# Calculates ranking probabilities for AUC from a time-course MBNMA

`rankauc.Rd`

Calculates ranking probabilities for AUC from a time-course MBNMA

## Arguments

- mbnma
An S3 object of class

`"mbnma"`

generated by running a time-course MBNMA model- lower_better
Indicates whether negative responses are better (

`lower_better=TRUE`

) or positive responses are better (`lower_better=FALSE`

)- treats
A character vector of treatment/class names (depending on the value of

`level`

). If left`NULL`` then rankings will be calculated for all treatments/classes. Note that unlike`

rank.mbnma()` this argument cannot take a numeric vector.- level
Can take either

`"treatment"`

to make predictions for treatments, or`"class"`

to make predictions for classes (in which case`object`

must be a class effect model).- int.range
A numeric vector with two elements that indicates the range over which to calculate AUC. Takes the form c(lower bound, upper bound). If left as

`NULL`

(the default) then the range will be between zero and the maximum follow-up time in the dataset.- n.iter
The number of iterations for which to calculate AUC (if

`"auc"`

is included in`params`

). Must be a positive integer. Default is the value used in`mbnma`

.- subdivisions
The number of subdivisions over which to integrate (see

`integrate`

)- ...
Arguments to be sent to R2jags for synthesis of the network reference treatment effect (using

`ref.synth()`

)

## Value

A named list whose elements include:

`summary.rank`

A data frame containing mean, sd, and quantiles for the ranks of each treatment given in`treats`

`prob.matrix`

A matrix of the proportions of MCMC results for which each treatment/class in`treats`

ranked in which position for the given parameter`rank.matrix`

A matrix of the ranks of MCMC results for each treatment/class in`treats`

for the given parameter.

## Details

`"auc"`

can be specified in `param`

to rank treatments based on
Area Under the Curve (AUC). This accounts for the effect of multiple
time-course parameters simultaneously on the treatment response, but will
be impacted by the range of time over which AUC is calculated (`int.range`

).
This requires integration over `int.range`

and can take some time to run (particularly)
for spline functions as this uses the trapezoid method rather than adaptive quadrature).
Note that `"auc"`

can only be calculated at the treatment-level in class effect models.

As with other post-estimation functions, `rank()`

should only be performed on
models which have successfully converged. Note that rankings can be very sensitive to
even small changes in treatment effects and therefore failure to converge in only
one parameter may have substantial impact on rankings.