Message-ID: <CAF5W3aQJJn1ZVveTee+b52+L5Ck+YHuPpcnY9Egcj+BTezev5Q@mail.gmail.com>
Date: 2017-08-29T14:05:54Z
From: Juan Pablo Edwards Molina
Subject: [R-meta] Intercept-slope model & network meta-analysis
In-Reply-To: <6d2b439c15324225858ba2608ace2baa@UM-MAIL3216.unimaas.nl>
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 fungic?ides 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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