Dear Olivia
I cannot answer all your questions but I think the first section can be
addressed by studying
http://www.metafor-project.org/doku.php/tips:comp_two_independent_estimates
You need to read a fair way in to get the answer but I think the issue
is that the separate models allow for different tau^2 whereas the common
model has a single estimate. The link shows you how to alter that.
I will leave the rest for others to have a go at.
Michael
On 07/08/2019 23:08, Olivia Cords wrote:
Dear Wolfgang,
Thanks so much again for your help. The comments you provided have been
incredibly useful and I have implemented your suggestions. I just wanted
follow up with you on this topic with a few additional clarifications
regarding incident rates, incident rate ratios, confidence intervals, and
similar issues for prevalence/odds ratios.
*Incidence Rates*
I calculated the incidence rates (measure = "IR", ti =
data$person_years/1000) for tuberculosis stratified by World Health
Organization Region and got the following results from the model.
m0 <- rma.mv(yi, vi, method='REML', mods = formula,
random= ~ 1 | study_id/cohort_id,
tdist=TRUE,
data=pd_ec)
estimate se tval
intrcpt 3.5210 0.9728 3.6195
who_region.1African Region 14.5115 2.6764 5.4219
who_region.1Eastern Mediterranean Region -0.9006 3.8084 -0.2365
who_region.1European Region 5.2299 2.2226 2.3531
who_region.1South-East Asian Region 7.2904 3.7296 1.9547
who_region.1Western Pacific Region 2.1339 3.3186 0.6430
ci.lb ci.ub
intrcpt 1.6083 5.4337 ***
who_region.1African Region 9.2492 19.7738 ***
who_region.1Eastern Mediterranean Region -8.3885 6.5873
who_region.1European Region 0.8600 9.5998 *
who_region.1South-East Asian Region -0.0426 14.6235 .
who_region.1Western Pacific Region -4.3910 8.6587
To find the incidence rates, I added the slopes to the intercept to
produce the following Incident Rates/1000 person years:
Intercpt (Americas) 3.521
African Region 18.03249
Eastern Mediterranean Region 2.620433
European Region 8.750917
South-East Asian Region 10.81145
Western Pacific Region 5.654908
To confirm these results I also ran the model on the dataset subsetted by
region, using the 'estimate' as each region's incidence rate. This
in the following:
Region of the Americas
3.244745079
African Region
19.06.383209
Eastern Mediterranean Region 2.491278082
European Region 7.675655135
South-East Asian Region
11.48738471
Western Pacific Region 5.600276079
*Q1) Why are the Incidence rates different when using the entire dataset
versus first subsetting by region? *
*Rate Ratios:*
Following your suggestion above for calculating rate ratios for each WHO
Region, I used the measure = "IRLN" and exponentiated the coefficients of
the model. I got the following model output:
Multivariate Meta-Analysis Model (k = 390; method: REML)
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 2.3898 1.5459 76 no study_id
sigma^2.2 0.5833 0.7637 390 no study_id/cohort_id
Test for Residual Heterogeneity:
QE(df = 384) = 118469.9261, p-val < .0001
Test of Moderators (coefficients 2:6):
F(df1 = 5, df2 = 384) = 7.3370, p-val < .0001
Model Results:
estimate se tval
intrcpt -7.1326 0.2449 -29.1259
who_region.1African Region 3.1798 0.6332 5.0217
who_region.1Eastern Mediterranean Region 0.9541 0.9634 0.9904
who_region.1European Region 1.8665 0.5612 3.3261
who_region.1South-East Asian Region 2.6693 0.9360 2.8518
who_region.1Western Pacific Region 1.4697 0.8381 1.7536
ci.lb ci.ub
intrcpt -7.6141 -6.6511 ***
who_region.1African Region 1.9348 4.4248 ***
who_region.1Eastern Mediterranean Region -0.9401 2.8483
who_region.1European Region 0.7631 2.9698 ***
who_region.1South-East Asian Region 0.8290 4.5096 **
who_region.1Western Pacific Region -0.1781 3.1176 .
