methadone maintenance treatment. The data were first analyzed by Caplehorn (1991)
and appeared in a handbook of small datasets by Hand et al (1994).
They have also been analyzed by Rabe-Hesketh and Everitt (2006) in various editions
of their Handbook of Statistical Analyses using Stata. The data are available
at https://grodri.github.io/datasets/heroin.dta.
The data come from two clinics coded 1 and 2; status is 1 if the event occurred (the person left the clinic) and 0 otherwise, and time is in days. The other two variables are an indicator of whether the person had a prison record, and the dose of methadone in mg.
(a) Verify that we have 150 failures in a total of 95,812 days of exposure. It will be convenient to recon time in months, treating a month as 30 days. Assume the hazard is constant over time. What's the event rate per month? What's the probability that someone would still be in treatment after one, two and three years?
(b) Split the dataset so that we have separate observations for 0-3, 3-6, 6-12, 12-18, 18-24 and 24-36 months. (In other words split the first year into two quarters and a semester, and the second year into two semesters.) There are no exits after 3 years. Fit a piece-wise exponential model and describe the shape of the hazard. Test the hypothesis that the hazard is constant. Estimate the probability that someone would be in treatment after one, two and three years.
(a) Introduce a dummy variable to identify clinic 1 and add it to the model. Interpret the exponentiated coefficient and test its significance. (Note that clinic is coded 1 and 2, we want to treat clinic 2 as the reference. You may want to save this model for part c.)
(b) Let us add an interaction between clinic and duration. To save d.f. we will group time in years for purposes of the interaction. For full credit present your parameter estimates in terms of the effect of clinic 1 compared to 2 in the first, second and third year of treatment. Comment on the point estimates.
(c) Test the hypothesis that the clinic effect does indeed vary by year using a likelihood ratio test and a Wald test.
(a) Starting from the model of part 2.b where the effect of clinic varies by year, add a dummy variable for prison history and interpret the resulting estimate.
(b) Add a linear effect of dose and interpret the estimate. The effect of 1 mg of methadone may not be of interest because doses vary by several mg. In fact the standard deviation is 14.45. What's the effect on the hazard of increasing the dose by one standard deviation?
(c) The original paper by Caplehorn and Bell treated dose as a categorical variable with three levels: < 60, 60-79, and 80+. Compare this specification with the linear term in part b in terms of parsimony and goodness of fit.
(a) Use the piece-wise exponential model of part 3.b with effects of clinic, dose, and prison history, to predict the probability of remaining in treatment after one, two and three years for someone with no prison record receiving the average dose of 60.4 mg of methadone in clinic 1.
(b) Repeat the calculations for someone with a prison record who receives 60.4 mg in clinic 1.
(c) Finally, repeat the calculations for someone without a prison record who receives the average dose of 60.4 mg, but in clinic 2.
Please present the results for parts a-c in a single table.
Caplehorn, J. (1991) Methadone dosage and retention of patients in maintenance treatment. Medical Journal of Australia, 154:195-199.
Everitt, B.S. and Rabe-Hesketh, S. (2006). Handbook of Statistical Analyses Using Stata. London: Chapman and Hall.
Hand, D. J., Daly, F., Lunn, A.D., McConway, K.J. and Ostrowski, E. (1994). A Handbook of Small Data Sets. London: Chapman and Hall.