library(metapsyData)
library(metapsyTools)
library(tidyverse)

data.v2 = getData("depression-psiloctr", version="25.0.2")
data = getData("depression-psiloctr", version="25.0.4")
dat = data$data
dat$full_ref = data.v2$data$full_ref

list(
  dat = dat %>%
  dplyr::rename("d1" = "rob_rand", "d2" = "rob_dev", "d3" = "rob_miss",
                "d4" = "rob_meas", "d5" = "rob_sel") %>%
  filterPoolingData(
    primary_instrument == 1,
    primary_timepoint == 1,
    outcome_type == "msd" | outcome_type == "imsd",
    !(Detect(study, "Goodwin, 2022") & (!is.na(multi_arm1)) & Detect(multi_arm1, "10 mg")),
    !(Detect(study, "Goodwin, 2022") & (!is.na(multi_arm2)) & Detect(multi_arm2, "10 mg")),
    !(Detect(study, "Krempien, 2023") & (!is.na(multi_arm1)) & Detect(multi_arm1, "12 mg")),
    !(Detect(study, "Krempien, 2023") & (!is.na(multi_arm2)) & Detect(multi_arm2, "12 mg"))) %>%
    runMetaAnalysis("combined", which.combine="studies") %>%
    {.$model.combined$data} %>% {.$.g = .$.g*-1;.},
  metadata = data$returnMetadata()
) -> PsiloDB

# Define primary analysis filter
PsiloDB$dat %>% {
  (is.na(.$post_crossover) | !str_detect(.$post_crossover, "1")) &
    (!str_detect(.$study, "Krempien 2023")) &
    (!str_detect(.$study, "Carhart-Harris 2021"))
} -> PsiloDB$dat$primary

save(PsiloDB, file="www/data/PsiloDB.rda")