[R-meta] Goodness of fit test for dose-response meta-analysis

Dear all,

I am currently working on random effect one-stage dose-response 
meta-analysis (DRMA), and I am trying to assess goodness of fit using 
the R package "dosresmeta".

Here are the R code I used and the results:

*# Goodness fits statistics for Random effect quadratic polynomial model*
library(dosresmeta)
data(alcohol_cvd)

lin.R <- dosresmeta(formula = logrr ~ dose+I(dose^2), type = type, id = 
id, ?se = se, cases = cases, n = n,proc = "1stage", data = alcohol_cvd, 
method="reml", control = list(maxiter =1000))

 >*gof(lin.R, fixed =FALSE)* Goodness-of-fit statistics: Deviance test: 

D = 19.920 (df = 17), p-value = 0.278????????????
*# Goodness fits statistics for fixed effect quadratic polynomial model*
lin.F <- dosresmeta(formula = logrr ~ dose+I(dose^2), type = type, id = 
id, ?se = se, cases = cases, n = n,proc = "1stage", data = alcohol_cvd, 
method="fixed")

 >*gof(lin.F)* Goodness-of-fit statistics: Deviance test: D = 40.992 (df 

= 17), p-value = 0.001
The deviances are different for fixed vs. random but the degrees of 
freedom are identical. Since the random effect model includes the 
between study variance-covariance components (which should be 
estimated),, I expected the degrees of freedom for random effect to be 
smaller.

I would appreciate if anyone?could share their thoughts.

Warm Regards,

*Marimuthu S,*
Ph.D. Student (Biostatistics)



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