[R] R: help with metasens

[Mario Petretta]

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:lists a dewey.myzen.co.uk] 
Inviato: mercoledì 12 agosto 2015 18.19
A: petretta a unina.it; r-help a 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, petretta a 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
>
>
>
> ----
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> DELL'UNIVERSITÀ DI NAPOLI
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> www.unina.it/Vademecum5permille
>
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--
Michael
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