psor                   package:msm                   R Documentation

_P_s_o_r_i_a_t_i_c _a_r_t_h_r_i_t_i_s _d_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

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

_U_s_a_g_e:

     data(psor)

_F_o_r_m_a_t:

     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

_R_e_f_e_r_e_n_c_e_s:

     Gladman, D. D. and Farewell, V.T. (1999) Progression in psoriatic
     arthritis: role of time-varying clinical indicators.  J.
     Rheumatol. 26(11):2409-13

_E_x_a_m_p_l_e_s:

     ## 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)

