[R-meta] metafor package in R - Risk ratios using rma.mv()

[Olivia Cords]

Hello!
My name is Olivia and I'm a researcher at Stanford University. Our group is
trying to calculate relative risk ratios for the a meta-analysis
extracting  incidence rates of tuberculosis disease using the metafor
package.

*Study Design: *
Incidence rates were extracted for each study identified by a systematic
review, with some studies reporting multiple rates for different years or
locations. In this case, multiple rates were treated as different study
cohorts, meaning that the data is clustered ('cohort_id' nested within
'study_id').

*Model:*
We used the rma.mv() function, inputting the calculated incidence rate as
the outcome variable ('pdc$yi), the variance ('pdc$vi'), WHO region
('who_region') and whether the study was conducted through passive or
active screening (passive_active') as moderators, and a random effects
argument for the study level ('study_id'). We are unclear how to go from
the output to Risk Ratios.

*Data:*
study_id cohort_id n_diagnosed person_years    who_region passive_active
131 34 77 14298    1Americas 1Passive
93 120 5 27750    1Americas 1Passive
93 121 14 277150    1Americas 1Passive
93 122 15 2000    1Americas 1Passive
136 383 2 2000    Africa 2Active
136 383 7 100000    Africa 2Active
187 16 16 100000    Africa 3Not Specified
187 517 2 100000    S.E. Asia 3Not Specified


*Code:*

library(xlsx)
library(metafor)


data <- read.xlsx('702_tb_incidence.xlsx', 1)
print(data)

#calculating the incidence rate and the variance
pd_ec <- escalc(
   measure = 'IR',
   xi = data$n_diagnosed,
   ti = data$person_years,
   data = data
)
pd_ec

#specifying mixed effects model
#first level cohort incidence rate and variation
#second level study_id
#who_region and passive_active as moderators
m0 <- rma.mv(pd_ec$yi, pd_ec$vi, method='REML', mods = ~ who_region +
passive_active,
        random= ~ 1 | study_id,
        tdist=TRUE,
        data=pd_ec)

summary(m0)

*Output:*

Multivariate Meta-Analysis Model (k = 8; method: REML)

  logLik  Deviance       AIC       BIC      AICc
 15.4961  -30.9922  -20.9922  -24.0607   39.0078

Variance Components:

            estim    sqrt  nlvls  fixed    factor
sigma^2    0.0000  0.0037      4     no  study_id

Test for Residual Heterogeneity:
QE(df = 4) = 94.4465, p-val < .0001

Test of Moderators (coefficients 2:4):
F(df1 = 3, df2 = 4) = 3.7931, p-val = 0.1152

Model Results:

                       estimate      se     tval    pval    ci.lb   ci.ub
intrcpt                  0.0027  0.0027   1.0073  0.3708  -0.0047  0.0101
who_regionAfrica        -0.0025  0.0046  -0.5493  0.6120  -0.0153  0.0102
who_regionS.E. Asia     -0.0027  0.0046  -0.5797  0.5932  -0.0154  0.0101
passive_active2Active   -0.0001  0.0053  -0.0167  0.9874  -0.0148  0.0146


Any advice/insight in much appreciated!


Best wishes,

Olivia

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