ditp: Integrated Two-Component Prediction (ITP) function

In MBNMAdose: Dose-Response MBNMA Models

Integrated Two-Component Prediction (ITP) function

Description

Similar parameterisation to the Emax model but with non-asymptotic maximal effect (Emax). Proposed by proposed by \insertCitefumanner;textualMBNMAdose

Usage

ditp(emax = "rel", rate = "rel", p.expon = FALSE)

Arguments

emax	Pooling for Emax parameter. Can take "rel", "common", "random" or be assigned a numeric value (see details).
rate	Pooling for Rate parameter. Can take "rel", "common", "random" or be assigned a numeric value (see details).
p.expon	A logical object to indicate whether ed50 and hill parameters should be expressed within the dose-response function on an exponential scale

Details

Emax represents the maximum response. Rate represents the rate at which a change in the dose of the drug leads to a change in the effect

{E_{max}}\times\frac{(1-exp(-{rate}\times{x}))}{(1-exp(-{rate}\times{max(x)}))}

Value

An object of class("dosefun")

Dose-response parameters

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.

When relative effects are modelled on more than one dose-response parameter, correlation between them is automatically estimated using a vague inverse-Wishart prior. This prior can be made slightly more informative by specifying the scale matrix omega and by changing the degrees of freedom of the inverse-Wishart prior using the priors argument in mbnma.run().

References

\insertAllCited

Examples

# Model a common effect on rate
ditp(emax="rel", rate="common")

MBNMAdose documentation built on Aug. 8, 2023, 5:11 p.m.