escalation (version 0.1.8)

CorrelatedPatientSample: A sample of patients that experience correlated events in simulations.

Description

Class to house the latent random variables that govern toxicity and efficacy events in patients. Instances of this class can be used in simulation-like tasks to effectively use the same simulated individuals in different designs, thus supporting reduced Monte Carlo error and more efficient comparison. This class differs from PatientSample in that the latent variables that underlie efficacy and toxicity events, and therefore those events themselves, are correlated, e.g. for positive association, a patient that experiences toxicity has increased probability of experiencing efficacy too. Correlated uniformly-distributed variables are obtained by inverting bivariate normal variables. The extent to which the events are correlated is controlled by rho, the correlation of the two normal variables.

Super class

escalation::PatientSample -> CorrelatedPatientSample

Public fields

num_patients

(`integer(1)`)

mu

(`numeric(2)`)

sigma

(`matrix(2, 2)`)

Methods

Public methods

• CorrelatedPatientSample$new()

• CorrelatedPatientSample$expand_to()

• CorrelatedPatientSample$clone()

Inherited methods

• escalation::PatientSample$get_eff_u()
• escalation::PatientSample$get_patient_eff()
• escalation::PatientSample$get_patient_tox()
• escalation::PatientSample$get_tox_u()
• escalation::PatientSample$set_eff_and_tox()

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Method new()

Creator.

Usage

CorrelatedPatientSample$new(num_patients = 0, rho = 0)

Arguments

num_patients

(`integer(1)`).

rho

(`integer(1)`) correlation of

Returns

[CorrelatedPatientSample].

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Method expand_to()

Expand sample to size at least num_patients

Usage

CorrelatedPatientSample$expand_to(num_patients)

Arguments

num_patients

(`integer(1)`).

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Method clone()

The objects of this class are cloneable with this method.

Usage

CorrelatedPatientSample$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Sweeting, M., Slade, D., Jackson, D., & Brock, K. (2023). Potential outcome simulation for efficient head-to-head comparison of adaptive dose-finding designs. Preprint.