Package 'metabolic' reference manual

Title:	Datasets and Functions for Reproducing Meta-Analyses
Description:	Dataset and functions from the meta-analysis published in Medicine & Science in Sports & Exercise. It contains all the data and functions to reproduce the analysis. "Effectiveness of HIIE versus MICT in Improving Cardiometabolic Risk Factors in Health and Disease: A Meta-analysis". Felipe Mattioni Maturana, Peter Martus, Stephan Zipfel, Andreas M Nieß (2020) <doi:10.1249/MSS.0000000000002506>.
Authors:	Felipe Mattioni Maturana [aut, cre, cph]
Maintainer:	Felipe Mattioni Maturana <[email protected]>
License:	CC0
Version:	0.1.2
Built:	2024-11-03 03:21:23 UTC
Source:	https://github.com/fmmattioni/metabolic

Build a GOfER diagram (Graphical Overview for Evidence Reviews)

Description

It is recommended to set save = TRUE and indicate the path to save to, as the plot is not going to look good in the Plots panel.

Usage

build_gofer(page = c("1", "2"), save = FALSE, path, format = ".png")
build_gofer(page = c("1", "2"), save = FALSE, path, format = ".png")

Arguments

`page`	A text string to indicate the page you would like to display. This GOfER has two pages (28 studies in page 1 and 28 studies in page 2).
`save`	A boolean to indicate whether to save the plot to disk.
`path`	Path to write to. It has to be a character string indicating the path and file name (without the extension). For example, `⁠~/Documents/metabolic_gofer_page_1⁠` will save `metabolic_gofer_page_1.png` to the `Documents` folder.
`format`	The file extension that you want to save the plot to. Only `.png`, is supported.

Value

A patchwork object

Examples

if (interactive()) {
 build_gofer(page = "1", save = TRUE, path = tempfile())
}
if (interactive()) {
 build_gofer(page = "1", save = TRUE, path = tempfile())
}

Build HTML report

Description

Build an HTML report with all the results from the chosen clinical endpoint

Usage

build_report(
  endpoint = c("VO2max", "Flow-mediated Dilation", "BMI", "Body Mass", "Body Fat",
    "Systolic Blood Pressure", "Diastolic Blood Pressure", "HDL", "LDL", "Triglycerides",
    "Total Cholesterol", "C-reactive Protein", "Fasting Insulin", "Fasting Glucose",
    "HbA1c", "HOMA-IR"),
  path,
  format = ".html"
)
build_report(
  endpoint = c("VO2max", "Flow-mediated Dilation", "BMI", "Body Mass", "Body Fat",
    "Systolic Blood Pressure", "Diastolic Blood Pressure", "HDL", "LDL", "Triglycerides",
    "Total Cholesterol", "C-reactive Protein", "Fasting Insulin", "Fasting Glucose",
    "HbA1c", "HOMA-IR"),
  path,
  format = ".html"
)

Arguments

`endpoint`	The clinical endpoint to build the HTML report.
`path`	Path to write to. It has to be a character string indicating the path and file name (without the extension). For example, `⁠~/Documents/metabolic_report⁠` will save `metabolic_report.html` to the `Documents` folder.
`format`	The file extension that you want to build the report with. Only `.html`, is supported.

Value

an HTML file.

Examples

if(interactive()) {
# Build an HTML report on VO2max
build_report(endpoint = "VO2max", path = tempfile())
}
if(interactive()) {
# Build an HTML report on VO2max
build_report(endpoint = "VO2max", path = tempfile())
}

Dataset for building a GOfER diagram (Graphical Overview for Evidence Reviews)

Description

A dataset containing the summary of the studies included in the meta-analysis. This dataset is used to build a GOfER with 'ggplot2' and 'patchwork'.

Usage

metabolic_gofer
metabolic_gofer

Format

A data frame with 115 rows and 33 variables:

