In fmmattioni/metabolic: Datasets and Functions for Reproducing Meta-Analyses

knitr::opts_chunk$set(
  echo = FALSE, 
  message = FALSE, 
  warning = FALSE, 
  cache = FALSE, 
  fig.width = 12, 
  fig.align = "center"
)
options(width = 999)

endpoint <- params$endpoint

forest_height_overall <- switch (
  endpoint,
  "VO2max" = 12,
  "Flow-mediated Dilation" = 4,
  "BMI" = 8,
  "Body Mass" = 11,
  "Body Fat" = 8,
  "Systolic Blood Pressure" = 8,
  "Diastolic Blood Pressure" = 8,
  "HDL" = 8,
  "LDL" = 8,
  "Triglycerides" = 8,
  "Total Cholesterol" = 8,
  "C-reactive Protein" = 4,
  "Fasting Insulin" = 6,
  "Fasting Glucose" = 8,
  "HbA1c" = 6,
  "HOMA-IR" = 6
)

library(metabolic)

Mattioni Maturana, Felipe, Martus, Peter, Zipfel, Stephan, Nieß, AM (2020). “Effectiveness of HIIE versus MICT in Improving Cardiometabolic Risk Factors in Health and Disease: a meta-anaylsis.” Medicine & Science in Sports & Exercise, Published Ahead of Print. doi: 10.1249/MSS.0000000000002506

## `r endpoint` meta-analysis

Overall {.tabset}

Forest plot

results <- perform_meta(endpoint = endpoint)

results$meta_analysis$Overall %>% 
  plot_metabolic()

R output

results$meta_analysis$Overall

Sensitivity analysis {.tabset}

Forest plot

results$sensitivity_analysis$Overall %>% 
  plot_metabolic()

R output

results$sensitivity_analysis$Overall

Small-study effects

results$meta_analysis$Overall %>% 
  plot_small_study_effects()

Subgroups Overview {.tabset}

Forest plot

results_bind <- perform_bind(results$meta_analysis)

results_bind %>% 
  plot_metabolic()

R output

results_bind

out <- results %>% 
  dplyr::filter(subgroup != "Overall") %>% 
  dplyr::mutate(
    endpoint = endpoint,
    output = purrr::map2(endpoint, subgroup, 
                         ~ knitr::knit_expand(file =  system.file("endpoint-report-level-2.Rmd", 
                                                                  package = "metabolic"),
                                              endpoint = .x,
                                              subgroup = .y))
  ) %>% 
  dplyr::select(output) %>% 
  tibble::deframe()