And exponentiating the coefficients, I got these rate ratios:
Intrcpt (Americas) 0.000799
African Region 24.04225
Eastern Mediterranean Region 2.596419
European Region 6.465383
South-East Asian Region 14.42971
Western Pacific Region 4.348128
Based on the incidence rates produced by using the entire dataset in the
model and first subsetting by region, these Rate Ratios don't seem to be
correct. Simply dividing the incidence rates by a comparator (Region of
Americas) to produce rate ratios would give the following:
Entire Dataset First Subsetted
Region of the Americas
African Region 5.121411531 5.875294245
Eastern Mediterranean Region 0.744106788 0.767788539
European Region 2.485089463 2.365564921
South-East Asian Region 3.070434536 3.540304225
Western Pacific Region 1.604657768 1.725952561
*Q2) Is exponentiating the coefficients (measure = IRLN) the way to
calculate rate ratios? Why are these results so different?*
*Confidence Intervals*
To calculate the 95% confidence intervals for the rate ratios, I first
calculated the standard deviation (SD[ln(IR)] = (1/A1 + 1/A2)^0.5, where
and A2 are the number of tuberculosis cases in each region), and then
the following: 95% CI's = exp[ln(IR) ? 1.96(SD)]).
*Q3) Is this the right approach to calculating the confidence intervals?
there a way to calculate the confidence intervals from the model (either
measure = "IRLN" or measure = "IR") output?*
*Prevalence:*
Additionally, we have data for calculating tuberculosis prevalence rate
WHO region and are trying to calculate odds ratios. For prevalence rate,
all of the code for the model is the same, except the escalc function is
follows:
pd_ec <- escalc(measure = 'PR', xi =
data_sub$prev_positive,ni
= data_sub$prev_total_n, append = TRUE,
data = data_sub),
*Q4) For prevalence rates, should we first subset the data to find each
region's prevalence rate, or use the entire dataset (adding intercept to
each region's slope)?*
*Q5)How would we calculate odds ratios using the model?*
*Q6)How would we calculate the confidence intervals for these odds ratios
using the model? *
Thank you so much again for your advice. It's really been indispensable
is greatly appreciated.
All the best,
Olivia
On Fri, Jul 26, 2019 at 1:44 PM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
Hi Olivia,
A couple comments before I get to your actual question:
1) You might want to divide the person-years by some factor. For
ti = data$person_years / 1000
The incidence rates then reflect the number of cases per 1000 person
years. That way, the rates aren't so small and the same goes for the
coefficients (e.g., the 0.0027 will turn into 2.7, which is a bit
communicate).
2) You don't need to write pd_ec$yi and pd_ec$vi when you use
So, rma.mv(yi, vi, ..., data=pd_ec) is sufficient.
3) You should include random effects for studies (as you have done) and
also random effects for estimates within studies. So:
pd_ec$id <- 1:nrow(pd_ec)
random = ~ 1 | study_id / id
In fact, I see that you may have multiple estimates for the same cohort,
so one could even use an additional level, that is:
random = ~ 1 | study_id / cohort_id / id
But if you only have 8 rows of data, then I wouldn't do that (so then I
would use random = ~ 1 | study_id / id).
4) Now for your actual question: You are analyzing incidence rates, so
coefficients of your model reflect how the (average) rate changes as a
function of those moderators (e.g., while the rate is 0.0027 for
"1Americas", the rate is 0.0027-0.0025=0.0002 for "Africa"). In other
words, the coefficients represent incidence rate differences. If you
ratios, you should analyze log incidence rates (measure="IRLN"). Then
coefficients reflect differences between log incidence rates, so if you
exponentiate the coefficients of your model, you get incidence rate
Best,
Wolfgang
-----Original Message-----
From: R-sig-meta-analysis [mailto:
r-sig-meta-analysis-bounces at r-project.org] On Behalf Of Olivia Cords
Sent: Friday, 26 July, 2019 21:27
To: r-sig-meta-analysis at r-project.org
Subject: [R-meta] metafor package in R - Risk ratios using rma.mv()
ATTACHMENT(S) REMOVED: metafor_output_0724 (1).png |
| 702_metafor_tb_incidence (4).r
Hello!
My name is Olivia and I'm a researcher at Stanford University. Our group
is trying to calculate relative risk ratios for the a meta-analysis
extracting incidence rates of tuberculosis disease using the metafor
package.
Study Design:
Incidence rates were extracted for each study identified by a systematic
review, with some studies reporting multiple rates for different years
locations. In this case, multiple rates were treated as different study
cohorts, meaning that the data is clustered ('cohort_id' nested within
'study_id').
Model:
We used the rma.mv() function, inputting the calculated incidence rate
the outcome variable ('pdc$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
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
Any advice/insight in much appreciated!
Best wishes,
Olivia
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