[R] help with metasens

[petretta at unina.it]

Dear all,

I use R 3.1.1 for Windows (x 64).

I performed a meta-analysis of hazard ratio using the below reported  
Dataset and metagen function from package meta.

meta1<-metagen(Dataset$lnHR, Dataset$seHR, sm="HR")

Thereafter, I try to use the copas function from package metasens.

cop1<-copas(meta1)


and I have these 3 warnings:

Warning in sqrt(solve(junk2$hessian + 1e-08)[1, 1]) :
NaN was produced
Warning in sqrt(solve(junk2$hessian + 1e-08)[1, 1]) :
NaN was produced
Warning in sqrt(solve(junk2$hessian + 1e-08)[1, 1]) :
NaN was produced

If I try:
plot (cop1)

  I have:
ERROR:
object "is.relative.effect" not found

Any suggestion is welcome.

The Dataset is:

    id Year      lnHR       seHR
1   1 2001 0.6881346 0.06940859
2   2 2001 1.4036430 0.60414338
3   3 2002 0.7419373 0.28897730
4   4 2003 1.5475625 0.45206678
5   5 2003 1.4816046 0.44859666
6   6 2005 0.9162908 0.17166950
7   7 2006 1.2697605 0.34205049
8   8 2009 0.8960880 0.24626434
9   9 2011 1.5040774 0.24683516
10 10 2012 0.4510756 0.17213355
11 11 2008 0.9895412 0.26590857
12 12 2009 2.8094027 0.61304092
13 13 2010 0.9162908 0.21362771
14 14 2011 0.5068176 0.15060408
15 15 2012 3.0027080 0.27239493
16 16 2013 1.9837563 0.55793673
17 17 2013 3.0492730 0.18798657
18 18 2014 1.2974632 0.44759619
19 19 2014 0.8241754 0.39551640
20 20 2014 2.2617631 0.56545281

The code used are:

meta1<-metagen(Dataset$lnHR, Dataset$seHR, sm="HR")

> meta1
       HR             95%-CI %W(fixed) %W(random)
1   1.99 [ 1.7369;  2.2800]     42.92       5.99
2   4.07 [ 1.2455; 13.2997]      0.57       3.71
3   2.10 [ 1.1919;  3.7000]      2.48       5.28
4   4.70 [ 1.9378; 11.3998]      1.01       4.47
5   4.40 [ 1.8264; 10.5998]      1.03       4.49
6   2.50 [ 1.7857;  3.5000]      7.02       5.75
7   3.56 [ 1.8209;  6.9599]      1.77       5.03
8   2.45 [ 1.5120;  3.9700]      3.41       5.47
9   4.50 [ 2.7740;  7.2999]      3.39       5.47
10  1.57 [ 1.1204;  2.2000]      6.98       5.75
11  2.69 [ 1.5974;  4.5300]      2.92       5.38
12 16.60 [ 4.9921; 55.1988]      0.55       3.67
13  2.50 [ 1.6447;  3.8000]      4.53       5.60
14  1.66 [ 1.2357;  2.2300]      9.12       5.81
15 20.14 [11.8085; 34.3497]      2.79       5.36
16  7.27 [ 2.4357; 21.6996]      0.66       3.94
17 21.10 [14.5971; 30.4998]      5.85       5.69
18  3.66 [ 1.5223;  8.7999]      1.03       4.49
19  2.28 [ 1.0502;  4.9499]      1.32       4.76
20  9.60 [ 3.1693; 29.0794]      0.65       3.90

Number of studies combined: k=20

                          HR           95%-CI       z  p.value
Fixed effect model   2.7148 [2.4833; 2.9679] 21.9628 < 0.0001
Random effects model 3.9637 [2.7444; 5.7247]  7.3426 < 0.0001

Quantifying heterogeneity:
tau^2 = 0.5826; H = 3.56 [3.04; 4.16]; I^2 = 92.1% [89.2%; 94.2%]

Test of heterogeneity:
       Q d.f.  p.value
  240.64   19 < 0.0001

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2

> cop1<-copas(meta1)

Warning in sqrt(solve(junk2$hessian + 1e-08)[1, 1]) :
NaN was produced

> plot (cop1)

ERROR:
object "is.relative.effect" not found

-------------------------------------------------------
Mario Petretta
Associate Professor of Internal Medicine
Department of Translational Medical Sciences
Naples University Federico II Italy



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