[R-meta] A question about meta-regression Analysis

Viechtbauer, Wolfgang (SP) wolfg@ng@viechtb@uer @ending from m@@@trichtuniver@ity@nl
Fri Jul 13 19:02:32 CEST 2018


Thanks. Well, hard to say if these data are suggestive of a quadratic trend based on visual inspection.

The test of the coefficient for 'poly(year, 2)2' tells you whether the quadratic model fits significantly better than just the linear model.

Best,
Wolfgang

-----Original Message-----
From: Hamed Kianmehr [mailto:hkianme1 using binghamton.edu] 
Sent: Friday, 13 July, 2018 18:39
To: Viechtbauer, Wolfgang (SP)
Cc: r-sig-meta-analysis using r-project.org
Subject: Re: [R-meta] A question about meta-regression Analysis

ATTACHMENT(S) REMOVED: Plot.jpg 

Thank you for your response. 
Please see the attached file regarding the plot. 

On Fri, Jul 13, 2018 at 12:35 PM, Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
Dear Hamed,

Please post in plain text format. I cannot see any plot. Either attach it as a separate image file or post it somewhere else and provide a link.

As for 2 - If you use poly(), try:

preds <- predict(res, transf = transf.ilogit, newmods = unname(poly(years, degree=2)))

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On Behalf Of Hamed Kianmehr
Sent: Friday, 13 July, 2018 16:03
To: r-sig-meta-analysis using r-project.org
Subject: [R-meta] A question about meta-regression Analysis

I have some questions regarding the meta-regression analysis. I have the database on the proportion (Below bubble plot). My advisor asked me to do the polynomial regression. I have two questions:

1- Is it any polynomial regression pattern in this data?
2- I wrote below code but I couldn't get the line, Could you help me?
----------------
res<-rma(yi=y, vi=s2, data=data, method="DL",mods = ~ poly(year, degree=2))
years <- 1989:2016
preds <- predict(res, transf = transf.ilogit, newmods =cbind(years,years^2))
wi <- 0.5/sqrt(data$s2)
wi <- 0.5 + 3.0 * (wi - min(wi))/(max(wi) - min(wi))
plot(data$year, transf.ilogit(y), cex=wi,xlab="Year",ylab="Patient Expectation (%)")
lines(years, preds$pred)

--
Best Regards,
Hamed Kianmehr, Ph.D. Candidate
Department of Systems Science and Industrial Engineering
The State University of New York at Binghamton


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