Hi everyone,
I’m currently modelling loss in life expectancy of cervical cancer patients, with a flexible parametric relative survival model. What I want to do is a traditional cohort approach for the years 1989–2009 and a period approach for the years 2010*–2014. However, the period approach is not going as I expected.
Cohort approach (blue lines in Figure 1):
Age at diagnosis and year of diagnosis are both modelled using restricted cubic splines with 3 degrees of freedom. Interactions between age and year are included. All main and interaction effects were allowed to be time-varying. The model had 4 degrees of freedom for the baseline and 2 for time-varying effects.
This approach gives me no trouble and provides me, for example for patients aged 30, with the following estimates:
Period approach (pink lines in Figure 1):
For the period approach, age is modelled continuously using restricted cubic splines and year of diagnosis is NOT included in the model (Andersson et al. 2015; supplementary material; doi: 10.1186/s12885-015-1427-2). Age is allowed to be time-varying. The model had 4 degrees of freedom for the baseline and 2 for time-varying effects.
This approach gives estimates which seem to be linear over time for the period 2010–2014 (pink line). This is contradictory to what I expected. I expected loss in life expectancy to vary over time, like in the periode 1989-2009.
Can anyone tell me what I should do differently?
I’m currently modelling loss in life expectancy of cervical cancer patients, with a flexible parametric relative survival model. What I want to do is a traditional cohort approach for the years 1989–2009 and a period approach for the years 2010*–2014. However, the period approach is not going as I expected.
Cohort approach (blue lines in Figure 1):
Age at diagnosis and year of diagnosis are both modelled using restricted cubic splines with 3 degrees of freedom. Interactions between age and year are included. All main and interaction effects were allowed to be time-varying. The model had 4 degrees of freedom for the baseline and 2 for time-varying effects.
Code:
* stset the data stset vitfup_years, failure(vitstat==1) id(patid) exit(time 10) * Model stpm2 agespl* yearspl* a1y? a2y? a3y?, scale(hazard) df(4) bhazard(rate) tvc(agespl* yearspl* a1y? a2y? a3y?) dftvc(2) * Predict loss in life expectancy predict ll, lifelost mergeby(_year sex _age) diagage(age) diagyear(yydx) nodes(40) tinf(80) using(popmortNL) survprob(prob) stub(surv) maxyear(2020) ci
| yydx | ll | ll_lci | ll_uci |
| 1989 | 9,390 | 5,994 | 12,785 |
| 1990 | 8,712 | 6,131 | 11,292 |
| 1991 | 8,096 | 6,136 | 10,056 |
| . | . | . | . |
| . | . | . | . |
| . | . | . | . |
| 2010 | 7,508 | 5,739 | 9,276 |
| 2011 | 7,244 | 5,184 | 9,305 |
| 2012 | 6,956 | 4,465 | 9,447 |
| 2013 | 6,650 | 3,664 | 9,635 |
| 2014 | 6,345 | 2,855 | 9,836 |
Period approach (pink lines in Figure 1):
For the period approach, age is modelled continuously using restricted cubic splines and year of diagnosis is NOT included in the model (Andersson et al. 2015; supplementary material; doi: 10.1186/s12885-015-1427-2). Age is allowed to be time-varying. The model had 4 degrees of freedom for the baseline and 2 for time-varying effects.
Code:
* stset the data stset vitdat, origin(incdat) enter(time mdy(1,1,2010)) exit(time mdy(12,31,2014)) fail(vitstat==1) id(patid) scale(365.24) * Model stpm2 agespl*, scale(hazard) df(4) bhazard(rate) tvc(agespl*) dftvc(2)
| yydx | ll | ll_lci | ll_uci |
| 2010 | 7,150 | 5,597 | 8,704 |
| 2011 | 7,149 | 5,596 | 8,702 |
| 2012 | 7,153 | 5,599 | 8,707 |
| 2013 | 7,152 | 5,598 | 8,706 |
| 2014 | 7,153 | 5,598 | 8,707 |
This approach gives estimates which seem to be linear over time for the period 2010–2014 (pink line). This is contradictory to what I expected. I expected loss in life expectancy to vary over time, like in the periode 1989-2009.
Can anyone tell me what I should do differently?
Attached Files
