| Title: | Integrate Single-Arm Observational Data in Network Meta Analysis |
|---|---|
| Description: | This package Calculates distance between single-arm observational studies by using co-variates information to remove heterogeneity in Network Meta-Analysis in Randomized clinical trials. This package will facilitate the inclusion of observational data in Network Meta ANalysis, thereby enhancing the comprehensiveness and robustness of comparative effectiveness research.The proposed R package will provide researchers with a powerful tool to systematically adjust for heterogeneity in single-arm observational studies, enabling their integration into NMAs. This will improve the accuracy and reliability of comparative effectiveness research, ultimately informing better healthcare decisions. Schmitz S et. al. The use of single armed observational data to closing the gap in otherwise disconnected evidence networks: a network meta-analysis in multiple myeloma. BMC Med Res Methodol. 2018 Jun 28;18(1):66. doi: 10.1186/s12874-018-0509-7. PMID: 29954322; PMCID: PMC6022299. |
| Authors: | Supreet Kaur [ctb], Akanksha Sharma [ctb], Shubhram Pandey [aut, cre] |
| Maintainer: | Shubhram Pandey <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.1.0 |
| Built: | 2025-01-19 05:00:56 UTC |
| Source: | https://github.com/heorlytics/closeloop |
Title To calculate distance between two studies using covariate information
calc_dist(df, col_names, Study = "Study", Treat = "Treatment", weights, digits)calc_dist(df, col_names, Study = "Study", Treat = "Treatment", weights, digits)
df |
A data frame consists of columns namely "Study", "Treatment", and at least one covariate. |
col_names |
A vector of column names specifying covariate names. |
Study |
A column name in a data frame named as "Study" specifying study names. |
Treat |
A column name in a data frame named as "Treatment" specifying treatment names. |
weights |
A variable in which the results of specify_weight() function was stored. |
digits |
A numeric value indicating the number of decimal places in the Distance calculated. |
Data frame
Shubhram Pandey [email protected]
attach(exampleData) var = c("Male","Age") weights = specify_weight(var, weights = c(0.5,0.5)) weights dist = calc_dist(df = exampleData, col_names = var, weights = weights,digits = 4) distattach(exampleData) var = c("Male","Age") weights = specify_weight(var, weights = c(0.5,0.5)) weights dist = calc_dist(df = exampleData, col_names = var, weights = weights,digits = 4) dist
Function to check if all values are numeric in data
check_data(df, col_names = NULL)check_data(df, col_names = NULL)
df |
A data frame contains columns that represent covariates |
col_names |
A numeric vector of covariates that can be binary or continuous |
logical
Shubhram Pandey [email protected]
attach(exampleData) var = c("Age","Male") x = check_data(df = exampleData, col_names = var) xattach(exampleData) var = c("Age","Male") x = check_data(df = exampleData, col_names = var) x
Data were extracted from the studies included.
exampleDataexampleData
A data frame with with the 4 following variables (columns).
This character vector represents number of the study.
This vector represents the proportion of males.
This vector represents the average age in each study.
This vector represents the treatment.
...
A simulated data were created to run examples.
Shubhram Pandey [email protected]
Function to check if columns are proportions
is_prop(df, col_names)is_prop(df, col_names)
df |
a data frame to be checked |
col_names |
column names to be checked |
list
Shubhram Pandey [email protected]
#' attach(exampleData) result <- is_prop(exampleData,c("Male","Age")) result#' attach(exampleData) result <- is_prop(exampleData,c("Male","Age")) result
Title specify_weight
specify_weight(var, weights)specify_weight(var, weights)
var |
Variables for which weights can be assigned |
weights |
weights in same sequence as variables |
list
Shubhram Pandey [email protected]
var = c("Male","Age") weights = specify_weight(var, weights = c(0.5,0.5)) weightsvar = c("Male","Age") weights = specify_weight(var, weights = c(0.5,0.5)) weights