This example show in details how to construct a new factory of
`DISTRIBUTION`

objects using a convolution of parameters to
propagate the uncertainty to the final calculation.

The estimation of sample size require the input of different parameters in a way that when the parameters are correct, the power to have a significant result is correct. However many times there are some uncertainty on the parameters and this is not reflected in the power.

The `convdist`

package could help on explicitly express
uncertainty on those parameters and estimate the distribution of sample
sizes that would satisfy the power requirements of the assay.

We explore how to estimate the sample size for binomial outcome with
uncertainty and explain the details of how to construct a
`DISTRIBUTION`

factory function.

For an individually randomized trial, the number of subjects per group could be defined as \[n = (z_{\alpha/2} + z_\beta)^2 [\pi_0(1 – \pi_0) + \pi_1(1 – \pi1)]/(\pi_0 – \pi_1)^2\]

In this formula, \(z_{\alpha/2}\) and \(z_\beta\) are standard normal distribution values corresponding to upper tail probabilities of \(z_{\alpha/2}\) and \(z_\beta\) respectively. This choice of sample size provides a power of 100(1 – \(\beta\))% to obtaining a significant difference (\(P < \alpha\) on a two-sided test), assuming that the true (population) proportion in presence or absence of the interventions are \(\pi_1\) and \(\pi_0\) respectively

\(\pi_1\) can be express on terms of \(\pi_0\) as \(\pi_1 = \pi_0 RR\) where \(RR\) is the relative risk.

In the real world, there could be uncertainty on the exact values of \(\pi_0\) and \(RR\) as they may come from previous studies or surveys. What would be the distribution of \(n\) if the uncertainty is taking into account?

`DISTRIBUTION`

factoryWe can construct a `DISTRIBUTION`

object which would
produce random numbers taking into account the a probability
distribution for \(\pi_0\) and \(RR\)

See the code of `new_UNIFORM`

function as an example of
how is a factory of `DISTRIBUTION`

objects:

```
library(convdistr)
new_UNIFORM
#> function (p_min, p_max, p_dimnames = "rvar")
#> {
#> stopifnot(p_min <= p_max)
#> .oval = (p_max + p_min)/2
#> names(.oval) <- p_dimnames
#> structure(list(distribution = "UNIFORM", seed = sample(1:2^15,
#> 1), oval = .oval, rfunc = restrict_environment(function(n) {
#> matrix(runif(n, p_min, p_max), ncol = 1, dimnames = list(1:n,
#> p_dimnames))
#> }, p_min = p_min, p_max = p_max, p_dimnames = p_dimnames)),
#> class = c("UNIFORM", "DISTRIBUTION"))
#> }
#> <bytecode: 0x559e87eb41d8>
#> <environment: namespace:convdistr>
```

The function should return an structure which consist of a list of four named vector and a class attribute that should include “DISTRIBUTION” The components of the list are:

**distribution** : A character with the name of the
distribution implemented. It is a good practice to use the same name
also in the class attribute

**seed** : A numerical seed that is use to get a
repeatable sample in the `summary`

function Use a
`sample`

function to ensure each new object will have a
different seed

**oval** : The observed value. It is the value expected.
It is used as a number for the mathematical operations of the
distributions as if they were a simple scalar

**rfunc(n)** : A function that generate random numbers
from the distribution. Its only parameter `n`

is the number
of drawns of the distribution. It returns a matrix with as many rows as
`n`

, and as many columns as the dimensions of the
distributions, The `rfunc`

should be defined within a
`restrict_environment`

function to ensure only the variables
required to the function are included in the environment of the
function. This will prevent *reference leaking* which could make
the objects big and slow when saved in a file.