study: last name of first author and year of publication
groups: group allocated in the study, it may be either: HIIT (High-intensity Interval Training), SIT (Sprint Interval Training), or MICT (Moderate-intensity Continuous Training)
sample_population: population category from the study, it may be either: Healthy, Overweight/obese, Cardiac Rehabilitation, Metabolic Syndrome, or T2D (Type-2 Diabetes)
sample_fitness: the general fitness condition of the sample reported in the study, it may be either: Active, Sedentary, or N/R (Not Reported)
sample_men_ratio: the men ratio (total men divided by sample size) reported in the study
anamnese_smoker: information whether participants in the sample were smokers, it may either: Y (Yes), N (No), or N/R (Not Reported)
anamnese_medicines_to_control_BP: information whether participants in the sample were taking regular medication to control blood pressure, it may either: Y (Yes), N (No), or N/R (Not Reported)
age: the age of each group reported in the study, in years
design_type_of_exercise: the type of exercise used for exercise training, it may be either running or cycling
design_sample_size: the sample size of each group in the study
design_training_duration: the training duration, in weeks
design_training_frequency: the training frequency for each group used in the study
design_exercise_intensity: the prescribed exercise intensity for each group
hiie_n_reps: number of repetitions prescribed for the HIIE (High-intensity Interval Exercise) protocol
hiie_rep_duration: length of repetitions prescribed for the HIIE (High-intensity Interval Exercise) protocol
hiie_work_rest_ratio: the work-rest ratio in the HIIE (High-intensity Interval Exercise) protocol
compliance: compliance reported in each group and study
endpoints_vo2max: information on whether the reported p-value was singificant comparing the effects pre- and post-training on VO2max (maximal oxygen uptake). If Yes, the reported p-value was less than 0.05; if No, the reported p-value was greater than 0.05
endpoints_fmd: information on whether the reported p-value was singificant comparing the effects pre- and post-training on Flow-mediated Dilation. If Yes, the reported p-value was less than 0.05; if No, the reported p-value was greater than 0.05
endpoints_body_mass: information on whether the reported p-value was singificant comparing the effects pre- and post-training on Body Mass. If Yes, the reported p-value was less than 0.05; if No, the reported p-value was greater than 0.05
endpoints_body_fat: information on whether the reported p-value was singificant comparing the effects pre- and post-training on Body Fat. If Yes, the reported p-value was less than 0.05; if No, the reported p-value was greater than 0.05
endpoints_sbp: information on whether the reported p-value was singificant comparing the effects pre- and post-training on Systolic Blood Pressure. If Yes, the reported p-value was less than 0.05; if No, the reported p-value was greater than 0.05
endpoints_dbp: information on whether the reported p-value was singificant comparing the effects pre- and post-training on Diastolic Blood Pressure. If Yes, the reported p-value was less than 0.05; if No, the reported p-value was greater than 0.05
endpoints_hdl: information on whether the reported p-value was singificant comparing the effects pre- and post-training on HDL. If Yes, the reported p-value was less than 0.05; if No, the reported p-value was greater than 0.05
endpoints_ldl: information on whether the reported p-value was singificant comparing the effects pre- and post-training on LDL. If Yes, the reported p-value was less than 0.05; if No, the reported p-value was greater than 0.05
endpoints_triglycerides: information on whether the reported p-value was singificant comparing the effects pre- and post-training on Triglycerides. If Yes, the reported p-value was less than 0.05; if No, the reported p-value was greater than 0.05
endpoints_total_cholesterol: information on whether the reported p-value was singificant comparing the effects pre- and post-training on Total Cholesterol. If Yes, the reported p-value was less than 0.05; if No, the reported p-value was greater than 0.05
endpoints_insulin: information on whether the reported p-value was singificant comparing the effects pre- and post-training on Fasting Insulin. If Yes, the reported p-value was less than 0.05; if No, the reported p-value was greater than 0.05
endpoints_glucose: information on whether the reported p-value was singificant comparing the effects pre- and post-training on Fasting Glucose. If Yes, the reported p-value was less than 0.05; if No, the reported p-value was greater than 0.05
endpoints_homa: information on whether the reported p-value was singificant comparing the effects pre- and post-training on HOMA-IR (insulin resistance). If Yes, the reported p-value was less than 0.05; if No, the reported p-value was greater than 0.05
endpoints_bmi: information on whether the reported p-value was singificant comparing the effects pre- and post-training on BMI (body mass index). If Yes, the reported p-value was less than 0.05; if No, the reported p-value was greater than 0.05
endpoints_crp: information on whether the reported p-value was singificant comparing the effects pre- and post-training on C-reactive Protein. If Yes, the reported p-value was less than 0.05; if No, the reported p-value was greater than 0.05
endpoints_hba1c: information on whether the reported p-value was singificant comparing the effects pre- and post-training on HbA1c (glycated hemoglobin). If Yes, the reported p-value was less than 0.05; if No, the reported p-value was greater than 0.05

Source

https://journals.lww.com/acsm-msse/Fulltext/2021/03000/Effectiveness_of_HIIE_versus_MICT_in_Improving.12.aspx

Dataset for reproducing the meta-analysis

Description

A dataset containing the processed data from the studies necessary to reproduce the meta-analysis.

