,
Guido Schwarzer [aut] | Title: | Ranking using Probabilistic Models and Treatment Choice Criteria |
|---|---|
| Description: | Implementation of a novel frequentist approach to produce clinically relevant treatment hierarchies in network meta-analysis. The method is based on treatment choice criteria (TCC) and probabilistic ranking models, as described by Evrenoglou et al. (2024) <DOI:10.48550/arXiv.2406.10612>. The TCC are defined using a rule based on the minimal clinically important difference. Using the defined TCC, the study-level data (i.e., treatment effects and standard errors) are first transformed into a preference format, indicating either a treatment preference (e.g., treatment A > treatment B) or a tie (treatment A = treatment B). The preference data are then synthesized using a probabilistic ranking model, which estimates the latent ability parameter of each treatment and produces the final treatment hierarchy. This parameter represents each treatment’s ability to outperform all the other competing treatments in the network. Consequently, larger ability estimates indicate higher positions in the ranking list. |
| Authors: | Theodoros Evrenoglou [aut, cre]
,
Guido Schwarzer [aut] |
| Maintainer: | Theodoros Evrenoglou <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1-1 |
| Built: | 2025-02-27 08:30:27 UTC |
| Source: | https://github.com/tevrenoglou/mtrank |
R package mtrank enables the production of clinically relevant treatment hierarchies in network meta-analysis using a novel frequentist approach based on treatment choice criteria (TCC) and probabilistic ranking models, as described by Evrenoglou et al. (2024). The TCC are defined using a rule based on the minimal clinically important difference. Using the defined TCC, the study-level data (i.e., treatment effects and standard errors) are first transformed into a preference format, indicating either a treatment preference (e.g., treatment A > treatment B) or a tie (treatment A = treatment B). The preference data are then synthesized using a probabilistic ranking model, which estimates the latent ability parameter of each treatment and produces the final treatment hierarchy. This parameter represents each treatment’s ability to outperform all the other competing treatments in the network. Consequently, larger ability estimates indicate higher positions in the ranking list.
The R package mtrank provides the following functions:
Function tcc defines the TCC and transforms the
study-specific relative treatment effects into a preference format.
Function mtrank synthesizes the output of the
tcc function and estimates the final treatment ability.
Forest plots are created either for the results of the
TCC (forest.tcc) or the final ability estimates
(forest.mtrank).
Function paired_pref uses the ability estimates
obtained from mtrank to calculate pairwise probabilities
that any treatment 'A' can be better, equal, or worse than any other
treatment 'B' in the network.
Type help(package = "mtrank") for a listing of R functions
available in mtrank.
Type citation("mtrank") on how to cite mtrank
in publications.
To report problems and bugs, please send an email to Theodoros Evrenoglou <[email protected]>.
The development version of mtrank is available on GitHub https://github.com/TEvrenoglou/mtrank.
Theodoros Evrenoglou <[email protected]>, Guido Schwarzer <[email protected]>
Evrenoglou T, Nikolakopoulou A, Schwarzer G, Rücker G, Chaimani A (2024): Producing treatment hierarchies in network meta-analysis using probabilistic models and treatment-choice criteria. https://arxiv.org/abs/2406.10612
Useful links:
Network meta-analysis comparing antidepressants in patients with major depressive disorder.
A data frame with the following columns:
| studyid | study id |
| drug_name | antidepressant name |
| ntotal | number of randomized patients in treatment arm |
| responders | number of responders |
Cipriani A, Furukawa T, Salanti G, Chaimani A, et al. (2018): Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis Lancet, 391, 1357–1366
data(antidepressants) # ranks <- tcc(treat = drug_name, studlab = studyid, event = responders, n = ntotal, data = antidepressants, mcid = 1.25, sm = "OR", small.values = "undesirable") # Visualize treatment choice criteron for treatment "escitalopram" forest(ranks, treat = "escitalopram") # Fit the model fit <- mtrank(ranks) # Visualize the results forest(fit) # Calculate pairwise probabilities paired_pref(fit, type = "better", treat1 = "bupropion", treat2 = "escitalopram") # Same results using pairwise object as input to tcc() # (and running a network meta-analysis) # pw <- pairwise(treat = drug_name, studlab = studyid, event = responders, n = ntotal, data = antidepressants, sm = "OR") # netmeta(pw) # ranks2 <- tcc(pw, mcid = 1.25, small.values = "undesirable") # fit2 <- mtrank(ranks2) # paired_pref(fit2, type = "better", treat1 = "bupropion", treat2 = "escitalopram")data(antidepressants) # ranks <- tcc(treat = drug_name, studlab = studyid, event = responders, n = ntotal, data = antidepressants, mcid = 1.25, sm = "OR", small.values = "undesirable") # Visualize treatment choice criteron for treatment "escitalopram" forest(ranks, treat = "escitalopram") # Fit the model fit <- mtrank(ranks) # Visualize the results forest(fit) # Calculate pairwise probabilities paired_pref(fit, type = "better", treat1 = "bupropion", treat2 = "escitalopram") # Same results using pairwise object as input to tcc() # (and running a network meta-analysis) # pw <- pairwise(treat = drug_name, studlab = studyid, event = responders, n = ntotal, data = antidepressants, sm = "OR") # netmeta(pw) # ranks2 <- tcc(pw, mcid = 1.25, small.values = "undesirable") # fit2 <- mtrank(ranks2) # paired_pref(fit2, type = "better", treat1 = "bupropion", treat2 = "escitalopram")
Network meta-analysis comparing six antihypertensive drugs against the incidence of diabetes.
