---
title: "MBNMAdose: Calculating model predictions"
author: "Hugo Pedder"
date: "`r Sys.Date()`"
output:
knitr:::html_vignette:
toc: TRUE
bibliography: REFERENCES.bib
vignette: >
%\VignetteIndexEntry{MBNMAdose: Calculating model predictions}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
library(MBNMAdose)
#devtools::load_all()
library(rmarkdown)
library(knitr)
library(dplyr)
library(ggplot2)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 7,
fig.height = 5,
include=TRUE,
tidy.opts=list(width.cutoff=80),
tidy=TRUE
)
```
## Prediction
For calculating predictions from MBNMAdose we will demonstrate using
results from an Emax MBNMA on the triptans dataset:
```{r, results="hide"}
tripnet <- mbnma.network(triptans)
trip.emax <- mbnma.run(tripnet, fun=demax(emax="rel", ed50="rel"))
```
After performing an MBNMA, responses can be predicted from the model
parameter estimates using `predict()` on an `"mbnma"` object. A number
of important arguments should be specified for prediction. See
`?predict.mbnma` for detailed specification of these arguments.
`E0` This is the response at dose = 0 (equivalent to the placebo
response). Since relative effects are the parameters estimated in MBNMA,
the placebo response is not explicitly modelled and therefore must be
provided by the user in some way. The simplest approach is to provide
either a single numeric value for `E0` (deterministic approach), or a
string representing a distribution for `E0` that can take any Random
Number Generator (RNG) distribution for which a function exists in R
(stochastic approach). Values should be given on the natural scale. For
example for a binomial outcome:
- Deterministic: `E0 <- 0.2`
- Stochastic: `E0 <- "rbeta(n, shape1=2, shape2=10)"`
Another approach is to estimate `E0` from a set of studies. These would
ideally be studies of untreated/placebo-treated patients that closely
resemble the population for which predictions are desired, and the
studies may be observational. However, synthesising results from the
placebo arms of trials in the original network is also possible. For
this, `E0` is assigned a data frame of studies in the long format (one
row per study arm) with the variables `studyID`, and a selection of `y`,
`se`, `r`, `n` and `E` (depending on the likelihood used in the MBNMA
model). `synth` can be set to `"fixed"` or `"random"` to indicate
whether this synthesis should be fixed or random effects.
```{r, eval=FALSE}
E0 <- triptans[triptans$dose==0,]
```
Additionally, it's also necessary to specify the doses at which to
predict responses. By default, `predict()` uses the maximum dose within
the dataset for each agent, and predicts doses at a series of cut
points. The number of cut points can be specified using `n.doses`, and
the maximum dose to use for prediction for each agent can also be
specified using `max.doses` (a named list of numeric values where
element names correspond to agent names).
An alternative approach is to predict responses at specific doses for
specific agents using the argument `exact.doses`. As with `max.doses`,
this is a named list in which element names correspond to agent names,
but each element is a numeric vector in which each value within the
vector is a dose at which to predict a response for the given agent.
```{r}
# Predict 20 doses for each agent, with a stochastic distribution for E0
doses <- list("Placebo"=0,
"eletriptan"=3,
"sumatriptan"=3,
"almotriptan"=3,
"zolmitriptan"=3,
"naratriptan"=3,
"rizatriptan"=3)
pred <- predict(trip.emax, E0="rbeta(n, shape1=2, shape2=10)",
max.doses=doses, n.dose=20)
# Predict exact doses for two agents, and estimate E0 from the data
E0.data <- triptans[triptans$dose==0,]
doses <- list("eletriptan"=c(0,1,3),
"sumatriptan"=c(0,3))
```
```{r, results="hide"}
pred <- predict(trip.emax, E0=E0.data,
exact.doses=doses)
```
An object of class `"mbnma.predict"` is returned, which is a list of
summary tables and MCMC prediction matrices for each treatment
(combination of dose and agent). The `summary()` method can be used to
print mean posterior predictions at each time point for each treatment.
```{r}
summary(pred)
```
### Plotting predictions
Predictions can also be plotted using the `plot()` method on an
object of `class("mbnma.predict")`. The predicted responses are joined
by a line to form the dose-response curve for each agent predicted, with
95% credible intervals (CrI). Therefore, when plotting the response it
is important to predict a sufficient number of doses (using `n.doses`)
to get a smooth curve.
```{r}
# Predict responses using default doses up to the maximum of each agent in the dataset
pred <- predict(trip.emax, E0=0.2, n.dose=20)
plot(pred)
```
Shaded counts of the number of studies in the original dataset that
investigate each dose of an agent can be plotted over the 95% CrI for
each treatment by setting `disp.obs = TRUE`, though this requires that
the original `"mbnma.network"` object used to estimate the MBNMA be
provided via `network`.
```{r}
plot(pred, disp.obs = TRUE)
```
This can be used to identify any extrapolation/interpretation of the
dose-response that might be occurring for a particular agent. As you can
see, more observations typically leads to tighter 95% CrI for the
predicted response at a particular point along the dose-response curve.
We can also plot the results of a "split" Network Meta-Analysis (NMA) in
which all doses of an agent are assumed to be independent. As with
`disp.obs` we also need to provide the original `mbnma.network` object
to be able to estimate this, and we can also specify if we want to
perform a common or random effects NMA using `method`. Treatments that
are only connected to the network via the dose-response relationship
(rather than by a direct head-to-head comparison) will not be included.
```{r, results="hide", warning=FALSE, message=FALSE}
alognet <- mbnma.network(alog_pcfb)
alog.emax <- mbnma.run(alognet, fun=demax(), method="random")
pred <- predict(alog.emax, E0=0, n.dose=20)
plot(pred, overlay.split = TRUE, method="random")
```
By plotting these, as well as observing how responses can be
extrapolated/interpolated, we can also see which doses are likely to be
providing most information to the dose-response relationship. The
tighter 95% CrI on the predicted responses from the MBNMA also show that
modelling the dose-response function also gives some additional
precision even at doses for which there is information available.
More detailed documentation can be accessed using `?plot.mbnma.predict`.
### Ranking predicted responses
Predicted responses from an object of `class("mbnma.predict")` can also
be ranked using the `rank()` method. As when applied to an object of
`class("mbnma")`, this method will rank parameters (in this case
predictions) in order from either highest to lowest (`direction=1`) or
lowest to highest (`direction=-1`), and return an object of
`class("mbnma.rank")`.
If there have been predictions at dose = 0 for several agents only one
of these will be included in the rankings, in order to avoid duplication
(since the predicted response at dose = 0 is the same for all agents).
```{r}
pred <- predict(trip.emax, E0=0.2, n.doses=4,
max.doses = list("eletriptan"=5, "sumatriptan"=5,
"frovatriptan"=5, "zolmitriptan"=5))
ranks <- rank(pred)
plot(ranks)
```
## References