[R-meta] Intercept-slope model & network meta-analysis
Juan Pablo Edwards Molina
edwardsmolina at gmail.com
Tue Aug 29 16:05:54 CEST 2017
Thanks Wolfgang, I will follow your advice for the mixed model, including
the block effect.
For the network model, what should I use for V in:
rma.mv(yi, V, W, mods,...
Juan
*Juan*
On Tue, Aug 29, 2017 at 10:48 AM, Viechtbauer Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
> Much more legible - thanks.
>
> As for your first question: " Question: do I need to include the moderator
> variables in random effects? Is it enough to use the AIC to test the
> goodness of fit of the models and likelihood ratio of them to select the
> best model?"
>
> The moderator variables are constant within 'study', so you cannot include
> them as random effects.
>
> As for model selection, there are many different approaches and opinions
> one can take. You could use information criteria (like AIC) to select the
> model, but make sure you use REML=FALSE then (since you are comparing
> models with different fixed effects). Or you could fit the 'sev * mod_A'
> model, test the interaction (and report the results), if significant,
> report the results from that model, if not fit the 'sev + mod_A+' model and
> report that model.
>
> You may also want to consider including block as a random effect. So:
> (sev|study/rep)
>
> As for the second part: I would stick to just analyzing the raw data. I
> see no benefit here for aggregating and analyzing the means.
>
> Best,
> Wolfgang
>
> --
> Wolfgang Viechtbauer, Ph.D., Statistician | Department of Psychiatry and
> Neuropsychology | Maastricht University | P.O. Box 616 (VIJV1) | 6200 MD
> Maastricht, The Netherlands | +31 (43) 388-4170 | http://www.wvbauer.com
>
> -----Original Message-----
> From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bo
> unces at r-project.org] On Behalf Of Juan Pablo Edwards Molina
> Sent: Tuesday, August 29, 2017 15:25
> To: r-sig-meta-analysis at r-project.org
> Subject: [R-meta] Intercept-slope model & network meta-analysis
>
> Dear List,
>
> I have a datset containing 36 field plots experiments testing the effect
> of several fungicides to control a soybean fungic disease.
>
> This is how my raw data looks like (36 independent studies - CRBDs):
>
> study fungic rep Mod_A Mod_B sev yield
> 1 Check 1 2High 1Low 55 2918
> 1 Check 2 2High 1Low 50 3468
> 1 Check 3 2High 1Low 45 1626
> 1 Check 4 2High 1Low 40 2921
> 1 Trt_A 1 2High 1Low 35 2414
> 1 Trt_A 2 2High 1Low 40 3104
> 1 Trt_A 3 2High 1Low 25 1878
> 1 Trt_A 4 2High 1Low 30 1952
> 1 Trt_B 1 2High 1Low 40 2708
> 1 Trt_B 2 2High 1Low 50 2475
> ...
> 36
>
> At each study, a set of fungicides are the treatments including a
> Check (different combinations across the studies, that´s why I adopted
> network MA), "rep" are the blocks, "sev" is the disease (%) and
> "yield" is the grain mass.
>
> The moderator variables are study-specific characteristics, like disease
> pressure (Mod_A) or Yield potential (Mod_B)
>
> I have two objectives:
>
> 1° estimate the intercept and slope of the relationship yield ~ sev and
> test the inclusion of moderator variables (I´m not testing the effect of
> the treatments in this case, I´m interested on the trends of yield ~ sev).
>
> I started using a multivariate Two-Stage Analysis approach then,
> following the tutorial (
> http://www.metafor-project.org/doku.php/tips:two_stage_analy
> sis#mixed-effects_model_approach)
>
> I moved into a multi-level Mixed-Effects Models with very similiar
> results (but much more time-efficiency)
>
> I am trying this:
>
> # Overall random intercept and slopes
> m1 <- lmer(yield ~ sev + (sev|study), data=df)
>
> # Including effect of moderators on the intercept and slopes
> m2 <- lmer(yield ~ sev * mod_A+ (sev|study), data=df)
>
> # Including effect of moderator A on the intercept
> m3 <- lmer(yield ~ sev + mod_A+ (sev|study), data=df)
>
> # Including effect of moderator A on the slope
> m4 <- lmer(yield ~ sev : mod_A+ (sev|study), data=df)
>
> # Including effect of moderator A on the slope and moderator B on the
> intercept
> m5 <- lmer(yield ~ sev : mod_A + mod_B + (sev|study), data=longs)
>
> Question: do I need to include the moderator variables in random effects?
> Is it enough to use the AIC to test the goodness of fit of the models and
> likelihood ratio of them to select the best model?
>
> ===============================
>
> 2° Then I do wanted to test the effect of treatments on yield, considering
> mean differences to the untreated checks within each study.
> So I performed a network meta-analysis, agreggating the data and estimating
> the Mean Square Error from each study ANOVA:
>
> Aggregated data:
>
> study fungic yield_m Mod_A Mod_B MSE
> 1 Check 2640 2_High 1_Low 88931.95
> 1 Trt_A 2733 2_High 1_Low 88931.95
> 1 Trt_B 2858 2_High 1_Low 88931.95
> ...
>
> where yield_m is the within-study treatment mean and MSE is the
> within-study mean square error from ANOVA
>
> The model I tried is:
>
> net_D <- rma.mv(yield_m, vi2,
> mods = ~ fungic * Mod_A,
> random = ~ fungic | study,
> struct= "UN", method="ML",
> data= df,
> control = list(optimizer="nlm"))
>
> anova(net_D, btt=9:14) # to test the effect of moderators
>
> where vi2: vi = MSE / bk #Sampling variance for yi (bk = 4)
>
> My concern is if Am I going well with this model? or should I try to use
> the raw data as well, considering the block effect?
>
> Thanks for your help!
>
> Juan Edwards
>
> (Phd candidate at Plant disease epidemiology lab in Univ. Sao Paulo -
> Brazil)
>
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