Usage

metabolic_meta
metabolic_meta

Format

A data frame with 391 rows and 21 variables:

study: last name of first author and year of publication
endpoint: the clinical endpoint analyzed, it may be either: VO2max (maximal oxygen uptake), Flow-mediated Dilation, BMI (body mass index), Body Mass, Body Fat, Systolic Blood Pressure, Diastolic Blood Pressure, HDL, LDL, Triglycerides, Total Cholesterol, C-reactive Protein, Fasting Insulin, Fasting Glucose, HbA1c (glycated hemoglobin), or HOMA-IR (insulin resistance)
population: population category from the study, it may be either: Healthy, Overweight/obese, Cardiac Rehabilitation, Metabolic Syndrome, or T2D
age: the median age between the groups in the study, in years
category_age: age category based on the age column, it may be either: < 30 y, 30 - 50 y, or > 50 y
duration: the training duration, in weeks
category_duration: training duration category based on the duration column, it may be either: < 5 weeks, 5 - 10 weeks, or > 10 weeks
men_ratio: the men ratio (total men divided by sample size) reported in the study
category_men_ratio: men ratio category based on the men_ratio column, it may be either: < 0.5 or > 0.5
type_exercise: the type of exercise used for exercise training, it may be either running or cycling
bsln: the baseline value reported for the clinical endpoint (the median between groups is used)
bsln_adjusted: the adjusted baseline value for the clinical endpoint. Values were adjusted according to their categories described in the paper. For example, VO2max values were adjusted to their age and sex percentile ranks, etc. From these values, the categories are defined in 'category_bsln'
category_bsln: the baseline category based on the bsln column
N_HIIE: sample size of the HIIE (High-intensity Interval Exercise) group
Mean_HIIE: mean difference between pre- and post-training in the HIIE (High-intensity Interval Exercise) group
SD_HIIE: standard deviation of the difference between pre- and post-training in the HIIE (High-intensity Interval Exercise) group
N_MICT: sample size of the MICT (Moderate-intensity Continuous Training) group
Mean_MICT: mean difference between pre- and post-training in the MICT (Moderate-intensity Continuous Training) group
SD_MICT: standard deviation of the difference between pre- and post-training in the MICT (Moderate-intensity Continuous Training) group
HIIE: the type of HIIE used in the study: it may be either: HIIT (High-intensity Interval Training) or SIT (Sprint Interval Training)
desired_effect: the desired effect expected for post-training improvements. This is needed simply to display the effects related to HIIE and MICT on the same side of the forest plot throughout the clinical endpoints

Source

https://journals.lww.com/acsm-msse/Fulltext/2021/03000/Effectiveness_of_HIIE_versus_MICT_in_Improving.12.aspx

Combine the subgroup meta-analyses

Description

Combine the subgroup meta-analyses to ...

Usage

perform_bind(x)
perform_bind(x)

Arguments

`x`	An object retrieved from perform_meta.

Value

a tibble with named lists.

Examples

if (interactive()) {
# Perform meta-analysis on VO2max
results <- perform_meta(endpoint = "VO2max")
results

# Combine Overall and Subgroups meta-analysis results
results_bind <- perform_bind(results$meta_analysis)
results_bind
}
if (interactive()) {
# Perform meta-analysis on VO2max
results <- perform_meta(endpoint = "VO2max")
results

# Combine Overall and Subgroups meta-analysis results
results_bind <- perform_bind(results$meta_analysis)
results_bind
}

Perform meta-analysis

Description

Perform the meta-analysis, sensitivity analysis, and meta-regression on the chosen clinical endpoint.

Usage

perform_meta(
  endpoint = c("VO2max", "Flow-mediated Dilation", "BMI", "Body Mass", "Body Fat",
    "Systolic Blood Pressure", "Diastolic Blood Pressure", "HDL", "LDL", "Triglycerides",
    "Total Cholesterol", "C-reactive Protein", "Fasting Insulin", "Fasting Glucose",
    "HbA1c", "HOMA-IR")
)
perform_meta(
  endpoint = c("VO2max", "Flow-mediated Dilation", "BMI", "Body Mass", "Body Fat",
    "Systolic Blood Pressure", "Diastolic Blood Pressure", "HDL", "LDL", "Triglycerides",
    "Total Cholesterol", "C-reactive Protein", "Fasting Insulin", "Fasting Glucose",
    "HbA1c", "HOMA-IR")
)

Arguments

endpoint

The clinical endpoint to perform the meta-analysis and meta-regression.

Value

a tibble with named lists.

