rnmamod (version 0.5.1)

gower_distance: Weighted Gower's dissimilarity measure (Trials' comparability for transitivity evaluation)

Description

gower_distance calculate the weighted Gower's dissimilarity coefficient for all pairs of trials included in a network of interventions, considering several characteristics measured at trial level (Spineli et al., 2025). It takes values from 0 to 1, with 0 implying complete similarity and 1 complete dissimilarity.

Usage

gower_distance(input, weight)

Value

gower_distance returns the following list of elements:

Dissimilarity_table

A lower off-diagonal matrix of 'dist' class with the dissimilarities of all pairs of trials.

Types_used

A data-frame with type mode (i.e., double or integer) of each characteristic.

Total_missing

The percentage of missing cases in the comparison, calculated as the ratio of total missing cases to the product of the number of studies with the number of characteristics.

Arguments

input

A data-frame in the long arm-based format. Two-arm trials occupy one row in the data-frame. Multi-arm trials occupy as many rows as the number of possible comparisons among the interventions. The first two columns refer to the trial name, and the pairwise comparison, respectively. The remaining columns refer to summary characteristics. See 'Details' for the specification of the columns.

weight

A vector of non-negative numbers to define the weight contribution of each characteristic. The default is a vector of 1s for all characteristics.

Author

Loukia M. Spineli

Details

The correct type mode of columns in input must be ensured to use the function gower_distance. The first two columns referring to the trial name, and pairwise comparison, respectively, must be character. The remaining columns referring to the characteristics must be double or integer depending on whether the corresponding characteristic refers to a quantitative or qualitative variable. The type mode of each column is assessed by gower_distance using the base function typeof. Note that gower_distance invites unordered and ordered variables; for the latter, add the argument ordered = TRUE in the base function factor().

gower_distance is integrated in the function comp_clustering.

References

Gower J. General Coefficient of Similarity and Some of Its Properties. Biometrics 1971;27(4):857--71. doi: 10.2307/2528823

Spineli LM, Papadimitropoulou K, Kalyvas C. Exploring the Transitivity Assumption in Network Meta-Analysis: A Novel Approach and Its Implications. Stat Med 2025;44(7):e70068. doi: 10.1002/sim.70068.

See Also

comp_clustering

Examples

# Fictional dataset
data_set <- data.frame(Trial_name = as.character(1:7),
                      comp = c("1vs2", "1vs2", "1vs2", "1vs3", "1vs3", "2vs3", "2vs3"),
                      sample = c(140, 145, 150, 40, 45, 75, 80),
                      age = c(18, 18, 18, 48, 48, 35, 35),
                      blinding = as.integer(c("yes", "yes", "yes", "no", "no", "no", "no")))

# Calculate the weighted Gower dissimilarity of all study pairs in the network
gower_distance(input = data_set)