MBNMAtime: Package Overview
Hugo Pedder
2024-07-03
mbnmatime-overview.RmdIntroduction
This vignette demonstrates how to use MBNMAtime to
perform meta-analysis of studies with multiple follow-up measurements in
order to account for time-course relationships within single or multiple
treatment comparisons. This can be performed by conducting Model-Based
(Network) Meta-Analysis (MBNMA) to pool relative treatment effects.
MBNMA models therefore estimate treatment effects over time (e.g. days,
weeks, months).
Including all available follow-up measurements within a study makes use of all the available evidence in a way that maintains connectivity between treatments and explains how the response of the treatment changes over time, thus accounting for heterogeneity and inconsistency that may be present from “lumping” together different time points in a standard Network Meta-Analysis (NMA). 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 4.3.1 or later is required if using R version >= 4.2 with Windows Operating Systems) (JAGS Computer Program 2017). For full details of time-course MBNMA methodology see Pedder et al. (2019), and a simulation study exploring the statistical properties of the method is reported in Pedder et al. (2020).
MBNMAtime provides a complete set of functions that
allow for meta-analysis of longitudinal time-course data and plotting of
a number of informative graphics. Functions are provided for ranking,
prediction, and for assessing consistency when modelling using relative
effects. The package allows for flexible modelling of either relative or
absolute effects interchangeably on different time-course parameters
within the same analysis, whilst providing a straightforward syntax with
which to define these models.
This package has been developed alongside MBNMAdose, a
package that allows users to perform dose-response MBNMA to allow for
modelling of dose-response relationships between different agents within
a network. 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.
Within the vignettes, some models have not been evaluated, or have been run with fewer iterations than would be necessary to achieve convergence and produce valid results in practice. This has been done to speed up computation and rendering of the vignettes.
Workflow within the package
Functions within MBNMAtime follow a clear pattern of
use:
- Load your data into the correct format using
mb.network()(Exploring the data - Specify a suitable time-course function and analyse your data using
mb.run()(Performing a time-course MBNMA - Test for consistency using functions like
mb.nodesplit()(Checking for consistency - Examine model outputs, such as relative effects, forest plots and treatment rankings (Model outputs
- Use your model to make predictions or estimate treatment effects at
specific time-points using
predict()(Predictions
At each of these stages there are a number of informative graphs that can be generated to help understand the data and make decisions regarding model fitting.
Datasets Included in the Package
Pain relief in osteoarthritis
osteopain is from a systematic review of treatments for
pain in osteoarthritis, used previously in Pedder et al. (2019). The outcome is pain measured on a
continuous scale, and aggregate data responses correspond to the mean
WOMAC pain score at different follow-up times. The dataset includes 30
Randomised-Controlled Trials (RCTs), comparing 29 different treatments
(including placebo). osteopain is a data frame in long
format (one row per time point, arm and study), with the variables
studyID, time, y,
se, treatment and arm.
| studyID | time | y | se | treatment | arm | treatname |
|---|---|---|---|---|---|---|
| Baerwald 2010 | 0 | 6.55 | 0.09 | Pl_0 | 1 | Placebo_0 |
| Baerwald 2010 | 2 | 5.40 | 0.09 | Pl_0 | 1 | Placebo_0 |
| Baerwald 2010 | 6 | 4.97 | 0.10 | Pl_0 | 1 | Placebo_0 |
| Baerwald 2010 | 13 | 4.75 | 0.11 | Pl_0 | 1 | Placebo_0 |
| Baerwald 2010 | 0 | 6.40 | 0.13 | Na_1000 | 2 | Naproxen_1000 |
| Baerwald 2010 | 2 | 4.03 | 0.13 | Na_1000 | 2 | Naproxen_1000 |
Alogliptin for lowering blood glucose concentration in type II diabetes
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 Randomised-Controlled Trials (RCTs),
comparing 5 different doses of alogliptin with placebo (6 different
treatments in total). alog_pcfb is a data frame in long
format (one row per time point, arm and study), with the variables
studyID, clinicaltrialGov_ID,
agent, dose, treatment,
time, y, se, and
N.
