dloglin

<p>Modelled assuming relative effects (<code>"rel"</code>)</p>

Fits Bayesian dose-response model-based network meta-analysis (MBNMA)
that incorporate multiple doses within an agent by modelling different dose-response functions, as
described by Mawdsley et al. (2016) <doi:10.1002/psp4.12091>.
By modelling dose-response relationships this can connect networks of evidence that might
otherwise be disconnected, and can improve precision on treatment estimates. Several common
dose-response functions are provided; others may be added by the user. Various characteristics
and assumptions can be flexibly added to the models, such as shared class effects. The consistency
of direct and indirect evidence in the network can be assessed using unrelated mean effects models
and/or by node-splitting at the treatment level.

Hugo Pedder

MBNMAdose

Argument	Model specification
`"rel"`	Implies that relative effects should be pooled for this dose-response parameter separately for each agent in the network.
`"common"`	Implies that all agents share the same common effect for this dose-response parameter.
`"random"`	Implies that all agents share a similar (exchangeable) effect for this dose-response parameter. This approach allows for modelling of variability between agents.
`numeric()`	Assigned a numeric value, indicating that this dose-response parameter should not be estimated from the data but should be assigned the numeric value determined by the user. This can be useful for fixing specific dose-response parameters (e.g. Hill parameters in Emax functions) to a single value.

dloglin: Log-linear (exponential) dose-response function

Description

Usage

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Value

Details

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Examples