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Secondary_outcomes

Secondary_outcomes.Rmd

Load data 

#library(librarian)
#librarian::shelf(here, readxl, metafor, quiet = TRUE)

dat.second <- readxl::read_excel(here::here("data", "Dex_metan.xlsx"),
                         range = "B17:K79",
                         col_names = c("study",
                                       "m1i", "sd1i",
                                       "m2i", "sd2i",
                                       "n1i", "n2i",
                                       "summary1", "summary2",
                                       "outcome")
                         )

Secondary Outcome Analysis

Plateau pressure 

dat.plat <- metafor::escalc(measure="MD", 
                   m1i=m1i,
                   sd1i=sd1i,
                   n1i=n1i,
                   m2i=m2i,
                   sd2i=sd2i,
                   n2i=n2i,
                   data=dplyr::filter(dat.second, outcome=="Plateau Pressure"),
                   slab = study)

pm.plat <- metafor::rma(yi, vi, data=dat.plat, method="PM")
metafor::forest(pm.plat,
       at=seq(from=-8, to =2, by=2))

Peak pressure 

dat.peak <- metafor::escalc(measure="MD",
                   m1i=m1i,
                   sd1i=sd1i,
                   n1i=n1i,
                   m2i=m2i,
                   sd2i=sd2i,
                   n2i=n2i,
                   data=dplyr::filter(dat.second, outcome=="Peak Pressure"),
                   slab = study)

pm.peak <- metafor::rma(yi, vi, data=dat.peak, method="PM")
metafor::forest(pm.peak,
       at=seq(from=-8, to =2, by=2))

Respiratory Compliance: 

dat.resp <- metafor::escalc(measure="MD",
                   m1i=m1i,
                   sd1i=sd1i,
                   n1i=n1i,
                   m2i=m2i,
                   sd2i=sd2i,
                   n2i=n2i,
                   data=dplyr::filter(dat.second, outcome=="Respiratory Compliance"),
                   slab = study)

pm.resp <- metafor::rma(yi, vi, data=dat.resp, method="PM")
metafor::forest(pm.resp,
       at=seq(from=0, to =8, by=2))

FEV1 

dat.fev1<- metafor::escalc(measure="MD",
                   m1i=m1i,
                   sd1i=sd1i,
                   n1i=n1i,
                   m2i=m2i,
                   sd2i=sd2i,
                   n2i=n2i,
                   data=dat.second[grep(pattern = "FEV1", x = dat.second$outcome), ],
                   slab = study)

pm.fev1 <- metafor::rma(yi, vi, data=dat.fev1, method="PM")
metafor::forest(pm.fev1)

POD 1 

dat.pod1<- metafor::escalc(measure="MD",
                   m1i=m1i,
                   sd1i=sd1i,
                   n1i=n1i,
                   m2i=m2i,
                   sd2i=sd2i,
                   n2i=n2i,
                   data=dplyr::filter(dat.second, outcome=="FEV1 POD 1"),
                   slab = study)

pm.pod1 <- metafor::rma(yi, vi, data=dat.pod1, method="PM")
metafor::forest(pm.pod1)

POD 2 

dat.pod2<- metafor::escalc(measure="MD",
                  m1i=m1i,
                  sd1i=sd1i,
                  n1i=n1i,
                  m2i=m2i,
                  sd2i=sd2i,
                  n2i=n2i,
                  data=dplyr::filter(dat.second, outcome=="FEV1 POD 2"),
                  slab = study)
dat.pod2
#> 
#>           study m1i sd1i m2i sd2i n1i n2i summary1 summary2    outcome     yi 
#> 1 Lee et al (2) 2.2  0.5   2  0.4  50  50     <NA>     <NA> FEV1 POD 2 0.2000 
#> 2   Jannu et al 2.3  0.5   2  0.3  40  40     <NA>     <NA> FEV1 POD 2 0.3000 
#>       vi 
#> 1 0.0082 
#> 2 0.0085
pm.pod2 <- metafor::rma(yi, vi, data=dat.pod2, method="PM")
weights(pm.pod2)
#> Lee et al (2)   Jannu et al 
#>       50.8982       49.1018
dat.pod2
#> 
#>           study m1i sd1i m2i sd2i n1i n2i summary1 summary2    outcome     yi 
#> 1 Lee et al (2) 2.2  0.5   2  0.4  50  50     <NA>     <NA> FEV1 POD 2 0.2000 
#> 2   Jannu et al 2.3  0.5   2  0.3  40  40     <NA>     <NA> FEV1 POD 2 0.3000 
#>       vi 
#> 1 0.0082 
#> 2 0.0085
metafor::forest(pm.pod2)

Length of Stay 

dat.los <- metafor::escalc(measure="MD",
                  m1i=m1i,
                  sd1i=sd1i,
                  n1i=n1i,
                  m2i=m2i,
                  sd2i=sd2i,
                  n2i=n2i,
                  data=dplyr::filter(dat.second, outcome=="LOS"),
                  slab = study)

pm.los <- metafor::rma(yi, vi, data=dat.los, method="PM")
dat.los
#> 
#>           study  m1i sd1i  m2i sd2i n1i n2i summary1 summary2 outcome      yi 
#> 1    Meng et al 4.10 1.48 4.60 1.88  20  20     <NA>     <NA>     LOS -0.5000 
#> 2   Zhang et al 7.10 3.90 7.76 3.12  28  28     <NA>     <NA>     LOS -0.6600 
#> 3      Wu et al 5.60 2.50 5.90 2.50  30  30     <NA>     <NA>     LOS -0.3000 
#> 4   Jannu et al 6.33 4.61 9.66 5.38  40  40     <NA>     <NA>     LOS -3.3300 
#> 5 Lee et al (1) 7.16 3.93 8.03 3.93  25  25     <NA>     <NA>     LOS -0.8700 
#> 6 Lee et al (2) 6.10 4.58 7.13 3.81  50  50     <NA>     <NA>     LOS -1.0300 
#>       vi 
#> 1 0.2862 
#> 2 0.8909 
#> 3 0.4167 
#> 4 1.2549 
#> 5 1.2356 
#> 6 0.7098
metafor::forest(pm.los)

Excluding outlier Jannu et al.:

pm.los.exc.jannu <- metafor::leave1out(pm.los)
metafor::forest(pm.los.exc.jannu$estimate, sei=pm.los.exc.jannu$se, header=TRUE, xlab="Leave One Out Estimate", refline=coef(pm.los))

Note in the above figure that Study 4 is Jannu et al., hence this row gives the summary estimate when Jannu et al. is excluded.