A data frame with the following columns:
| study | study label |
| id | study id |
| t | treatment label |
| r | number of events |
| n | group sample size |
| rob | risk of bias assessment |
Elliott W, Meyer P (2007): Incident diabetes in clinical trials of antihypertensive drugs: a network meta-analysis Lancet, 369
data(diabetes) # ranks <- tcc(treat = t, studlab = study, event = r, n = n, data = diabetes, mcid = 1.20, sm = "OR", small.values = "desirable") # forest(ranks, treat = "ARB")data(diabetes) # ranks <- tcc(treat = t, studlab = study, event = r, n = n, data = diabetes, mcid = 1.20, sm = "OR", small.values = "desirable") # forest(ranks, treat = "ARB")
mtrank
This function produces a forest plot that visualizes the ability estimates
calculated with mtrank.
## S3 method for class 'mtrank' forest( x, sorting = "ability", backtransf = FALSE, xlab = "", leftcols = "studlab", leftlabs = "Treatment", rightcols = c("effect", "ci"), rightlabs = c(paste0(if (!backtransf) "log-", "Abilities"), NA), label.left = "Favors average treatment", label.right = "Favors treatment", header.line = TRUE, ... )## S3 method for class 'mtrank' forest( x, sorting = "ability", backtransf = FALSE, xlab = "", leftcols = "studlab", leftlabs = "Treatment", rightcols = c("effect", "ci"), rightlabs = c(paste0(if (!backtransf) "log-", "Abilities"), NA), label.left = "Favors average treatment", label.right = "Favors treatment", header.line = TRUE, ... )
x |
An object of class |
sorting |
An argument specifying the criterion to sort the ability estimates in the forest plot (see Details). |
backtransf |
A logical argument specifying whether to show log-ability
estimates ( |
xlab |
A label for the x-axis. |
leftcols |
A character vector specifying columns
to be printed on the left side of the forest plot
(see |
leftlabs |
A character vector specifying labels for columns on left side of the forest plot. |
rightcols |
A character vector specifying columns
to be printed on the right side of the forest plot
(see |
rightlabs |
A character vector specifying labels for columns on right side of the forest plot. |
label.left |
Graph label on left side of null effect. |
label.right |
Graph label on right side of null effect. |
header.line |
A logical value indicating whether to print a header line or a character string ("both", "below", ""). |
... |
Additional arguments (passed on to
|
The function produces a forest plot and visualizes the ability estimates
obtained from mtrank. The order of the estimates in the
forest plot (argument sorting) can be one of the following:
"ability": sort by descending ability estimates (default),
"se": sort by descending precision, i.e., increasing standard errors,
"none": use order from data set.
A forest plot is plotted in the active graphics device.
Evrenoglou T, Nikolakopoulou A, Schwarzer G, Rücker G, Chaimani A (2024): Producing treatment hierarchies in network meta-analysis using probabilistic models and treatment-choice criteria. https://arxiv.org/abs/2406.10612
data(antidepressants) # ranks <- tcc(treat = drug_name, studlab = studyid, event = responders, n = ntotal, data = antidepressants, mcid = 1.25, sm = "OR", small.values = "undesirable") # fit <- mtrank(ranks) forest(fit, treat = "escitalopram")data(antidepressants) # ranks <- tcc(treat = drug_name, studlab = studyid, event = responders, n = ntotal, data = antidepressants, mcid = 1.25, sm = "OR", small.values = "undesirable") # fit <- mtrank(ranks) forest(fit, treat = "escitalopram")
This function produces a forest plot for all (or selected) study
specific comparisons and visualizes the treatment preference or ties
which are defined from the treatment choice criterion in tcc.