| studyID | clinicaltrialGov_ID | agent | dose | treatment | time | y | se | n |
|---|---|---|---|---|---|---|---|---|
| 1 | NCT01263470 | alogliptin | 0.00 | placebo | 2 | 0.00 | 0.02 | 75 |
| 1 | NCT01263470 | alogliptin | 6.25 | alog_6.25 | 2 | -0.16 | 0.02 | 79 |
| 1 | NCT01263470 | alogliptin | 12.50 | alog_12.5 | 2 | -0.17 | 0.02 | 84 |
| 1 | NCT01263470 | alogliptin | 25.00 | alog_25 | 2 | -0.16 | 0.02 | 79 |
| 1 | NCT01263470 | alogliptin | 50.00 | alog_50 | 2 | -0.15 | 0.02 | 79 |
| 1 | NCT01263470 | alogliptin | 0.00 | placebo | 4 | -0.01 | 0.04 | 75 |
Tiotropium, Aclidinium and Placebo for maintenance treatment of moderate to severe chronic obstructive pulmonary disease
A dataset from a systematic review of Randomised-Controlled Trials
(RCTs) for maintenance treatment of moderate to severe chronic
obstructive pulmonary disease (COPD) (Karabis et
al. 2013). Data are extracted from (Tallarita, De lorio, and Baio 2019). SEs were
imputed for three studies, and number of patients randomised were
imputed for one study (LAS 39) in which they were missing, using the
median standard deviation calculated from other studies in the dataset.
The outcome is trough Forced Expiratory Volume in 1 second (FEV1),
measured in litres and reported in each study arm as mean change from
baseline to follow-up. The dataset includes 13 RCTs, comparing 2
treatments (Tiotropium and Aclidinium) and placebo. copd is
a data frame in long format (one row per observation, arm and study),
with the variables studyID, time,
y, se, treatment, and
n.
| studyID | time | y | se | treatment | n |
|---|---|---|---|---|---|
| ACCORD I | 1 | -0.01 | 0.01 | Placebo | 187 |
| ACCORD I | 4 | -0.01 | 0.01 | Placebo | 187 |
| ACCORD I | 8 | -0.01 | 0.01 | Placebo | 187 |
| ACCORD I | 12 | -0.02 | 0.01 | Placebo | 187 |
| ACCORD I | 1 | 0.10 | 0.01 | Aclidinium | 190 |
| ACCORD I | 4 | 0.11 | 0.01 | Aclidinium | 190 |
Body weight reduction in obesity patients
obesityBW_CFB is from a systematic review of
pharmacological treatments for obesity. The outcome measured is change
from baseline in body weight (kg) at different follow-up times. 35 RCTs
are included that investigate 26 different treatments (16 agents/agent
combinations compared at different doses). obesityBW_CFB is
a dataset in long format (one row per time point, arm and study), with
the variables studyID, time, y,
se, N, treatment,
arm, treatname, agent and
class.
class is the class of a particular agent
(e.g. Lipase inhibitor)
| studyID | time | y | se | n | treatment | treatname | agent | class | |
|---|---|---|---|---|---|---|---|---|---|
| 27 | Apfelbaum 1999 | 4 | -1.00 | 0.39 | 78 | plac | placebo | placebo | Placebo |
| 28 | Apfelbaum 1999 | 4 | -1.59 | 0.38 | 81 | sibu_10MG | sibutramine 10MG | sibutramine | SNRI |
| 29 | Apfelbaum 1999 | 9 | -1.59 | 0.40 | 78 | plac | placebo | placebo | Placebo |
| 30 | Apfelbaum 1999 | 9 | -3.01 | 0.39 | 81 | sibu_10MG | sibutramine 10MG | sibutramine | SNRI |
| 31 | Apfelbaum 1999 | 13 | -2.25 | 0.41 | 78 | plac | placebo | placebo | Placebo |
| 32 | Apfelbaum 1999 | 13 | -4.76 | 0.40 | 81 | sibu_10MG | sibutramine 10MG | sibutramine | SNRI |
Serum uric acid concentration in gout
goutSUA_CFB 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 different follow-up times. The dataset includes 28 RCTs,
comparing 41 treatments (8 agents compared at different doses).
goutSUA_CFB is a data frame in long format (one row per arm
and study), with the variables studyID, time,
y, se, treatment,
arm, class and treatname.
| studyID | time | y | se | treatment | treatname | class |
|---|---|---|---|---|---|---|
| 1102 | 1 | 0.07 | 0.25 | RDEA_100 | RDEA594_100 | RDEA |
| 1102 | 1 | 0.02 | 0.18 | RDEA_200 | RDEA594_200 | RDEA |
| 1102 | 1 | 0.06 | 0.25 | RDEA_400 | RDEA594_400 | RDEA |
| 1102 | 2 | -0.53 | 0.25 | RDEA_100 | RDEA594_100 | RDEA |
| 1102 | 2 | -1.37 | 0.18 | RDEA_200 | RDEA594_200 | RDEA |
| 1102 | 2 | -1.73 | 0.25 | RDEA_400 | RDEA594_400 | RDEA |