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List: sas-l
Subject: SAS Forum: Meta analysis of a single proportion in R with forest and funnel plots
From: Roger DeAngelis <rogerjdeangelis () GMAIL ! COM>
Date: 2020-01-13 21:29:39
Message-ID: 4048271701325509.WA.rogerjdeangelisgmail.com () listserv ! uga ! edu
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SAS Forum: Meta analysis of a single proportion in R with forest and funnel plots \
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Pooling data from 14 clinical trails and compting a pooled confidence interval on \
rare events.
\
For meta analsis R is the prefered language? \
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Output graphics \
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FULLEL PLOT \
https://tinyurl.com/tahhjrn \
https://github.com/rogerjdeangelis/utl-meta-analysis-of-a-single-proportion-in-R-with-forest-and-funnel-plots \
\
FOREST PLOT \
https://tinyurl.com/rgfde8h \
https://github.com/rogerjdeangelis/utl-meta-analysis-of-a-single-proportion-in-R-with-forest-and-funnel-plots/blob/master/forest.pdf \
\
related github \
https://tinyurl.com/vb5jbub \
https://github.com/rogerjdeangelis/utl-meta-analysis-of-deaths-in-twenty-studies-with-forest-plot \
\
SAS COMMUNITY \
https://tinyurl.com/v9462dd \
https://communities.sas.com/t5/Statistical-Procedures/Meta-analysis-of-proportions-using-SAS/m-p/616936 \
\
macros \
https://tinyurl.com/y9nfugth \
https://github.com/rogerjdeangelis/utl-macros-used-in-many-of-rogerjdeangelis-repositories \
\
\
I sugest you take a look at metaprop, metafor and meta \
I know some of these packages were developed by leading academics. \
https://www.rdocumentation.org/packages/meta/versions/4.9-6/topics/metaprop \
\
*_ _ \
(_)_ __ _ __ _ _| |_ \
| | '_ \| '_ \| | | | __| \
| | | | | |_) | |_| | |_ \
|_|_| |_| .__/ \__,_|\__| \
|_| \
; \
\
options validvarname=upcase; \
libname sd1 "d:/sd1"; \
data sd1.have; \
input study$ country$ response sample; \
cards4; \
Batra Aus 88 199 \
Berry13 US 130 250 \
Berry17 US 65 170 \
Black US 155 300 \
Cecil Eur 125 250 \
Dosa US 19 50 \
MIME Aus 185 320 \
MISE US 82 130 \
KILO US 77 110 \
PERT US 50 111 \
LEPT Aus 44 180 \
;;;; \
run;quit; \
\
\
SD1.HAVE total obs=11 \
\
STUDY COUNTRY RESPONSE SAMPLE \
\
Batra Aus 88 199 \
Berry13 US 130 250 \
Berry17 US 65 170 \
Black US 155 300 \
Cecil Eur 125 250 \
Dosa US 19 50 \
MIME Aus 185 320 \
MISE US 82 130 \
KILO US 77 110 \
PERT US 50 111 \
LEPT Aus 44 180 \
\
* _ _ \
___ _ _| |_ _ __ _ _| |_ \
/ _ \| | | | __| '_ \| | | | __| \
| (_) | |_| | |_| |_) | |_| | |_ \
\___/ \__,_|\__| .__/ \__,_|\__| \
|_| \
; \
\
FULLEL PLOT \
https://tinyurl.com/tahhjrn \
\
FOREST PLOT \
https://tinyurl.com/rgfde8h \
\
\
WORK.WANT total obs=30 \
\
Study Deaths Total Proportion 95%-CI \
Setting: Aus \
Batra 88 199 0.442 [0.372; 0.514] \
MIME 185 320 0.578 [0.522; 0.633] \
LEPT 44 180 0.244 [0.184; 0.314] \
Subtotal 699 0.454 [0.417; 0.491] \
Heterogeneity: I 2 = 94%, p < 0.01 \
\
Setting: US \
Berry13 130 250 0.520 [0.456; 0.583] \
Berry17 65 170 0.382 [0.309; 0.460] \
Black 155 300 0.517 [0.459; 0.574] \
Dosa 19 50 0.380 [0.247; 0.528] \
MISE 82 130 0.631 [0.542; 0.714] \
KILO 77 110 0.700 [0.605; 0.784] \
PERT 50 111 0.450 [0.356; 0.548] \
Subtotal 1121 0.516 [0.486; 0.545] \
Heterogeneity: I 2 = 86%, p < 0.01 \
\
Setting: Eur \
Cecil 125 250 0.500 [0.436; 0.564] \
Subtotal 250 0.500 [0.438; 0.562] \
Heterogeneity: not applicable \
\
Total 2070 0.493 [0.471; 0.514] \
Heterogeneity: I = 91%, p < 0.01 \
Residual heterogeneity: I 2 0.2 p < 0.4 \
\
* _ _ _ \
___ ___ | |_ _| |_(_) ___ _ __ \
/ __|/ _ \| | | | | __| |/ _ \| '_ \ \
\__ \ (_) | | |_| | |_| | (_) | | | | \
|___/\___/|_|\__,_|\__|_|\___/|_| |_| \
\
; \
\
%utl_submit_r64(' \
library(meta); \
library(haven); \
library(metafor); \
library(SASxport); \
library(funnelR); \
have<-read_sas("d:/sd1/have.sas7bdat"); \
have; \
studies <- have$STUDY; \
obs <- have$RESPONSE; \
denom <- have$SAMPLE; \
setting <- have$COUNTRY; \
m1 <- metaprop(obs, denom, studies, comb.random=F, byvar=setting, \
bylab="Setting", byseparator=": "); \
m2 <- metaprop(obs, denom, studies, comb.random=F, byvar=setting, \
bylab="Setting", byseparator=": ", method="GLMM"); \
m2$w.fixed <- m1$w.fixed; \
pdf("d:/pdf/forest.pdf",width = 10, height =8); \
forest(m2, print.tau2 = FALSE, col.by="black", text.fixed = "Total", \
text.fixed.w = "Subtotal", rightcols = c("effect","ci"), digits=3L, \
leftlabs=c("Study","Deaths","Total")); \
pdf(); \
colnames(have)<-c("id","country","n","d"); \
flimits <- fundata(input=have, \
benchmark=0.50, \
alpha=0.80, \
alpha2=0.95, \
method="approximate", \
step=1); \
funnelPlt <- funplot(input=have,flimits); \
pdf("d:/pdf/funnel.pdf",width = 10, height =8); \
funnelPlt; \
pdf(); \
'); \
\
\
* scape the pdf to create want SAS dataset; \
%utl_submit_r64(' \
library("tm"); \
library(SASxport); \
file <- "d:/pdf/forest.pdf"; \
Rpdf <- readPDF(control = list(text = "-layout")); \
corpus <- VCorpus(URISource(file), \
readerControl = list(reader = Rpdf)); \
want <- as.data.frame(content(content(corpus)[[1]])); \
colnames(want)<-"lines"; \
lines <- as.data.frame(strsplit(as.character(want$lines), split="\\r\\n")); \
colnames(lines)<-"lines"; \
str(lines); \
lines[] <- lapply(lines, function(x) if(is.factor(x)) as.character(x) else x); \
write.xport(lines,file="d:/xpt/lines.xpt"); \
'); \
\
* convert from VR transport to SAS dataset; \
libname xpt xport "d:/xpt/lines.xpt"; \
data want; \
set xpt.lines; \
run;quit; \
libname xpt clear; \
\
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