The {ppseq} package provides functions to design clinical trials using Bayesian sequential predictive probability monitoring. Functionality is available to design one-arm or two-arm trials by searching over a grid of combinations of posterior and predictive thresholds and identifying the optimal design according to two criteria: accuracy and efficiency. Interactive plotting allows easy comparison of the various design options and easy trial implementation through decision rule plots.

You can install the production version of ppseq from CRAN with:

`install.packages("ppseq")`

Or you can install the development version of ppseq from GitHub with:

`::install_github("zabore/ppseq") remotes`

`library(ppseq)`

The primary function to search over a grid of combinations of
posterior and predictive thresholds for a certain trial design is
`calibrate_thresholds()`

. This function is computationally
intensive to varying degrees depending on the number of looks and the
number of threshold combinations, and is best run on a server and/or
with parallelization.

```
set.seed(12345)
<-
calthresh calibrate_thresholds(
p_null = c(0.2, 0.2),
p_alt = c(0.2, 0.5),
n = cbind(seq(10, 50, 10), seq(10, 50, 10)),
N = c(50, 50),
pp_threshold = seq(0.9, 0.95, 0.01),
ppp_threshold = seq(0.05, 0.2, 0.05),
delta = 0
)
```

The resulting design options can be compared interactively compared
by passing the results to `plot()`

with the option
`plotly = TRUE`

. Static versions of the plots are also
available using the default option `plotly = FALSE`

, which
produces `ggplot`

results. Plot output can optionally be
filtered by desired range of type 1 error and minimum power. The default
plots all design options. The results can also be viewed in tabular form
by passing the results to `print()`

with filtering options
for the desired range of type 1 error and minimum power.

`plot(calthresh)`

The optimal accuracy and optimal efficiency designs can be obtained
by passing the results to the `optimize_design()`

function,
with filtering applied for the desired range of type 1 error and minimum
power.

`optimize_design(calthresh, type1_range = c(0.025, 0.05), minimum_power = 0.8)`

After selecting a design, we can obtain a set of decision rules to
implement the trial, so that no calculations will be needed during the
course of the trial. The `calc_decision_rules()`

function
will generate the decision rules to stop or continue at each interim
look of the trial.

```
set.seed(123456)
<-
opteffrules calc_decision_rules(
n = cbind(seq(10, 50, 10), seq(10, 50, 10)),
N = c(50, 50),
theta = 0.94,
ppp = 0.2,
p0 = NULL,
delta = 0
)
```

The results can be displayed with interactive graphics by passing the
results to `plot()`

with the default option
`plotly = TRUE`

. Below are the static `ggplot`

versions created with the option `plotly = FALSE`

for
demonstration purposes. Tabular results can be obtained by passing the
results to `print()`

.

`plot(opteffrules, plotly = FALSE)`

See the vignettes for the one-sample and two-sample cases for additional details about available features and options.