Type:	Package
Title:	Study Design for Personalised Synthetic Controls
Version:	1.0.0
Maintainer:	Richard Jackson <richJ23@liverpool.ac.uk>
Description:	Tools for the design of prospective studies using Personalised Synthetic Controls. Can be used in either single arm or randomised studies.
License:	GPL-3
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.3
Depends:	R (≥ 4.0.0), survival
Imports:	psc (≥ 2.0.0), S7
Suggests:	knitr
VignetteBuilder:	knitr
URL:	https://github.com/richJJackson/pscDesign
BugReports:	https://github.com/richJJackson/pscDesign/issues
NeedsCompilation:	no
Packaged:	2026-01-23 16:48:18 UTC; richardjackson
Author:	Richard Jackson [cre, aut, cph]
Repository:	CRAN
Date/Publication:	2026-01-27 21:30:12 UTC

Simulate covariates from a Counter factual model

Description

A function to simulate covariate values from a Counter Factual Model

Usage

covSim(CFM, n = 100)

Arguments

Details

This functions extracts the covariates form a counter factual model and samples from them in order to create a simulated dataset.

Value

A cumulative Hazard function

Study design using Personalised Synthetic Controls

Description

pscDesign performs the simulations required to estimate the Power of a study designed using personalised synthetic controls.

Usage

dataSim(CFM, n0 = 0, n1 = 100, beta = 0, fuTime, recTime, rec = NULL)

Arguments

Details

The datSim function simulates a dataset including sampled covariatees and outcome data from a counterfactual model

Time parameters (fuTime, recTime) should be expressed on the same scale on which they are specified in the Counter Factual Model. One of recTime or rec must be specified. If rec is specified, recTime will be ignored.

Value

A data frame intended for use with the CFM including covariate and outcome data

Examples

gemCFM <- pscDesign::gemCFM
dataSim(gemCFM,n0=10,n1=20,beta=log(0.7),fuTime=12,recTime=12)

Generate a cumulative hazard function from a flexible parametric model

Description

A function to estimate the survival function based on parameter estimates.

Usage

fpmH(CFM, maxTime = 24)

Arguments

Details

This functions extracts the baseline (cumulative) hazard parameters from a counter factual model. This is used in the simulation of datasets.

Value

A cumulative Hazard function

Generate a survival function from a flexible parametric model

Description

A function to estimate the survival function based on parameter estimates.

Usage

fpmSurv(CFM, beta = NULL, lp = NULL, maxTime = 24)

Arguments

Details

This functions extracts the baseline (cumulative) hazard parameters from a counter factual model and uses these to construct survival estimates. This is used in the simulation of datasets. If a (log) hazard ratio, beta, or a linear predictor, lp, are supplied - these will be used to adjust the baseline estimates.

Value

A survival function

gemCFM

Description

Model for a survival outcome based on Gemcitbine patients from ESPAC-3. A generated model with a survival endpoint and a cuymulative hazard function estimated using flexible parametric splines. Data for the model were obtained from the ESPAC-3 trials

Usage

gemCFM

Format

A model of class 'pscCFM' containg a 'flexsurvreg' model:

gamma

cumulative baseline hazard parameters

nodes

negative (n=1) or positive (n=2) lymph nodes

grade

tumour grade (1,2 or 3)

lca199

log transformed ca19.9

ResecM

Resection Margins)

Source

simulated

modp

Description

A function which rrturns either the input value (if positive) or zero (if negative)

Usage

modp(x)

Arguments

Details

A fucntion which returns a version of x with negative values replacd with 0

Value

a numeric vector with negative values replaced with 0

A function to evaluate posterior distribution

Description

postEval evaluated a distribition based on given mean and standard deviation

Usage

postEval(mn, sd, bound = 0, direction = "greater")

Arguments

Study design using Personalised Synthetic Controls

Description

pscDesign performs the simulations required to estimate the Power of a study designed using personalised synthetic controls.

Usage

pscDesign(
  CFM,
  n0 = 0,
  n1,
  beta,
  fuTime,
  recTime,
  rec = NULL,
  nsim = 4,
  nsim.psc = 500,
  burn.psc = 200,
  bound = 0,
  direction = "greater",
  alpha_eval = c(0.01, 0.025, 0.05, 0.1, 0.15, 0.2)
)

Arguments

Details

The pscDesign function performs a simulation study to estimate the design parameters for a study using personalised synthetic controls.

Time parameters (fuTime, recTime) should be expressed on the same scale on which they are specified in the Counter Factual Model. One of recTime or rec must be specified. If rec is specified, recTime will be ignored.

Value

A list containing a summary of the simulated datasets and estimated type-II error rates (Power) against leveld of significance (alpha-levels)

Examples

gemCFM <- pscDesign::gemCFM
pscDesign(gemCFM,n0=0,n1=100,beta=log(0.7),fuTime=12,recTime=12,nsim=2,
    nsim.psc=300,burn.psc=100)

Estimating recruitment rates based on the number of sites and average site recruitment rates.

Description

Estimating recruitment rates based on the number of sites and average site recruitment rates.

Usage

recForcast(N.site, rpm, open.rate, Max.Time, penal = 0.5, plot = TRUE, ...)

Arguments

Details

This functions estimates monthly recruitment rates based on the number of sites and the average monthly recruitment rate. The resulting dataset can be passed to pscDesign to improve estimation of design parameters.

Value

A dataset giving the monthly recruitment rate

Examples

recForcast(N.site=5,rpm=1,open.rate=1,Max.Time=12)

Evaluation of a sampled dataset within pscDesign

Description

trialSamp evaluates a sampled dataset and returns the reuslts

Usage

trialSamp(
  CFM,
  n0,
  n1,
  beta,
  fuTime,
  recTime,
  rec,
  nsim.psc = 750,
  burn.psc = 250
)

Arguments

Value

The number of events as well as estimates of the posterior mean and standard deviation