---
title: "pscDesign"
author: "Richard Jackson"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{psc-vignette}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height= 5
)
```

#  pscDesign: A tool for Synthetically Controlled Trials

pscDesign is a package which allows the design of prospective studies/trials 
using Personalised Synthetic Controls.

The purpose of a fully synthetic trial is to recruit only patients to an 
experimental arm.  Personalised synthetic controls here allow a patients 
observed response to be compared against their model estimated control.
Details can be found here ("https://richjjackson.github.io/mecPortal/").  

We present an example based in Pancreatic Ductal Adenocarcinoma using a Counter 
Factual Model (CFM) generated on patients receivign Gemcitabine therapy.
Full details on the model setting, construction, validation and interpretation 
are found [here](https://richjjackson.github.io/mecPortal//models/pdac_gem.html)

## Trial Design

The overall design uses a Bayesian approach and uses a simulation 
study to estimate the study parameters.  Evaluations of efficacy are based on 
the posterior distribution comparing the experimental regimen against the 
synthetically generated controls.

Trial design also dependes on the ability to recruit patients, in this example 
we assume a study with 4 sites recruiting at an average rate of 0.88 
patients/site/month and sites opening to recruitment at a rate of 1 per month.  
The pscDesign package includes a function [recForcast()] which allows for 
monthly recruitment estimates.

## 'Traditional' design comparisons

As an illustration, we initially examine traditional randomised phase II trial 
designsetting a HR = 0.7, using an alpha level of 0.1 and power of 90%

### Randomised pahse II study design

```
gsdesign.survival(c(1),haz.ratio=0.7,r=1,sig.level=0.1,power=0.9,alternative="one.sided")

 Group sequential design for comparing survival data with hazard ratio = 0.7 
   Treatment allocated at 1:1 (C:E) ratio 
   power family of boundary; 0 (Pocock) to 0.5 (O'Brien-Fleming) 

 total number of events = 206.56 
   information fraction = 1 
      efficacy boundary = 1.282 (power = 0.5) 
              sig.level = 0.1 
                  power = 0.9 
            alternative = one.sided 
```


## Syntheticall Controlled Trial Design

We now walk through the steps required to use the pscDesign function. To do this we make use of the gemCFM data
 

```{r, loadData}
library(pscDesign)
library(psc)
gemCFM <- pscDesign::gemCFM
```


Before using the pscDesign() function to perform the simulation study, we first 
ensure the CFM is compatible by simulating a single dataset using the dataSim() 
function


```{r, singleFit}
simData <- dataSim(gemCFM,n0=0,n1=500,beta=log(0.5),fuTime=12,recTime=24)
psc.ex <- pscfit(gemCFM,simData)

```

We can evaluate the fit of this example using the summary and plot() functions


```{r, singleFitSumm}
summary(psc.ex)
plot(psc.ex)
```


### Single Arm Trial Design

We first demonstrate the impact of a single arm trial design.  We design the 
study in 2-steps.  Initially we provide a estimated monthly recruitment rate. 


#### recruitment forecast
This is based on 4 sites recruiting at an average rate of 0.88 patients/site/month.  
Sites will themselves open at a rate of 1 site/month and a maximum recruitment
time of 24 months is set.  This gives a total sample size of 70 patients.

```{r, singleArmRec}
N.site <- 4
rpm <- 0.88
open.rate <- 1
recTime <- 24
recF <- recForcast(N.site,rpm,open.rate,Max.Time=recTime)  
```


#### Power estimates

We run the pscDesign function using the 'rec=recF' option to include these 
recruitment estimates.  Other parameters show we are interested in a ningle arm 
study (n=0) looking for a HR = 0.7 (beta=log(0.7)).  We allow for a follow-up 
period of 12 months and in this example use only 10 simulations (you will want 
more but we keep this small to keep the processing speed down).  We limit the 
pscfit function to use 750 MCMC iteration and allow the first 250 to act as a 
burn in.

```{r, singleArm,echo=T,results='hide'}
pscDes_singArm <- pscDesign(CFM=gemCFM,n0=0,n1=70,beta=log(0.7),rec=recF,
                                fuTime=12,nsim=10,nsim.psc=750,burn.psc=250,
                                bound=0,direction="greater",
                                alpha_eval=c(0.05,0.1,0.15,0.2))
```

We view the results below showing the esitmated number of events, posterior mean
and posterior standard deviation.  Also given are the power estiamtes for a 
range of alpha levels.

```{r, singleArmRes}
pscDes_singArm
```


### Randomised Trial Design

In a randomised trial design we extend the example to allow 30 patients onto the 
control arm as well as 70 on the experimental.

#### recruitment forecast

Given the extra patients required we extend recruitment by a period of 6 months.

```{r, randRec}
N.site <- 4
rpm <- 0.88
open.rate <- 1
recTime <- 30
recF <- recForcast(N.site,rpm,open.rate,Max.Time=recTime)  #100 patients over 30 months
```

We ammend the pscDesign() function allowing for control patients (n=30)

```{r, randArm,echo=T,results='hide'}
pscDes_rand <- pscDesign(CFM=gemCFM,n0=30,n1=70,beta=log(0.7),rec=recF,
                                fuTime=12,nsim=5,nsim.psc=750,burn.psc=250,
                                bound=0,direction="greater",
                                alpha_eval=c(0.05,0.1,0.15,0.2))
```

The results of the design given below now include estimates of the efficacy 
parameter using Personalised Synthetic Controls, Direct Estimation (e.g. using 
only the information in the trial) and the 'posterior' estimate which combines 
these two efficacy estimates

```{r, randArmRes}
pscDes_rand
```