## S3 method for class 'tcc' forest( x, treat = NULL, backtransf = FALSE, leftcols = "studlab", leftlabs = NULL, rightcols = c("effect", "ci"), lty.equi = gs("lty.equi"), col.equi = gs("col.equi"), fill.equi = gs("fill.equi"), fill.lower.equi = fill.equi, fill.upper.equi = rev(fill.equi), header.line = TRUE, col.subgroup = "black", ... )## S3 method for class 'tcc' forest( x, treat = NULL, backtransf = FALSE, leftcols = "studlab", leftlabs = NULL, rightcols = c("effect", "ci"), lty.equi = gs("lty.equi"), col.equi = gs("col.equi"), fill.equi = gs("fill.equi"), fill.lower.equi = fill.equi, fill.upper.equi = rev(fill.equi), header.line = TRUE, col.subgroup = "black", ... )
x |
An object of class |
treat |
A treatment of interest. If specified it returns a forest plot
for all study specific effects related to |
backtransf |
A logical indicating whether results should be
back transformed. If |
leftcols |
A character vector specifying columns
to be printed on the left side of the forest plot
(see |
leftlabs |
A character vector specifying labels for columns on left side of the forest plot. |
rightcols |
A character vector specifying columns
to be printed on the right side of the forest plot
(see |
lty.equi |
Line type (limits of equivalence). |
col.equi |
Line colour (limits of equivalence). |
fill.equi |
Colour(s) for area between limits of equivalence or more general limits. |
fill.lower.equi |
Colour of area between lower limit(s) and reference value. Can be equal to the number of lower limits or the number of limits plus 1 (in this case the the region between minimum and smallest limit is also filled). |
fill.upper.equi |
Colour of area between reference value and upper limit(s). Can be equal to the number of upper limits or the number of limits plus 1 (in this case the region between largest limit and maximum is also filled). |
header.line |
A logical value indicating whether to print a header line or a character string ("both", "below", ""). |
col.subgroup |
The colour to print information on subgroups, i.e., pairwise comparisons. |
... |
Additional arguments (passed on to
|
This function produces forest plots for the study specific treatment effects
in the network. The color indicates whether treatment effects show
a preference (red color) or tie (black color). Additionally, the respective
range of equivalence defined at the function
tcc is visualized for the forest plot.
Argument treat is optional. By default ( treat = NULL),
all study-specific treatment effects in the network are shown. If specified,
only study-specific treatment effects related to the specified treat
are shown which is useful in busy networks with many direct comparisons.
A forest plot is plotted in the active graphics device.
Evrenoglou T, Nikolakopoulou A, Schwarzer G, Rücker G, Chaimani A (2024): Producing treatment hierarchies in network meta-analysis using probabilistic models and treatment-choice criteria. https://arxiv.org/abs/2406.10612
data(diabetes) # ranks <- tcc(treat = t, studlab = study, event = r, n = n, data = diabetes, mcid = 1.20, sm = "OR", small.values = "desirable") # forest(ranks) forest(ranks, treat = "ARB")data(diabetes) # ranks <- tcc(treat = t, studlab = study, event = r, n = n, data = diabetes, mcid = 1.20, sm = "OR", small.values = "desirable") # forest(ranks) forest(ranks, treat = "ARB")
This function fits the Bradley-Terry ranking model and produces a treatment hierarchy based on the method described by Evrenoglou et al. (2024) for network meta-analysis.
