[R-meta] network meta-analysis - include block (within-study) level
Sorry for that Wolfgang, y = grain yield at plot (single value). Actually, plots were ~ 15m?, however the grain weight was expressed in kg/10000 m? (1ha). ? ?? x = is the leaf crop area damaged by a fungal disease (%) Both are quantitative positive variables, single-point assessments, and each row has the same plot values (block/treatment) We expect that "x" has a negative effect on y. So we have interest on the intercept (soybean yield in abscense of the disease) and how much yield it is reduced by a unit increment of x. W?e also want to test the effect of moderators of y~x. I hope I have clarified your doubts. Best, Juan Edwards On Tue, Aug 8, 2017 at 7:34 PM, Viechtbauer Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
So is 'y' is the mean treatment yield here? Also, is that really the average of multiple measurements (e.g., if there is subsampling)? Or is 'y' just the single measurement (yield) for that particular block and treatment? I still do not quite understand what kind of data you have. Also, what is 'x'? Best, Wolfgang -----Original Message----- From: Juan Pablo Edwards Molina [mailto:edwardsmolina at gmail.com] Sent: Tuesday, August 08, 2017 23:26 To: Viechtbauer Wolfgang (SP) Cc: r-sig-meta-analysis at r-project.org Subject: Re: [R-meta] network meta-analysis - include block (within-study) level Pretty close to that structure ?you say?: I have ?several treatments at each block (balanced experiments), actually different set of treatments across the k-trials (all trials have the untreated Check) This are a few lines of trial 3: ? trt trial bk x y Check 3 1 40 2493 Check 3 2 45 2173 Check 3 3 40 2628 Check 3 4 40 2168 Fox 3 1 35 3194 Fox 3 2 30 2363 Fox 3 3 35 2887 Fox 3 4 30 3278 NTX 3 1 40 2988 NTX 3 2 35 2361 NTX 3 3 35 2341 NTX 3 4 35 3218 ? |? Also, do you have the raw mean and variance (or SD) and sample size for each row of the dataset? It seems like you are first fitting some kind of ANOVA within each study, but | that might actually complicate things. Yes, I have the raw full dataset so I ?have the observation level ?values to calculate SD, means..? Several authors from the Phytopathology area use ANOVA MSE : "...The within-study variance (V) for IND or DON for these fungicide trials is the residual variance (mean square error) from an analysis of variance (ANOVA) of the effects of treatment on disease or toxin. Where the original data were available, this variance was calculated directly from an ANOVA..." http://apsjournals.apsnet.org/doi/abs/10.1094/PHYTO-97-2-0211 Juan On Tue, Aug 8, 2017 at 6:03 PM, Viechtbauer Wolfgang (SP) < wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: Dear Juan, Could you show a bit of the data (structure)? In particular, does each block contain two treatments, so that the structure looks something like this? trial block treatment mean -------------------------- 1 1 1 ... 1 1 2 ... 1 2 1 ... 1 2 2 ... 2 1 1 ... 2 1 2 ... 2 2 1 ... 2 2 2 ... 2 3 1 ... 2 3 2 ... ... ?? Also, do you have the raw mean and variance (or SD) and sample size for each row of the dataset? It seems like you are first fitting some kind of ANOVA within each study, but that might actually complicate things. Best, Wolfgang -----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 08, 2017 22:09 To: r-sig-meta-analysis at r-project.org Subject: [R-meta] network meta-analysis - include block (within-study) level Dear list, I have a dataset containing crop field randomized block design experiments with observations at plot level (experimental unit), and I want to estimate the treatments grain yield difference relative to a untreated check. net1 <- rma.mv(yield, vi2, mods = ~ treatment, random = ~ treatment| trial, method="ML", struct="UN", data=df) where yield is the vector of mean treatments yield for vi2 is the vector of sampling variances obtained by: vi2 <- V_yield/n (for each trial) (V_yield = MSE from anova) Do I need to include the block in the model? or using the experiment treatments means will obtain the same results? I suppose something like: net2 <- rma.mv(yield, vi2, mods = ~ treatment, random = ~ treatment| block| trial, method="ML", struct="UN", data=df) If the latter would be a better approach, how do I include the sampling variance? Thanks in advance, Juan Edwards