The factory of object for the sample size of proportions could be defined as:

```
#' Sample size for proportions, with uncertainty
#'
#' Create an new DISTRIBUTION object that produce
#' random drawns of the estimated sample size for
#' two proportions
#' @param p0 a DISTRIBUTION object that drawns for proportions in control group
#' @param logrr a DISTRIBUTION object that drawns log(RR) of the intervention
#' @param alpha significant value
#' @param beta 1-power
new_SAMPLE_SIZE <- function(p0,logrr, alpha= 0.05, beta=0.2){
#checkings
stopifnot(inherits(p0,"DISTRIBUTION"))
stopifnot(inherits(logrr,"DISTRIBUTION"))
stopifnot(0 < alpha & alpha < 1)
stopifnot(0 < beta & beta < 1)
# function of alpha and beta
f_alpha_beta <- (qnorm(alpha/2,lower.tail = F) + qnorm(beta, lower.tail = F)) ^ 2
# The expected value of the distribution with default name for the dimension
# based on the oval value of the individual distributions
v0 <- p0$oval
v1 <- p0$oval*exp(logrr$oval)
# expected value of the distribution
.oval <- f_alpha_beta * (v0 * (1 - v0) + v1 * (1 - v1)) / (v0 - v1) ^ 2
names(.oval) <- "rvar"
#random function within a restricted environment where only
#the specified variables can be accesed within the function
.rfunc <- restrict_environment(
function(n) {
d_p0 = rfunc_p0(n)
d_p1 = d_p0 * exp(rfunc_logrr(n))
fab * (d_p0 * (1 - d_p0) + d_p1 * (1 - d_p1)) / (d_p0 - d_p1) ^ 2
},
rfunc_p0 = p0$rfunc,
rfunc_logrr = logrr$rfunc,
fab = f_alpha_beta
)
# Create the object with 4 slots
structure(
list(
distribution = "SAMPLE_SIZE",
seed = sample(1:2 ^ 15, 1),
oval = .oval,
rfunc = .rfunc
),
class = c("SAMPLE_SIZE","DISTRIBUTION")
)
}
```

As example, suppose that the incidence of anaemia in children under 5 have been estimated from a survey as 35% with 95%CI (30%,40%), and a prevention treatment that in other settings have shown to decrease anaemia by 50% with 95%CI(8% to 73%).

We can define a `BETA`

distribution based on the CI as

And a `NORMAL`

distribution of the `log(RR)`

as

```
rr = log(1 - 50/100)
sd = (log((1 - 8 / 100)) - log((1 - 73 / 100))) / 4
d_logrr = new_NORMAL(rr,sd)
```

So the input distributions are

distribution | varname | oval | nsample | mean_ | sd_ | lci_ | median_ | uci_ |
---|---|---|---|---|---|---|---|---|

BETA | rvar | 0.35 | 10000 | 0.35 | 0.03 | 0.30 | 0.35 | 0.40 |

NORMAL | rvar | -0.69 | 10000 | -0.69 | 0.31 | -1.29 | -0.69 | -0.09 |

And base on this we can calculate the distribution of samples with those conditions

distribution | varname | oval | nsample | mean_ | sd_ | lci_ | median_ | uci_ |
---|---|---|---|---|---|---|---|---|

SAMPLE_SIZE | rvar | 95 | 10000 | 1515441 | 144696062 | 37 | 95 | 2436 |

The oval value of the distribution give us the result we obtain from
the formula. With the conditions specified, a sample size of
`95`

is required. However, the distribution is quite skewed
as you can see the mean and the median value are far, and the standard
deviation very big. This is because the sample size require for
combinations of low efficacy and low prevalence could be very large and
there is no restriction on how low could be the efficacy or the
prevalence, which although have low frequency, could happen in the
simulations.

```
rdrawn <- rfunc(d_sample, 10000)
f_ecdf <- ecdf(rdrawn)
n_80 = trunc(uniroot(function(x){f_ecdf(x) - 0.8}, interval = c(10,10000))$root)
```