mtrank(x, reference.group = NULL, level = x$level) ## S3 method for class 'mtrank' print( x, sorting = "ability", backtransf = FALSE, digits = gs("digits"), digits.prop = gs("digits.prop"), ... )mtrank(x, reference.group = NULL, level = x$level) ## S3 method for class 'mtrank' print( x, sorting = "ability", backtransf = FALSE, digits = gs("digits"), digits.prop = gs("digits.prop"), ... )
x |
|
reference.group |
An argument specifying the reference group. If set to NULL (default), ability estimates of all treatments will be calculated. If some treatment is set as the reference group, relative abilities of all treatments versus the specified reference treatment will be calculated. |
level |
The level used to calculate confidence intervals for ability estimates. |
sorting |
An argument specifying the criterion to sort the ability estimates in the printout (see Details). |
backtransf |
A logical argument specifying whether to show log-ability
estimates ( |
digits |
Minimal number of significant digits for ability estimates,
see |
digits.prop |
Minimal number of significant digits for proportions,
see |
... |
Additional arguments (passed on to |
This function is used to fit a Bradley-Terry model to the paired-preference
data generated from the treatment choice criterion constructed by the
tcc function. This function estimates the ability of
each treatment in the network and the respective standard errors and
confidence intervals using the maximum likelihood approach. To retain
identifiability, the maximization of the log-likelihood takes place subject
to the constrain that the ability estimates sum to 1. Then, the maximum
likelihood estimates (MLEs) are calculated iteratively.
Note that the final estimates of the ability parameters are not necessarily
needed to sum to 1 as after the first iteration of the algorithm the ability
estimates are not normalized. However, by normalizing the final ability
estimates to sum up to 1 these can be interpreted as "the probability that
each treatment is having the highest ability".
Finally, a parameter "v" controlling the prevalence of ties in the network
is also estimated. Although the estimated values of this parameter do
not have a direct interpretation they are useful for estimating pairwise
probabilities (see paired_pref).
If argument reference.group is not NULL, a reference treatment
group is specified. Mathematically, this means that the maximization problem
is now identifiable, subject to the condition that the ability of this
treatment is 0. Then, the resulting MLEs are the relative abilities of all
treatments in the network versus the specified reference treatment group.
Note that the estimates of the parameter "v" and the normalized probabilities
do not depend on the value for argument reference.group.
A data frame containing the resulting log-ability estimates, their standard errors and their confidence intervals.
The estimate of the tie prevalence parameter v.
The normalized ability estimates for each treatment.
Evrenoglou T, Nikolakopoulou A, Schwarzer G, Rücker G, Chaimani A (2024): Producing treatment hierarchies in network meta-analysis using probabilistic models and treatment-choice criteria. https://arxiv.org/abs/2406.10612
data(antidepressants) ranks <- tcc(treat = drug_name, studlab = studyid, event = responders, n = ntotal, data = antidepressants, mcid = 1.25, sm = "OR", small.values = "undesirable") # fit1 <- mtrank(ranks) # # Print log-ability estimates fit1 # # Print ability estimates print(fit1, backtransf = TRUE) # Visualize results forest(fit1) # Repeat using a 'pairwise' object pw <- pairwise(treat = drug_name, studlab = studyid, event = responders, n = ntotal, data = antidepressants, sm = "OR") ranks2 <- tcc(pw, mcid = 1.25, small.values = "undesirable") # fit2 <- mtrank(ranks2) # Print log-ability estimates fit2 # Print ability estimates print(fit2, backtransf = TRUE) # Visualize results forest(fit2)data(antidepressants) ranks <- tcc(treat = drug_name, studlab = studyid, event = responders, n = ntotal, data = antidepressants, mcid = 1.25, sm = "OR", small.values = "undesirable") # fit1 <- mtrank(ranks) # # Print log-ability estimates fit1 # # Print ability estimates print(fit1, backtransf = TRUE) # Visualize results forest(fit1) # Repeat using a 'pairwise' object pw <- pairwise(treat = drug_name, studlab = studyid, event = responders, n = ntotal, data = antidepressants, sm = "OR") ranks2 <- tcc(pw, mcid = 1.25, small.values = "undesirable") # fit2 <- mtrank(ranks2) # Print log-ability estimates fit2 # Print ability estimates print(fit2, backtransf = TRUE) # Visualize results forest(fit2)
mtrank objectThis function uses the estimates of ability and tie prevalence parameters
from a mtrank object and calculates pairwise probabilities
about the preference or the tie between two treatments based on equations (7)
and (8) in Evrenoglou et al. (2024).
paired_pref(x, treat1, treat2, type) ## S3 method for class 'paired_pref' print(x, type = attr(x, "type"), digits = 4, ...)paired_pref(x, treat1, treat2, type) ## S3 method for class 'paired_pref' print(x, type = attr(x, "type"), digits = 4, ...)
x |
An object of class |
treat1 |
The first treatment considered in the treatment comparison. |
treat2 |
The second treatment considered in the treatment comparison. |
type |
A character vector specifying the probability of interest. Either "better", "tie", "worse", or "all" (can be abbreviated). |
digits |
Minimal number of significant digits for proportions,
see |
... |
Additional arguments (passed on to |
Pairwise probabilities between any two treatments in the network can be
calculated using the ability estimates obtained from mtrank
and equations (7) and (8) in Evrenoglou et al. (2024). The probabilities
are calculated in the direction treat1 vs treat2. The available
probability types are
"better": probability that treat1 is better than treat2,
"tie": probability that treat1 is equal to treat2,
"worse": probability that treat1 is worse than treat2,
"all": all three probabilities.
