Calculates the robustness index, a novel index that quantifies the overall divergence of the sensitivity analysis results from the primary analysis results. The robustness index considers objective decision rules to infer the presence or lack of robustness of the primary analysis results when conducting a sensitivity analysis (Spineli et al., 2021).
robustness_index(sens, threshold)robustness_index prints on the R console a message in green
text on the threshold of robustness determined by the user.
Then, the function returns the following list of elements:
A numeric scalar or vector on the robustness
index values. In the case of a pairwise meta-analysis,
robust_index is scalar as only one summary effect size is obtained.
In the case of network meta-analysis, robust_index is a vector with
length equal to the number of possible pairwise comparisons;
one robustness index per possible comparison.
A character or character vector (of same length with
robust_index) on whether the primary analysis results are
robust or frail to the different re-analyses.
A vector or matrix on the Kullback-Leibler divergence
(KLD) measure in the summary effect size from a subsequent re-analysis to
the primary analysis. In the case of a pairwise meta-analysis, kld
is a vector with length equal to the number of total analyses (one KLD
value is obtained per analysis). The number of total analyses equals the
square of the number of scenarios indicated in the argument
mean_scenarios of run_sensitivity, in the case of
missing participant outcome data; otherwise, the length of the character
vector in argument sens.
In the case of network meta-analysis, robust_index is a matrix with
number of rows equal to the number of total analyses and number of columns
equal to the number of possible pairwise comparisons; one KLD value per
analysis and possible comparison.
The threshold used to be inherited by the
heatmap_robustness function.
The scenarios considered to be inherited by the
kld_barplot function.
An object of S3 class run_sensitivity when
sensitivity analysis refers to different scenarios about the average
missingness parameter. See 'Value' in run_sensitivity. For a
general sensitivity analysis, insert a list of at least two objects
of S3 class run_model indicating different re-analyses: the
first object (of class run_model) in the list should refer to
the primary analysis.
A number indicating the threshold of robustness, that is, the minimally allowed deviation between the primary analysis and re-analysis results. See 'Details' below.
Loukia M. Spineli
Thresholds of robustness have been proposed only for the odds ratio
and standardised mean difference (Spineli et al., 2021).
The user may consider the values 0.28 and 0.17 in the argument
threshold for the odds ratio and standardised mean difference effect
measures (the default values), respectively, or consider other plausible
values. When the argument threshold has not been defined,
robustness_index considers the default values 0.28 and 0.17 as
threshold for robustness for binary and continuous outcome, respectively,
regardless of the effect measure (the default thresholds may not be proper
choices for other effect measures; hence, use these threshold with great
caution in this case). Spineli et al. (2021) offers a discussion on
specifying the threshold of robustness.
In the case of binary outcome, robustness_index considers the
results in the odds ratio scale to calculate the robustness index.
This is because, the odds ratio is used as the 'best-case' effect measure
in run_model. Then, relative risk, and risk difference are
functions of the odds ratio and the selected baseline risk (See 'Details'
in run_model).
In the case of missing participant outcome data, the primary analysis is
considered to be the middle of the numbers in the argument
mean_scenarios of run_sensitivity (see 'Arguments'
and 'Details' in run_sensitivity).
In robust, the value "robust" appears when
robust_index is less than threshold; otherwise, the value
"frail" appears.
In the case of missing participant outcome data, robustness_index
can be used only when missing participant outcome data have been
extracted for at least one trial. Otherwise, the execution of the function
will be stopped and an error message will be printed in the R console.
Kullback S, Leibler RA. On information and sufficiency. Ann Math Stat 1951;22(1):79--86. doi: 10.1214/aoms/1177729694
Spineli LM, Kalyvas C, Papadimitropoulou K. Quantifying the robustness of primary analysis results: A case study on missing outcome data in pairwise and network meta-analysis. Res Synth Methods 2021;12(4):475--90. doi: 10.1002/jrsm.1478
heatmap_robustness, kld_barplot,
run_model, run_sensitivity
data("nma.baker2009")
# Read results from 'run_sensitivity' (using the default arguments)
res_sens <- readRDS(system.file('extdata/res_sens_baker.rds',
package = 'rnmamod'))
# Calculate the robustness index
robustness_index(sens = res_sens,
threshold = 0.28)
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