library(MBNMAdose) #devtools::load_all() library(rmarkdown) library(knitr) library(dplyr) knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 7, fig.height = 5, include=TRUE, tidy.opts=list(width.cutoff=80), tidy=TRUE )
This vignette demonstrates how to use MBNMAdose
to perform Model-Based
Network Meta-Analysis (MBNMA) of studies with multiple doses of
different agents by accounting for the dose-response relationship. This
can connect disconnected networks via the dose-response relationship and
the placebo response, improve precision of estimated effects and allow
interpolation/extrapolation of predicted response based on the
dose-response relationship.
Modelling the dose-response relationship also avoids the "lumping" of different doses of an agent which is often done in Network Meta-Analysis (NMA) and can introduce additional heterogeneity or inconsistency. All models and analyses are implemented in a Bayesian framework, following an extension of the standard NMA methodology presented by [@lu2004] and are run in JAGS (version 4.3.0 or later is required) [-@jags]. For full details of dose-response MBNMA methodology see Mawdsley et al. [-@mawdsley2016]. Throughout this vignette we refer to a treatment as a specific dose or a specific agent
This package has been developed alongside MBNMAtime
, a package that
allows users to perform time-course MBNMA to incorporate multiple time
points within different studies. However, they should not be loaded
into R at the same time as there are a number of functions with shared
names that perform similar tasks yet are specific to dealing with either
time-course or dose-response data.
Functions within MBNMAdose
follow a clear pattern of use:
mbnma.network()
and explore potential relationships (Exploring the datambnma.run()
(Performing a dose-response MBNMA. Modelling of effect modifying covariates is also possibly using Network Meta-Regression.nma.nodesplit()
and nma.run()
(Checking for consistencypredict()
(PredictionsAt each of these stages there are a number of informative plots that can be generated to help understand the data and to make decisions regarding model fitting.
triptans
is from a systematic review of interventions for pain relief
in migraine [@thorlund2014]. The outcome is binary, and represents (as
aggregate data) the number of participants who were headache-free at 2
hours. Data are from patients who had had at least one migraine attack,
who were not lost to follow-up, and who did not violate the trial
protocol. The dataset includes 70 Randomised-Controlled Trials (RCTs),
comparing 7 triptans with placebo. Doses are standardised as relative to
a "common" dose, and in total there are 23 different treatments
(combination of dose and agent). triptans
is a data frame in long
format (one row per arm and study), with the variables studyID
,
AuthorYear
, N
, r
, dose
and agent
.
kable(head(triptans), digits=2)
There are 3 psoriasis datasets from a systematic review of RCTs
comparing biologics at different doses and placebo [@warren2019]. Each
dataset contains a different binary outcome, all based on the number of
patients experiencing degrees of improvement on the Psoriasis Area and
Severity Index (PASI) measured at 12 weeks follow-up. Each dataset
contains information on the number of participants who achieved
$\geq75\%$ (psoriasis75
), $\geq90\%$ (psoriasis90
), or $100\%$
(psoriasis100
).
ssri
is from a systematic review examining the efficacy of different
doses of SSRI antidepressant drugs and placebo [@furukawa2019]. The
response to treatment is defined as a 50% reduction in depressive
symptoms after 8 weeks (4-12 week range) follow-up. The dataset includes
60 RCTs comparing 5 different SSRIs with placebo.
kable(head(ssri), digits=2)
gout
is from a systematic review of interventions for lowering Serum
Uric Acid (SUA) concentration in patients with gout [not published
previously]. The outcome is continuous, and aggregate data responses
correspond to the mean change from baseline in SUA in mg/dL at 2 weeks
follow-up. The dataset includes 10 Randomised-Controlled Trials (RCTs),
comparing 5 different agents, and placebo. Data for one agent (RDEA)
arises from an RCT that is not placebo-controlled, and so is not
connected to the network directly. In total there were 19 different
treatments (combination of dose and agent). gout
is a data frame in
long format (one row per arm and study), with the variables studyID
,
y
, se
, agent
and dose
.
kable(head(gout), digits=2)
osteopain
is from a systematic review of interventions for pain relief
in osteoarthritis, used previously in Pedder et al. [-@pedder2019]. The
outcome is continuous, and aggregate data responses correspond to the
mean WOMAC pain score at 2 weeks follow-up. The dataset includes 18
Randomised-Controlled Trials (RCTs), comparing 8 different agents with
placebo. In total there were 26 different treatments (combination of
dose and agent). The active treatments can also be grouped into 3
different classes, within which they have similar mechanisms of action.
osteopain_2wkabs
is a data frame in long format (one row per arm and
study), with the variables studyID
, agent
, dose
, class
, y
,
se
, and N
.
kable(head(osteopain), digits=2)
alog_pcfb
is from a systematic review of Randomised-Controlled Trials
(RCTs) comparing different doses of alogliptin with placebo
[@langford2016]. The systematic review was simply performed and was
intended to provide data to illustrate a statistical methodology rather
than for clinical inference. Alogliptin is a treatment aimed at reducing
blood glucose concentration in type II diabetes. The outcome is
continuous, and aggregate data responses correspond to the mean change
in HbA1c from baseline to follow-up in studies of at least 12 weeks
follow-up. The dataset includes 14 RCTs, comparing 5 different doses of
alogliptin with placebo, leading to 6 different treatments (combination
of dose and agent) within the network. alog_pcfb
is a data frame in
long format (one row per arm and study), with the variables studyID
,
agent
, dose
, y
, se
, and N
.
kable(head(alog_pcfb), digits=2)
ssi_closure
is from a systematic review examining the efficacy of different wound closure methods to reduce Surgical Site Infections (SSI). The outcome is binary and represents the number of patients who experienced a SSI. The dataset includes 129 RCTs comparing 16 different interventions in 6 classes. This dataset is primarily used to illustrate how MBNMAdose
can be used to perform different types of network meta-analysis without dose-response information. It is in long format (one row per study arm) and includes the variables studyID
, Year
, n
, r
, trt
and class
.
kable(head(ssi_closure), digits=2)
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