Please note that all the arguments of this function are mandatory.
The probability (or probabilities) of interest for the comparison
treat1 vs treat2 based on the argument type.
Evrenoglou T, Nikolakopoulou A, Schwarzer G, Rücker G, Chaimani A (2024): Producing treatment hierarchies in network meta-analysis using probabilistic models and treatment-choice criteria. https://arxiv.org/abs/2406.10612
data(antidepressants) # ranks <- tcc(treat = drug_name, studlab = studyid, event = responders, n = ntotal, data = antidepressants, mcid = 1.25, sm = "OR", small.values = "undesirable") # fit <- mtrank(ranks) # paired_pref(fit, type = c("better", "worse"), treat1 = "bupropion", treat2 = "escitalopram") # paired_pref(fit, type = c("better", "worse"), treat1 = "escitalopram", treat2 = "bupropion") # paired_pref(fit, type = "all", treat1 = c("bupropion", "escitalopram"), treat2 = c("escitalopram", "bupropion"))data(antidepressants) # ranks <- tcc(treat = drug_name, studlab = studyid, event = responders, n = ntotal, data = antidepressants, mcid = 1.25, sm = "OR", small.values = "undesirable") # fit <- mtrank(ranks) # paired_pref(fit, type = c("better", "worse"), treat1 = "bupropion", treat2 = "escitalopram") # paired_pref(fit, type = c("better", "worse"), treat1 = "escitalopram", treat2 = "bupropion") # paired_pref(fit, type = "all", treat1 = c("bupropion", "escitalopram"), treat2 = c("escitalopram", "bupropion"))
Auxiliary function to transform data from paired-preference to long-arm format
pp2long(x)pp2long(x)
x |
An object of class "ppdata" (part of |
Data set in long-arm format that can be used as input to
rankings.
Guido Schwarzer [email protected]
data(diabetes) # ranks <- tcc(treat = t, studlab = study, event = r, n = n, data = diabetes, mcid = 1.20, sm = "OR", small.values = "desirable") # pdat <- ranks$ppdata # ldat <- pp2long(pdat) head(ldat) library("PlackettLuce") ungrouped.preferences <- rankings(ldat, id = "id", item = "treat", rank = "rank") grouped.preferences <- as.rankings(ungrouped.preferences, index = as.numeric(as.factor(pdat$studlab))) # fit <- PlackettLuce(grouped.preferences) # coef(summary(fit, ref = ranks$reference.group))[, 1] # Results stored in mtrank() mtrank(ranks)$estimates$log_abilitydata(diabetes) # ranks <- tcc(treat = t, studlab = study, event = r, n = n, data = diabetes, mcid = 1.20, sm = "OR", small.values = "desirable") # pdat <- ranks$ppdata # ldat <- pp2long(pdat) head(ldat) library("PlackettLuce") ungrouped.preferences <- rankings(ldat, id = "id", item = "treat", rank = "rank") grouped.preferences <- as.rankings(ungrouped.preferences, index = as.numeric(as.factor(pdat$studlab))) # fit <- PlackettLuce(grouped.preferences) # coef(summary(fit, ref = ranks$reference.group))[, 1] # Results stored in mtrank() mtrank(ranks)$estimates$log_ability
This function transforms data that are given in wide or long
arm-based format (e.g. input format for WinBUGS or JAGS) to a
paired-preference format needed as input to mtrank.
