This vignette demonstrates how to use
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 (Lu and Ades 2004) and are run in JAGS (version 4.3.0 or later is required) (2017). For full details of dose-response MBNMA methodology see Mawdsley et al. (2016). Throughout this vignette we refer to a treatment as a specific dose or a specific agent
This package has been developed alongside
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.
MBNMAdose follow a clear pattern of
mbnma.network()and explore potential relationships (Exploring the data
mbnma.run()(Performing a dose-response MBNMA. Modelling of effect modifying covariates is also possibly using Network Meta-Regression.
nma.run()(Checking for consistency
At 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 (Thorlund et al.
2014). 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
|1||Tfelt-Hansen P 2006||22||6||0||placebo|
|1||Tfelt-Hansen P 2006||30||14||1||sumatriptan|
|2||Goadsby PJ 2007||467||213||1||almotriptan|
|2||Goadsby PJ 2007||472||229||1||zolmitriptan|
There are 3 psoriasis datasets from a systematic review of RCTs
comparing biologics at different doses and placebo (Warren et al. 2019). 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 \(\geq75\%\) (
psoriasis90), or \(100\%\) (
ssri is from a systematic review examining the efficacy
of different doses of SSRI antidepressant drugs and placebo (Furukawa et al. 2019). 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.
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
gout is a data frame in long format (one row per
arm and study), with the variables
osteopain is from a systematic review of interventions
for pain relief in osteoarthritis, used previously in Pedder et al.
(2019). 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.
a data frame in long format (one row per arm and study), with the
alog_pcfb is from a systematic review of
Randomised-Controlled Trials (RCTs) comparing different doses of
alogliptin with placebo (Langford et al.
2016). 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
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