# [R-meta] Dependant variable in Meta Analysis

Viechtbauer, Wolfgang (SP) wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Sun Jun 7 14:32:57 CEST 2020

```See responses below.

>-----Original Message-----
>From: Tarun Khanna [mailto:khanna using hertie-school.org]
>Sent: Friday, 05 June, 2020 21:48
>To: Viechtbauer, Wolfgang (SP); r-sig-meta-analysis using r-project.org
>Subject: Re: Dependant variable in Meta Analysis
>
>
>As you correctly said, most of the studies in my set use models of the form
>ln(y) = b0 + b1 + e. Can we relax the requirement of units of measurement of
>y in this case because the interpretation of b1 is % change in y for unit
>change in x?

b1 is not % change, exp(b1) is. But yes, one could combine estimates of b1 from different studies even if the units of y differ across studies, as long as they only differ by a multiplicative transformation.

>While most of the studies in my set employ regression models, some employ
>difference of means test (with the group means and standard error reported).
>How can I calculate coefficients in this case that are commensurable to the
>ones coming from studies that employ the regression models? Would converting
>the means to percentage change work? For example if mt is treatment mean and
>ct is control mean, then is the percentage difference mt-ct/ct commensurable
>with estimates coming from the regression? A previous meta analysis in the
>field does this but I am not sure if this is correct.

In the model ln(y) = b0 + b1 x + e, if x is a dummy variable that distinguishes two groups (e.g., x = 0 for group 1 and x = 1 for group 2), then b1 is the estimated mean difference of log(y) for the two groups. That's similar (but not the same -- see below) to using the log-transformed ratio of means as the effect size measure. See help(escalc) and search for "ROM". Using (mt-mc)/mc would not be correct to use, since b1 is not % change, but log-transformed % change. And log((mt-mc)/mc) = log(mt/mc - 1), which is like ROM, but not quite right (due to the -1).

The reason why using ROM isn't quite right is due to Jensen's inequality (https://en.wikipedia.org/wiki/Jensen's_inequality). b1 in the regression model is mean(log(y) for group 1) - mean(log(y) for group 2). However, you have mean(y for group 1) and mean(y for group 2) and when you compute "ROM" based on this, you get log(mean(y for group 1)) - log(mean(y for group 2)). These two mean differences are not the same. They might not differ greatly though. An example:

set.seed(1234)
x <- c(rep(0,50), rep(1,50))
y <- 100 + 5 * x + rnorm(100, 0, 10)
lm(log(y) ~ x)
mean(log(y)[x==1]) - mean(log(y)[x==0])
log(mean(y[x==1])) - log(mean(y[x==0])) # ROM
escalc(measure="ROM", m1i=mean(y[x==1]), m2i=mean(y[x==0]), sd1i=sd(y[x==1]), sd2i=sd(y[x==0]), n1i=50, n2i=50)

So, with this caveat aside (but discussed as part of the limitations), I would use ROM for those studies. You can also code 'b1 used vs ROM used' as a dummy variable and examine empirically via meta-regression if there are systematic differences between these two cases (although those could stem from other things besides Jensen's inequality).

Best,
Wolfgang

>From: Viechtbauer, Wolfgang (SP)
><wolfgang.viechtbauer using maastrichtuniversity.nl>
>Sent: 04 June 2020 15:10:04
>To: Tarun Khanna; r-sig-meta-analysis using r-project.org
>Subject: RE: Dependant variable in Meta Analysis
>
>Assuming that the coefficients are commensurable, you can just meta-analyze
>them directly. The squared standard errors of the coefficients are then the
>sampling variances.
>
>With commensurable, I mean that they measure the same thing and can be
>directly compared. For example, suppose the regression model y = b0 + b1 x +
>e has been examined in multiple studies. Since b1 reflects how many units y
>changes (on average) for a one-unit increase in x, the coefficient b1 is
>only comparable across studies if y has been measured in the same units
>across studies and x has been measured in the same units across studies (or
>if there is a known linear transformation that converts x from one study
>into the x from another study (and the same for y), then one can adjust b1
>to make it commensurable across studies).
>
>In certain models, one can relax the requirement that the units must be the
>same. For example, if the model is ln(y) = b0 + b1 x + e, then the units of
>y can actually differ across studies if they are multiplicative
>transformations of each other. If the model is ln(y) = b0 + b1 ln(x) + e,
>then x can also differ across studies in terms of a multiplicative
>transformation.
>
>I think the latter gets close to (or is?) what people in economics do to
>estimate 'elasticities' and this is in fact what you might be dealing with.
>
>Another complexity comes into play when there are other x's in the model.