The function can transform data with binary and continuous arm-based to
preference-based format.
tcc( treat, event, n, mean, sd, data = NULL, studlab, mcid = NULL, lower.equi = NULL, upper.equi = NULL, small.values = gs("small.values"), relax = FALSE, level = 0.95, sm, keepdata = gs("keepdata"), ... ) ## S3 method for class 'tcc' print(x, ...)tcc( treat, event, n, mean, sd, data = NULL, studlab, mcid = NULL, lower.equi = NULL, upper.equi = NULL, small.values = gs("small.values"), relax = FALSE, level = 0.95, sm, keepdata = gs("keepdata"), ... ) ## S3 method for class 'tcc' print(x, ...)
treat |
Either a |
event |
A list or vector with information on number of events for individual treatment arms (see Details). |
n |
A list or vector with information on number of observations for individual treatment arms (see Details). |
mean |
A list vector with estimated means for individual treatment arms (see Details). |
sd |
A list or vector with information on the standard deviation for individual treatment arms (see Details). |
data |
A data frame containing the study information. |
studlab |
A vector with study labels. |
mcid |
A numeric vector specifying the minimal clinically important value (see Details). |
lower.equi |
A numeric value specifying the lower limit of the range of equivalence (see Details). |
upper.equi |
A numeric value specifying the upper limit of the range of equivalence (see Details). |
small.values |
A character string specifying whether small
treatment effects indicate a beneficial ( |
relax |
A logical optional argument. If TRUE it 'relaxes' the tcc to only consider the bounds of ROE when specifying 'wins' and ties. The default FALSE uses the criterion described by Evrenoglou et al. (2024) and considers also the statistical significance on top of the ROE bounds (see Details). |
level |
The level used to calculate confidence intervals for log-abilities. |
sm |
The effect measure of interest (see Details). |
keepdata |
A logical indicating whether original data should be kept in tcc object. |
... |
Additional arguments (passed on to
|
x |
An object of class |
R function mtrank expects data in a paired-preference
format, where for each study-specific pairwise comparison in the network a
treatment preference or tie is indicated. For example, for the
study-specific comparison between treatments A and B the
potential outcomes are:
A > B
A < B
A = B
The data transformation takes place based on the study-specific treatment
effects and the treatment choice criterion. R function
pairwise is called internally to calculate the
study-specific treatment effect estimates and standard errors. This ensures
that data given in either 'long' or 'wide' arm-based format will be suitably
used to calculate the study-specific treatment effect estimates and standard
errors while ensuring that a network of multi-arm studies gets an
equivalent representation as a network of two-arm studies. It is also
possible to provide a pairwise as the main input.
In this case, inputs for the arguments event, n, mean,
sd, data, studlab, or keepdata are ignored.
This function implements treatment choice criteria based on the method by Evrenoglou et al. (2024). Namely, a range of equivalence (ROE) can be specified by
argument mcid. Then the limits of the ROE
will be defined based on the values (i) mcid, 1/mcid for
ratio measures and (ii) mcid and -mcid for difference
measures.
arguments lower.equi and upper.equi.
These arguments allow the users to define their own limits of the ROE,
given the restriction that the lower limit will always be smaller than the
upper limit.
Note that when the argument mcid is specified, the arguments
lower.equi and upper.equi are ignored.
Either only the mcid or both of the lower.equi and
upper.equi must be specified for the proper
definition of the ROE.
After setting the ROE, each study-specific treatment effect will be
categorised as a treatment preference or a tie. The argument relax
controls the amount of conservatism of the treatment choice criterion.
If set to FALSE (default), the treatment choice criterion is
equivalent to the one described by Evrenoglou et al. (2024). In this case,
study-specific treatment effects need to be both statistically and clinically
significant to indicate a treatment preference. If set to TRUE, the
criterion is relaxed and the study-specific treatment effects need to be only
clinically significant to indicate a treatment preference.
This function can transform data with binary and continuous outcomes. Depending on the outcome, the following arguments are mandatory:
treat, event, n (for binary outcomes);
treat, n, mean, sd (for continuous outcomes).
Finally, the argument sm is used to define the effect measure of
interest for transforming the data into paired-preference format;
see metabin and metacont for a
list of available effect measures.
The initial data in a paired-preference format.
The correspondence between the initial study names (passed in the argument studlab) and the index name of the paired-preference format data.
Evrenoglou T, Nikolakopoulou A, Schwarzer G, Rücker G, Chaimani A (2024): Producing treatment hierarchies in network meta-analysis using probabilistic models and treatment-choice criteria. https://arxiv.org/abs/2406.10612
data(diabetes) # ranks <- tcc(treat = t, studlab = study, event = r, n = n, data = diabetes, mcid = 1.20, sm = "OR", small.values = "desirable") # forest(ranks, treat = "ARB")data(diabetes) # ranks <- tcc(treat = t, studlab = study, event = r, n = n, data = diabetes, mcid = 1.20, sm = "OR", small.values = "desirable") # forest(ranks, treat = "ARB")