[R-meta] metafor: Interaction between a factor and a continous variable
Kraus, Ute, Dr.
ute@kr@u@ @end|ng |rom he|mho|tz-muenchen@de
Tue Jan 5 10:55:51 CET 2021
Dear everybody!
This is my very first post and I would appreciate it very much if somebody
has time enough and could help me.
I am currently working on a meta-analysis on sex differences in the
association between air pollutants and cardiovascular mortality. I
calculated a mixed effects model including an interaction of a factor
(sex) and a continuous variable (publication year). However, I find it
difficult to interpret the estimates, and I get stuck when I want to graph
the results.
Here are the code and the results. The continuous variable “publication
year” is called “moderator” in my program.
Question 1: Are my following interpretations correct?
The Test of moderators shows that main effects and interaction term has no
significant effect (p-value 0.3482), explaining no heterogeneity between
studies (R2=0%).
The interaction between sex and publication year is not significant
(pval=0.3176).
Meaning of model results:
Intercept à estimate for men, when moderator (=publication year) = 0
Moderator à estimate for moderator (=publication year); with each 1 year
that an article is published later, the average estimate for the
association between air pollution and cardiovascular mortality increases
by 0.0001 (not significant)
Factor(sex)women à estimate indicates the effect of women compared to men;
women have a 0.2127 lower average mortality risk related to air pollutants
than men (not significant)
Moderator:factor(sex)women à estimate indicate by how much the risk in
women increases with each increase of one year of publication compared to
men (0.0001 greater increase than men; not significant).
Question 2: How can I present the results graphically?
I would need an intercept and a slope for each sex group showing the
change in the average mortality risk by a 1-year increase in publication
year. I would calculate it from the model results:
Intercept men: -0.1013 (estimate intrcpt)
Slope men: 0.0001 (estimate moderator)
Intercept women: -0.1013 – 0.2127 (estimate intrcpt + estimate
factor(sex)women)
Slope women: 0.0001+0.0001 (estimate moderator + estimate
moderator:factor(sex)women)
However, I am not really convinced that this is correct. And I think there
is a more elegant and easier way, isn’t there? I think it does not make
sense to calculate a model without intercept
(mods=~moderator:factor(sex)-1), which would give me the slopes, I guess,
but then I have just a slope but no intercept.
Thank you very much for your help in advance!
Best,
Ute
Helmholtz Zentrum MÃ¼nchen
Helmholtz Zentrum Muenchen
Deutsches Forschungszentrum fuer Gesundheit und Umwelt (GmbH)
Ingolstaedter Landstr. 1
85764 Neuherberg
www.helmholtz-muenchen.de
Aufsichtsratsvorsitzende: MinDir.in Prof. Dr. Veronika von Messling
Geschaeftsfuehrung: Prof. Dr. med. Dr. h.c. Matthias Tschoep, Kerstin Guenther
Registergericht: Amtsgericht Muenchen HRB 6466
USt-IdNr: DE 129521671
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