Description Usage Format Details Source Examples
A hypothetical dataset with 500 subjects suspected of having deep vein thrombosis (DVT).
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A data frame with 500 observations of 16 variables.
sex
gender (0=female, 1=male)
malign
active malignancy (0=no active malignancy, 1=active malignancy)
par
paresis (0=no paresis, 1=paresis)
surg
recent surgery or bedridden
tend
tenderness venous system
oachst
oral contraceptives or hst
leg
entire leg swollen
notraum
absence of leg trauma
calfdif3
calf difference >= 3 cm
pit
pitting edema
vein
vein distension
altdiagn
alternative diagnosis present
histdvt
history of previous DVT
ddimdich
dichotimized D-dimer value
dvt
final diagnosis of DVT
study
study indicator
Hypothetical dataset derived from the Individual Participant Data Meta-Analysis from Geersing et al (2014). The dataset consists of consecutive outpatients with suspected deep vein thrombosis, with documented information on the presence or absence of proximal deep vein thrombosis (dvt
) by an acceptable reference test. Acceptable such tests were either compression ultrasonography or venography at initial presentation, or, if venous imaging was not performed, an uneventful follow-up for at least three months.
Geersing GJ, Zuithoff NPA, Kearon C, Anderson DR, Ten Cate-Hoek AJ, Elf JL, et al. Exclusion of deep vein thrombosis using the Wells rule in clinically important subgroups: individual patient data meta-analysis. BMJ. 2014;348:g1340.
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'data.frame': 500 obs. of 15 variables:
$ sex : num 0 1 0 1 0 0 1 0 1 0 ...
$ malign : num 0 0 0 0 0 0 0 0 0 0 ...
$ par : num 0 0 1 0 0 0 0 0 0 0 ...
$ surg : num 0 0 0 0 0 0 0 0 1 0 ...
$ tend : num 1 1 0 1 1 0 0 1 1 1 ...
$ oachst : num 0 0 0 0 0 0 0 0 0 0 ...
$ leg : num 1 0 0 0 0 1 1 0 0 0 ...
$ notraum : num 1 1 1 1 1 0 0 1 0 1 ...
$ calfdif3: num 0 0 0 0 0 0 0 0 0 0 ...
$ pit : num 0 0 0 0 0 1 0 1 1 1 ...
$ vein : num 0 0 0 0 1 0 0 0 0 1 ...
$ altdiagn: num 1 0 1 1 1 0 1 1 1 1 ...
$ histdvt : num 0 1 0 0 0 0 1 0 0 0 ...
$ ddimdich: num 1 0 0 0 0 1 1 0 1 1 ...
$ dvt : num 0 0 0 0 0 0 0 0 0 0 ...
sex malign par surg tend oachst leg notraum calfdif3
0:299 0:460 0:446 0:446 0:192 0:472 0:313 0:103 0:315
1:201 1: 40 1: 54 1: 54 1:308 1: 28 1:187 1:397 1:185
pit vein altdiagn histdvt ddimdich dvt
0:185 0:411 0:226 0:417 0:190 0:418
1:315 1: 89 1:274 1: 83 1:310 1: 82
Call:
glm(formula = "dvt~sex+oachst+malign+surg+notraum+vein+calfdif3+ddimdich",
family = binomial, data = DVTipd)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5424 -0.5687 -0.2874 -0.1260 2.7104
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.1664 0.6365 -8.117 4.76e-16 ***
sex 0.8146 0.2825 2.883 0.00393 **
oachst 0.4324 0.6227 0.694 0.48739
malign 0.5679 0.4025 1.411 0.15826
surg 0.1002 0.4111 0.244 0.80734
notraum 0.3351 0.3700 0.906 0.36513
vein 0.4831 0.3186 1.516 0.12939
calfdif3 1.1841 0.2819 4.200 2.67e-05 ***
ddimdich 2.6081 0.5310 4.911 9.04e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 446.24 on 499 degrees of freedom
Residual deviance: 345.98 on 491 degrees of freedom
AIC: 363.98
Number of Fisher Scoring iterations: 6
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