# Evaluate a single dataset for a single pp_threshold and ppp_threshold combination

Source:`R/calibrate_thresholds.R`

`eval_thresh.Rd`

Helper function for calibrate_thresholds() function that evaluates a single combination of a pp_threshold and a ppp_threshold for a single dataset

## Usage

```
eval_thresh(
data,
pp_threshold,
ppp_threshold,
p0,
N,
direction = "greater",
delta = NULL,
monitoring = "futility",
prior = c(0.5, 0.5),
S = 5000
)
```

## Arguments

- data
the name of the dataset

- pp_threshold
the posterior probability threshold of interest

- ppp_threshold
the posterior probability threshold of interest for futility monitoring

- p0
The target value to compare to in the one-sample case. Set to NULL for the two-sample case.

- N
the total planned sample size at the end of the trial, c(N0, N1) for two-sample case; integer of total planned sample size at end of trial N for one-sample case

- direction
"greater" (default) if interest is in P(p1 > p0) and "less" if interest is in P(p1 < p0) for two-sample case. For one-sample case, "greater" if interest is in P(p > p0) and "less" if interest is in P(p < p0).

- delta
clinically meaningful difference between groups. Typically 0 for the two-sample case. NULL for the one-sample case (default).

- monitoring
the type of interim monitoring to be performed. One of "futility" or "efficacy". Default is "futility".

- prior
hyperparameters of prior beta distribution. Beta(0.5, 0.5) is default

- S
number of samples, default is 5000

## Value

Returns a tibble with the total sample size at the end of the trial, the number of responses observed at the end of the trial, the pp_threshold considered, the ppp_threshold considered, the observed predictive probability generated from calc_predictive(), and an indicator for whether the trial was positive or not at the end