diagonal bacn is a typo, sorry for that. My brain meant to type diagonal band, ie going in this case from north west to south east but my fingers failed completely.

On 13/08/2015 10:25, Mario Petretta wrote:
Many thanks for your suggestion.

I will try a new database search and the hc metaphor function.

Mario

PS: what is diagonal bacn?

-----Messaggio originale-----
Da: Michael Dewey [mailto:li...@dewey.myzen.co.uk]
Inviato: mercoledì 12 agosto 2015 18.19
A: petre...@unina.it; r-help@r-project.org
Oggetto: Re: [R] help with metasens

Dear Mario

I do not use metasens myself so cannot be of direct help but I have looked at 
your dataset and it does seem rather strange (as you perhaps know). You have 
two quite large studies with very large hazard ratios and if we ignore them all 
the rest of the studies fall on a diagonal bacn indicative of extreme small 
study bias.

One thing you could consider is to use metafor and within it use the hc 
function which uses a different approach due to Henmi and Copas (the same 
Copas).

On 12/08/2015 15:19, petre...@unina.it wrote:
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



----
5x1000 AI GIOVANI RICERCATORI
DELL'UNIVERSITÀ DI NAPOLI
Codice Fiscale: 00876220633
www.unina.it/Vademecum5permille

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--
Michael
http://www.dewey.myzen.co.uk/home.html



--
Michael
http://www.dewey.myzen.co.uk/home.html

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