The empirical cumulative distribution values of a random sample of
the distribution show us that with `204`

subjects per arm,
80% of the possible scenarios are covered

The function created above show us a distribution of sample size, but
we can’t inspect condition on which a particular drawn is obtained,
*i.e.* how low an prevalent anaemia value could be compensated
with a high efficacy value? To do that we could create a second object
that have drawns in several dimensions, the prevalence, the RR and the
sample size so we can make a graph with the three values.

```
#' Sample size for proportions, with uncertainty (V2)
#'
#' Create an new DISTRIBUTION object that produce
#' random drawns of the estimated sample size for
#' two proportions as well as the samples from the parameters
#' @param p0 a DISTRIBUTION object that drawns for proportions in control group
#' @param logrr a DISTRIBUTION object that drawns log(RR) of the intervention
#' @param alpha significant value
#' @param beta 1-power
new_SAMPLE_SIZE2 <- function(p0,logrr, alpha= 0.05, beta=0.2){
#checkings
stopifnot(inherits(p0,"DISTRIBUTION"))
stopifnot(inherits(logrr,"DISTRIBUTION"))
stopifnot(0 < alpha & alpha < 1)
stopifnot(0 < beta & beta < 1)
# function of alpha and beta
f_alpha_beta <- (qnorm(alpha/2,lower.tail = F) + qnorm(beta, lower.tail = F)) ^ 2
# The expected value of the distribution with default name for the dimension
# based on the oval value of the individual distributions
v0 <- p0$oval
v1 <- p0$oval*exp(logrr$oval)
# expected value of the distribution
ss <- f_alpha_beta * (v0 * (1 - v0) + v1 * (1 - v1)) / (v0 - v1) ^ 2
.oval = c(p0$oval, exp(logrr$oval), ss)
names(.oval) <- c('prevalence', 'rr', 'sample_size')
#random function within a restricted environment where only
#the specified variables can be accesed within the function
.rfunc <- restrict_environment(
function(n) {
d_p0 = rfunc_p0(n)
d_rr = exp(rfunc_logrr(n))
d_p1 = d_p0 * d_rr
d_ss = fab * (d_p0 * (1 - d_p0) + d_p1 * (1 - d_p1)) / (d_p0 - d_p1) ^ 2
matrix(
c(d_p0, d_rr, d_ss),
ncol = 3,
dimnames = list(1:n, c('prevalence','rr','sample_size')))
},
rfunc_p0 = p0$rfunc,
rfunc_logrr = logrr$rfunc,
fab = f_alpha_beta
)
# Create the object with 4 slots
structure(
list(
distribution = "SAMPLE_SIZE2",
seed = sample(1:2 ^ 15, 1),
oval = .oval,
rfunc = .rfunc
),
class = c("SAMPLE_SIZE2","DISTRIBUTION")
)
}
```

distribution | varname | oval | nsample | mean_ | sd_ | lci_ | median_ | uci_ |
---|---|---|---|---|---|---|---|---|

SAMPLE_SIZE2 | prevalence | 0.35 | 10000 | 0.35 | 2.00000e-02 | 0.30 | 0.35 | 0.40 |

SAMPLE_SIZE2 | rr | 0.50 | 10000 | 0.53 | 1.60000e-01 | 0.28 | 0.50 | 0.91 |

SAMPLE_SIZE2 | sample_size | 95.31 | 10000 | 5415076.49 | 5.39913e+08 | 37.70 | 95.82 | 2472.10 |

```
ss <- rfunc(d_sample2, 10000)
df <- data.frame(ss)
df$category = cut(df$sample_size, c(0,150,Inf),include.lowest = T, ordered_result = T)
head(df)
#> prevalence rr sample_size category
#> 1 0.3106612 0.4046465 74.35399 [0,150]
#> 2 0.3306757 0.4420427 79.80817 [0,150]
#> 3 0.3400101 0.7226961 361.76586 (150,Inf]
#> 4 0.3088985 0.5602841 151.70860 (150,Inf]
#> 5 0.3305111 0.5129411 109.66271 [0,150]
#> 6 0.3754060 0.4871143 81.28017 [0,150]
```

The following graph shows the combination of options that would lead to a sample size lower than 150 subjects

We see that under this particular set of conditions, it is possible to have more than 80% power in all prevalence range provided the relative risk is really lower than 50%.