Replies: 7 comments
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For the time being, I restricted |
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There are two main challenges for dichotomous endpoints:
For (2), the worst-case scenario is that non-continuous non-identity-link conditional means priors are not possible in
``` r
suppressPackageStartupMessages({
library(brms)
library(coda)
library(emmeans)
library(mmrm)
library(posterior)
library(tidyverse)
library(zoo)
})
emm_options(sep = "|")
FEV data from the mmrm package, using LOCF and then LOCF reversedto impute responses.data(fev_data, package = "mmrm") lm + emmeansmodel <- glm( custom marginal means from lm draws using a custom mappingfrom model parameters to marginal means.proportional_factors <- model.matrix( Compare resultssummary <- summary_custom %>%
|
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We may want to focus on "unconditional treatment effects" in the sense of the recently updated FDA guideline on Adjusting for Covariates in Randomized Clinical Trials (see section III C. on "Nonlinear Models"). |
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If I understand correctly, I think we are mostly using unconditional treatment effects, i.e. regress on |
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I wonder, if we allow a non-identity link and dichotomous data, would we have to change the package name to something like |
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And would it make sense anymore to regress on baseline? |
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If we use a link function for dichotomous data, we need a distributional family for dichotomous data, such as I am favor of excluding non-Gaussian families from the package, or at least waiting until we know exactly which family or families to bring within scope. Transferring this issue thread to a discussion thread. |
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Should we extend
brms.mmrm
to support dichotomous data? I thinkbrms
straightforwardly accepts nonlinear link functions, but I am wondering how this would affect #53 and #40.Beta Was this translation helpful? Give feedback.
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