[R-meta] Pre-test Post-test Control design Different N
Hi Michael, In fact, there are different situations: - For some studies, the same plots are sampled once before and once after the treatment so they are paired, and pre-Test nT = post-Test nT. - For some studies, the same plots are sampled twice before and once after the treatment so they are paired, but as I group pre-Test data with Cochrane formula, pre-Test nT = 2 * post-Test nT. - For some studies, x plots are sampled pre-treatment and y (x and y different) plots are sampled post-treatment so pre-Test nT and post-Test nT are different; either some plots are common between pre-treatment and post-treatment (so they are paired) and some are only sampled once (not paired), or plots are completely different between pre and post-treatment sampling (not paired). I agree that I don't really have a paired design, as it depends on the studies (and on the plots within studies), but I couldn't find any other formula than Morris' to calculate an effect size for Pre-Test Post-Test Control design. Because I suppose that even if the plots are not paired, I can't consider pre-test and post-test data as independent, am I wrong? So that is why I was wondering if it was possible to adapt the formula for a non-paired design. But if you are aware of such formulas (for non-paired Pre-Test Post-Test Control design), I'm interested in it! Regards, Marianne ----- Mail original ----- De: "Michael Dewey" <lists at dewey.myzen.co.uk> ?: "Marianne DEBUE" <marianne.debue at mnhn.fr>, "r-sig-meta-analysis" <r-sig-meta-analysis at r-project.org> Envoy?: Mercredi 13 Janvier 2021 15:28:41 Objet: Re: [R-meta] Pre-test Post-test Control design Different N Dear Marianne Perhaps you could clarify something for us. You state that you have a pre-post design and you have used methods for paired data. You then go on to describe situations in which the number of units before and after is not the same. Perhaps it is just me but I do not understand how you can then have pairing. Michael
On 13/01/2021 08:04, Marianne DEBUE wrote:
Hi, I'm conducting a meta-analysis in ecology on a Pre-test Post-test Control design. I'd like to use Morris "dppc2" formula (in Estimating effect sizes from pretest-posttest-control group designs , [ https://doi.org/10.1177%2F1094428106291059 | https://doi.org/10.1177/1094428106291059 ] ) in order to take into account the non-independency of the pre-test post-test design. This formula applies for paired-observations and depends on the Pre-test Control Mean, Post-test Control Mean, Pre-test Treatment Mean, Post-test Treatment Mean, Pre-Test Control SD, Pre-test Treatment SD, Treatment Sample size nT and Control Sample size nC. Some studies have the same pre-test and post-test nT (and nC) because they always sample the same plots. However, so me studies have a different Pre-Test nT and Post-Test nT (and/or pre-Test nC and Post-Test nC), either because of the experimental design of the author of the study (for example 30 Before samples and 60 After samples), or because we have gathered the Before data if they were given for several years using the Cochrane combined group formula ( [ https://handbook-5-1.cochrane.org/chapter_7/table_7_7_a_formulae_for_combining_groups.htm | https://handbook-5-1.cochrane.org/chapter_7/table_7_7_a_formulae_for_combining_groups.htm ] ) (for example, 30 samples taken 2 years before the intervention, 30 samples taken one year before the intervention, and 30 samples taken one year after the intervention, giving 60 samples taken before the intervention and 30 taken after). Do you know if it is possible to adapt the formula to take into account a possible difference in nT or nC between the Before and After? Best regards, Marianne [[alternative HTML version deleted]]
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