[R-meta] standard error in predictive nonlinear meta-regression model
I would suggest you use the multivariate Delta method: https://en.wikipedia.org/wiki/Delta_method#Multivariate_delta_method You will have to compute some partial derivatives, which is not complicated in your case if you have ever done it before. Be warned though that, sometimes the Delta method approximations do not perform well (and can underestimate the standard error). An alternative would be to bootstrap the meta-analysis. -Philipp On Thu, Jan 18, 2018 at 6:07 PM, Cesar Terrer Moreno <cesar.terrer at me.com> wrote:
I am using a nonlinear meta-regression of the form y ~ p1 * exp(-p2*A),
with the following coefficients:
estimate se zval pval
p1 5.5447 4.2592 1.302 0.19298
p2 0.3856 0.0835 4.616 0.00000
tau2 0.0000 NA NA NA
I want to use this model to predict the effect size (in %) of a treatment,
influenced by the variable A. Let's assume, for example, this is the
effectiveness of a cancer treatment at a certain age A. For example, when
A= 9, effect size is ~19%:
make_pct <- function(x) (exp(x) - 1) * 100
make_pct(5.5447 * exp (-0.3856 * 9))
[1] 18.82309
The problem is that now I don't know how to calculate the standard error
at e.g. A=9 using this model. I am sure this is very basic but I don't have
any experience in this particular matter. Thanks
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