psor: Psoriatic arthritis data

In msm: Multi-State Markov and Hidden Markov Models in Continuous Time

Description

A series of observations of grades of psoriatic arthritis, as indicated by numbers of damaged joints.

Usage

psor

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)
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")
qmatrix.msm(psor.msm)
sojourn.msm(psor.msm)
hazard.msm(psor.msm)

Example output

Warning message:
In data(psor) : data set 'psor' not found
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
        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                         
        estimates        SE        L         U
State 1 10.423724 1.2597643 8.225277 13.209771
State 2  6.086186 0.7266461 4.816349  7.690816
State 3  3.931084 0.5796053 2.944488  5.248254
$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 0.8154000 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

msm documentation built on May 2, 2019, 6:51 p.m.