fysio | R Documentation |
These data were collected in 1987 as part of a large national survey of general practice (Van der Velden 1999).
fysio
A data frame with 16700 observations on the following 14 variables:
GP identifier.
Patient identifier.
Patient age in years.
Patient age group, with ages grouped into 7 categories (ordered factor with levels
page<35
, 35<=page<45
, 45<=page<55
, 55<=page<65
, 65<=page<75
,
75<=page<85
, 85<=page
).
Patient gender (factor with levels female
, male
).
Patient insurance indicator (factor with levels
privateins
(privately insured), publicins
(publically insured)).
Patient education level (ordered factor with levels none
(no formal education), primary
(primary education), secondary
(secondary and lower/middle vocational education), higher
(higher vocational
and university education)).
Primary diagnosis resulting from care episodes
(factor with levels
1
(symptoms/complaints neck),
2
(symptoms/complaints back),
3
(myalgia/fibrositis),
4
(symptoms of multiple muscles),
5
(disabilities related to the locomotive system),
6
(impediments of the cervical spine),
7
(arthrosis cervical spine),
8
(lumbago),
9
(ischialgia),
10
(hernia nuclei pulposi),
11
(impediments of the shoulder),
12
(epicondylitis lateralis),
13
(tendinitis/synovitis)).
GP experience (number of years working as a GP divided by ten).
GP workload (number of contacts in the 3-month registration period divided by 1000).
Practice type (factor with levels solo
,
duo
, group
, healthcentre
).
Practice location (factor with levels rural
,
suburban
, urban
, bigcity
).
Indicator of whether the GP has physiotherapists in their
social network (factor with levels no
, yes
).
Indicator of whether the patient was referred to a
physiotherapist (factor with levels no
, yes
).
The fysio
dataset is one of the example datasets analysed in
Leyland and Groenewegen (2020), and provided with the
multilevel-modelling software package MLwiN (Charlton et al., 2024).
Charlton, C., Rasbash, J., Browne, W.J., Healy, M. and Cameron, B. (2024) MLwiN Version 3.08 Centre for Multilevel Modelling, University of Bristol.
Leyland, A.H., Groenewegen, P.P. (2020). Multilevel Logistic Regression Using MLwiN: Referrals to Physiotherapy. In: Multilevel Modelling for Public Health and Health Services Research. Springer, Cham. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-3-030-34801-4_12")}
Van der Velden, K. (1999). General practice at work: its contribution to epidemiology and health policy. NIVEL, PhD thesis Erasmus University, Utrecht
## Not run:
data(fysio, package = "R2MLwiN")
# Example taken from Leyland and Groenewegen (2020)
# Change contrasts if wish to avoid warning indicating that, by default,
# specified contrasts for ordered predictors will be ignored by runMLwiN
# (they will be fitted as "contr.treatment" regardless of this setting). To
# enable specified contrasts, set allowcontrast to TRUE (this will be the
# default in future package releases).
my_contrasts <- options("contrasts")$contrasts
options(contrasts = c(unordered = "contr.treatment",
ordered = "contr.treatment"))
# As an alternative to changing contrasts, can instead use C() to specify
# contrasts for ordered predictors in formula object, e.g.:
# F1 <- logit(referral) ~ 1 + C(pagegrp, "contr.treatment") + patsex + diag +
# C(patedu, "contr.treatment") + patinsur + gpexper + gpworkload +
# practype + location + gpphysifr +
# (1 | gpid)
#
# (mod_MQL1 <- runMLwiN(Formula = F1,
# D = "Binomial",
# data = fysio,
# allowcontrast = TRUE))
F1 <- logit(referral) ~ 1 + pagegrp + patsex + diag +
patedu + patinsur + gpexper + gpworkload +
practype + location + gpphysifr +
(1 | gpid)
(mod_MQL1 <- runMLwiN(Formula = F1,
D = "Binomial",
data = fysio))
(mod_PQL2 <- runMLwiN(Formula = F1,
estoptions = list(nonlinear = c(N = 1, M = 2),
startval = list(FP.b = mod_MQL1@FP,
FP.v = mod_MQL1@FP.cov,
RP.b = mod_MQL1@RP,
RP.v = mod_MQL1@RP.cov)),
D = "Binomial",
data = fysio))
# Change contrasts back to pre-existing:
options(contrasts = my_contrasts)
## End(Not run)
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