DVTipd: Hypothetical dataset for diagnosis of Deep Vein Thrombosis...
In metamisc: Meta-Analysis of Diagnosis and Prognosis Research Studies

Description Usage Format Details Source Examples

A hypothetical dataset with 500 subjects suspected of having deep vein thrombosis (DVT).

1	data(DVTipd)

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.

data(DVTipd)
str(DVTipd) 
summary(apply(DVTipd,2,as.factor))

## Develop a prediction model to predict presence of DVT
model.dvt <- glm("dvt~sex+oachst+malign+surg+notraum+vein+calfdif3+ddimdich", 
                  family=binomial, data=DVTipd)
summary(model.dvt)

'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