A series of observations of grades of psoriatic arthritis, as indicated by numbers of damaged joints.
Format
A data frame containing 806 observations, representing visits to a psoriatic arthritis (PsA) clinic from 305 patients. The rows are grouped by patient number and ordered by examination time. Each row represents an examination and contains additional covariates.
ptnum | (numeric) | Patient identification number |
months | (numeric) | Examination time in months |
state | (numeric) | Clinical state of PsA. Patients in states 1, 2, 3 and 4 |
| have 0, 1 to 4, 5 to 9 and 10 or more damaged joints, | ||
| respectively. | ||
hieffusn | (numeric) | Presence of five or more effusions |
ollwsdrt | (character) | Erythrocyte sedimentation rate of less than 15 mm/h |
References
Gladman, D. D. and Farewell, V.T. (1999) Progression in psoriatic arthritis: role of time-varying clinical indicators. J. Rheumatol. 26(11):2409-13
Examples
## Four-state progression-only model with high effusion and low
## sedimentation rate as covariates on the progression rates. High
## effusion is assumed to have the same effect on the 1-2, 2-3, and 3-4
## progression rates, while low sedimentation rate has the same effect
## on the 1-2 and 2-3 intensities, but a different effect on the 3-4.
data(psor)
#> Warning: data set 'psor' not found
psor.q <- rbind(c(0,0.1,0,0),c(0,0,0.1,0),c(0,0,0,0.1),c(0,0,0,0))
psor.msm <- msm(state ~ months, subject=ptnum, data=psor,
qmatrix = psor.q, covariates = ~ollwsdrt+hieffusn,
constraint = list(hieffusn=c(1,1,1),ollwsdrt=c(1,1,2)),
fixedpars=FALSE, control = list(REPORT=1,trace=2), method="BFGS")
#> initial value 1184.216999
#> iter 2 value 1127.501356
#> iter 3 value 1122.654955
#> iter 4 value 1121.606113
#> iter 5 value 1120.763406
#> iter 6 value 1119.769934
#> iter 7 value 1116.747874
#> iter 8 value 1116.596341
#> iter 9 value 1114.972649
#> iter 10 value 1114.899884
#> iter 11 value 1114.899464
#> iter 11 value 1114.899461
#> iter 11 value 1114.899461
#> final value 1114.899461
#> converged
#> Used 37 function and 11 gradient evaluations
qmatrix.msm(psor.msm)
#> State 1 State 2
#> State 1 -0.09594 (-0.1216,-0.0757) 0.09594 ( 0.0757, 0.1216)
#> State 2 0 -0.16431 (-0.2076,-0.1300)
#> State 3 0 0
#> State 4 0 0
#> State 3 State 4
#> State 1 0 0
#> State 2 0.16431 ( 0.1300, 0.2076) 0
#> State 3 -0.25438 (-0.3396,-0.1905) 0.25438 ( 0.1905, 0.3396)
#> State 4 0 0
sojourn.msm(psor.msm)
#> estimates SE L U
#> State 1 10.423724 1.2597644 8.225277 13.209771
#> State 2 6.086186 0.7266461 4.816349 7.690817
#> State 3 3.931084 0.5796053 2.944488 5.248254
hazard.msm(psor.msm)
#> $ollwsdrt
#> HR L U
#> State 1 - State 2 0.5651903 0.3853452 0.828971
#> State 2 - State 3 0.5651903 0.3853452 0.828971
#> State 3 - State 4 1.6407662 0.8153999 3.301587
#>
#> $hieffusn
#> HR L U
#> State 1 - State 2 1.645956 1.148294 2.359299
#> State 2 - State 3 1.645956 1.148294 2.359299
#> State 3 - State 4 1.645956 1.148294 2.359299
#>