Examples

if (interactive()) {
# Perform meta-analysis on VO2max
results <- perform_meta(endpoint = "VO2max")
results

# Access results of Overall meta-analysis
results$meta_analysis$Overall

# Acess results of Age meta-regression
results$meta_regression$Age
}
if (interactive()) {
# Perform meta-analysis on VO2max
results <- perform_meta(endpoint = "VO2max")
results

# Access results of Overall meta-analysis
results$meta_analysis$Overall

# Acess results of Age meta-regression
results$meta_regression$Age
}

Plot results

Description

Plot results from the perform_meta() and perform_bind() function. Please, see 'Details' and 'Examples'.

Usage

plot_metabolic(x, save = FALSE, path, format = ".png")
plot_metabolic(x, save = FALSE, path, format = ".png")

Arguments

`x`	an object obtained from the meta-analysis results. See 'Details'.
`save`	A boolean to indicate whether to save the plot to disk.
`path`	Path to write to. It has to be a character string indicating the path and file name (without the extension). For example, `⁠~/Documents/metabolic_gofer_page_1⁠` will save `metabolic_gofer_page_1.png` to the `Documents` folder.
`format`	The file extension that you want to save the plot to. Only `.png`, is supported.

Details

This function can be used to plot the results derived from both perform_meta() and perform_bind(). It can produce forests and bubble plots, depending on the object passed to the function.

Value

a plot.

Examples

if(interactive()) {
# Perform meta-analysis on VO2max
results <- perform_meta(endpoint = "VO2max")

# Plot Overall meta-analysis results
results$meta_analysis$Overall %>%
   plot_metabolic()

# Plot Age meta-regression results
results$meta_regression$Age %>%
   plot_metabolic()

# Plot overview of Overall and Subgroups meta-analysis results
results_bind <- perform_bind(results$meta_analysis)
results_bind %>%
   plot_metabolic()

# Plot sensitivity analysis results
results$sensitivity_analysis$Overall %>%
   plot_metabolic()
}
if(interactive()) {
# Perform meta-analysis on VO2max
results <- perform_meta(endpoint = "VO2max")

# Plot Overall meta-analysis results
results$meta_analysis$Overall %>%
   plot_metabolic()

# Plot Age meta-regression results
results$meta_regression$Age %>%
   plot_metabolic()

# Plot overview of Overall and Subgroups meta-analysis results
results_bind <- perform_bind(results$meta_analysis)
results_bind %>%
   plot_metabolic()

# Plot sensitivity analysis results
results$sensitivity_analysis$Overall %>%
   plot_metabolic()
}

Plot small-study effects analysis

Description

Plot small-study effects analysis

Usage

plot_small_study_effects(x, save = FALSE, path, format = ".png")
plot_small_study_effects(x, save = FALSE, path, format = ".png")

Arguments

`x`	an object of class meta
`save`	A boolean to indicate whether to save the plot to disk.
`path`	Path to write to. It has to be a character string indicating the path and file name (without the extension). For example, `⁠~/Documents/small_study_effects⁠` will save `small_study_effects.png` to the `Documents` folder.
`format`	The file extension that you want to save the plot to. Only `.png`, is supported.

Value

a plot.

Examples

## Not run: 
  # Perform meta-analysis on VO2max
  results <- perform_meta(endpoint = "VO2max")

  # Plot small-study effects results
  results$meta_analysis$Overall %>%
     plot_small_study_effects()

## End(Not run)
## Not run: 
  # Perform meta-analysis on VO2max
  results <- perform_meta(endpoint = "VO2max")

  # Plot small-study effects results
  results$meta_analysis$Overall %>%
     plot_small_study_effects()

## End(Not run)

Read the paper

Description

This function will open the published paper in the journal website for you to read it in your default browser.

Usage

read_paper()
read_paper()

Examples

read_paper()

read_paper()

Package 'metabolic'

Help Index

Build a GOfER diagram (Graphical Overview for Evidence Reviews)

Description

Usage

Arguments

Value

Examples

Build HTML report

Description

Usage

Arguments

Value

Examples

Dataset for building a GOfER diagram (Graphical Overview for Evidence Reviews)

Description

Usage

Format

Source

Dataset for reproducing the meta-analysis

Description

Usage

Format

Source

Combine the subgroup meta-analyses

Description

Usage

Arguments

Value

Examples

Perform meta-analysis

Description

Usage

Arguments

Value

Examples

Plot results

Description

Usage

Arguments

Details

Value

Examples

Plot small-study effects analysis

Description

Usage

Arguments

Value

Examples

Read the paper

Description

Usage

Examples