>Strictly speaking, all models should include the same set of predictors as
>otherwise the coefficient of interest is 'adjusted for' different sets of
>covariates, which again makes it incommensurable. As a rough approximation
>to deal with different sets of covariates across studies, one could fit a
>meta-regression model (with the coefficient of interest as outcome) where
>one uses dummy variables to indicate for each study which covariates were
>included in the original regression models.
>
>Best,
>Wolfgang
>
>>-----Original Message-----
>>From: Tarun Khanna [mailto:khanna using hertie-school.org]
>>Sent: Thursday, 04 June, 2020 14:16
>>To: Viechtbauer, Wolfgang (SP); r-sig-meta-analysis using r-project.org
>>Subject: Re: Dependant variable in Meta Analysis
>>
>>
>>The "beta coefficients" that I refer to are not standardized regression
>>coefficients but the relevant regression coefficients in the original
>>studies. Would it be correct to direcly meta analyze the coefficients even
>>when they are not standardized? How to we take into account the standard
>>error of the coefficients? I have seen meta analysis in the literature that
>>use the tranformation beta coefficient/ (sample size)^1/2 but I don't see
>>how that takes into account the associated standard error.
>>
>>I have instead been calculating r coefficients using the t values of the
>>relevant coefficients and the sample size using the following formula.
>>
>>r = ( t^2 / (t^2 + sample size) )^1/2
>>
>>I have been using the r to Fisher's Z transformation that you
>>mentioned. Unfortunately, like you mentioned most of the studies
>>employ multivariate analysis and so the transformation is not accurate.
>What
>>would be the correct way to handle this?
>>
>>Best
>>Tarun
>>
>>Tarun Khanna
>>PhD Researcher
>>
>>Hertie School
>>
>>Friedrichstraße 180
>>10117 Berlin ∙ Germany
>>khanna using hertie-school.org ∙ www.hertie-school.org
>>________________________________________
>>From: Viechtbauer, Wolfgang (SP)
>><wolfgang.viechtbauer using maastrichtuniversity.nl>
>>Sent: 04 June 2020 13:56:59
>>To: Tarun Khanna; r-sig-meta-analysis using r-project.org
>>Subject: RE: Dependant variable in Meta Analysis
>>
>>Dear Tarun,
>>
>>What exactly do you mean by 'beta coefficient'? A standardized regression
>>coefficient? In the (very unlikely) case that the model includes no other
>>predictors and is just a standard regression model, then the standardized
>>regression coefficient for that single predictor is actually identical to
>>the correlation beteen the predictor and the outcome and converting this
>>correlation via Fisher's r-to-z transformation is fine (and then 1/(n-3)
>can
>>be used as the corresponding sampling variance). However, if there are
>other
>>predictors in the model, then the standardized regression coefficient is
>not
>>a simple correlation and while one can still apply Fisher's r-to-z
>>transformation to the coefficient, it will not have a variance of 1/(n-3)
>>and assuming so would be wrong.
>>
>>Why don't you just meta-analyze the 'beta coefficients' directly? If these
>>coefficients reflect percentage change, it sounds like they are 'unitless'
>>and comparable across studies. Then you get the pooled estimate of the
>>percentage change directly from the model.
>>
>>Best,
>>Wolfgang
>>
>>>-----Original Message-----
>>>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-
>>project.org]
>>>On Behalf Of Tarun Khanna
>>>Sent: Thursday, 04 June, 2020 13:41
>>>To: r-sig-meta-analysis using r-project.org
>>>Subject: [R-meta] Dependant variable in Meta Analysis
>>>
>>>Dear All,
>>>
>>>I am conducting a meta analysis of reduction in energy consumption in
>>>households that have been exposed to certain behavioural interventions in
>>>trials. The beta coefficients in the regressions in my the original
>studies
>>>can ususally be interpreted as percentage change in electricity
>>consumption.
>>>To do the meta analysis I am converting these beta coefficients to
>Fisher's
>>>Z. My problem is that Fisher's Z is not as easy to interpret as percentage
>>>change in energy consumption.
>>>
>>>Question 1: Is it possible to do the meta anlysis using the beta
>>>coefficients coming from the original studies so that the results remain
>>>easy to interpret?
>>>
>>>Question 2: Is it sensible to convert the final Fisher's Z estimates back
>>to
>>>the dependant variable coming from the studies?
>>>
>>>Sorry if this question sounds too basic.
>>>
>>>Best
>>>
>>>Tarun
>>>Tarun Khanna
>>>PhD Researcher
>>>Hertie School
>>>
>>>Friedrichstraße 180
>>>10117 Berlin ∙ Germany
>>>khanna using hertie-school.org ∙ www.hertie-school.org
```