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'UNIVERSIT? DI NAPOLI
Codice Fiscale: 00876220633
www.unina.it/Vademecum5permille
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 at 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'UNIVERSIT? DI NAPOLI > Codice Fiscale: 00876220633 > www.unina.it/Vademecum5permille > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.-- Michael http://www.dewey.myzen.co.uk/home.html
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 at dewey.myzen.co.uk] Inviato: mercoled? 12 agosto 2015 18.19 A: petretta at unina.it; r-help at 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 at 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'UNIVERSIT? DI NAPOLI > Codice Fiscale: 00876220633 > www.unina.it/Vademecum5permille > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.-- Michael http://www.dewey.myzen.co.uk/home.html