View source: R/time.functions.R
tfpoly | R Documentation |
As first described for use in Network Meta-Analysis by \insertCitejansen2015;textualMBNMAtime.
tfpoly(
degree = 1,
pool.1 = "rel",
method.1 = "common",
pool.2 = "rel",
method.2 = "common",
method.power1 = 0,
method.power2 = 0
)
degree |
The degree of the fractional polynomial as defined in \insertCiteroyston1994;textualMBNMAtime |
pool.1 |
Pooling for the 1st fractional polynomial coefficient. Can take |
method.1 |
Method for synthesis of the 1st fractional polynomial coefficient. Can take |
pool.2 |
Pooling for the 2nd fractional polynomial coefficient. Can take |
method.2 |
Method for synthesis of the 2nd fractional polynomial coefficient. Can take |
method.power1 |
Value for the 1st fractional polynomial power. Must take any numeric value in the set |
method.power2 |
Value for the 2nd fractional polynomial power. Must take any numeric value in the set |
\beta_1
represents the 1st coefficient.
\beta_2
represents the 2nd coefficient.
p_1
represents the 1st power
p_2
represents the 2nd power
For a polynomial of degree=1
:
{\beta_1}x^{p_1}
For a polynomial of degree=2
:
{\beta_1}x^{p_1}+{\beta_2}x^{p_2}
x^{(p)}
is a regular power except where p=0
, where x^{(0)}=ln(x)
.
If a fractional polynomial power p_m
repeats within the function it is multiplied by another ln(x)
.
An object of class("timefun")
Time-course parameters in the model must be specified using a pool
and a method
prefix.
pool
is used to define the approach used for pooling of a given time-course parameter and
can take any of:
Argument | Model specification |
"rel" | Indicates that relative effects should be pooled for this time-course parameter. Relative effects preserve randomisation within included studies, are likely to vary less between studies (only due to effect modification), and allow for testing of consistency between direct and indirect evidence. Pooling follows the general approach for Network Meta-Analysis proposed by \insertCitelu2004;textualMBNMAtime. |
"abs" | Indicates that study arms should be pooled across the whole network for this time-course parameter independently of assigned treatment to estimate an absolute effect. This implies estimating a single value across the network for this time-course parameter, and may therefore be making strong assumptions of similarity. |
method
is used to define the model used for meta-analysis for a given time-course parameter
and can take any of the following values:
Argument | Model specification |
"common" | Implies that all studies estimate the same true effect (often called a "fixed effect" meta-analysis) |
"random" | Implies that all studies estimate a separate true effect, but that each of these true effects vary randomly around a true mean effect. This approach allows for modelling of between-study heterogeneity. |
numeric() | Assigned a numeric value, indicating that this time-course parameter should not be estimated from the data but should be assigned the numeric value determined by the user. This can be useful for fixing specific time-course parameters (e.g. Hill parameters in Emax functions, power parameters in fractional polynomials) to a single value. |
When relative effects are modelled on more than one time-course parameter,
correlation between them is automatically estimated using a vague inverse-Wishart prior.
This prior can be made slightly more informative by specifying the scale matrix omega
and by changing the degrees of freedom of the inverse-Wishart prior
using the priors
argument in mb.run()
.
# 1st order fractional polynomial with random effects
tfpoly(pool.1="rel", method.1="random")
# 2nd order fractional polynomial
# with a single absolute parameter estimated for the 2nd coefficient
# 1st power equal to zero
tfpoly(degree=2, pool.1="rel", method.1="common",
pool.2="abs", method.2="random",
method.power1=0)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.