dmphillippo / multinma

Network meta-analysis of individual and aggregate data in Stan
https://dmphillippo.github.io/multinma
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No parameter "beta_aux" #44

Open dkarletsos opened 1 month ago

dkarletsos commented 1 month ago

Hi,

The following code works when likelihood is set to 'msplines' but returns the error below when 'exponential' or 'weibull' or any other likelihood that is not 'mspline'. Am I missing any settings in nma() ?

CODE:

    fit <- nma(net,
               regression = ~(covariate1 + covariate2)*.trt,
               likelihood = likelihood,
               prior_intercept = normal(0, 100),
               prior_trt = normal(0, 100),
               prior_reg = normal(0, 100),
               prior_aux = half_normal(1),
               QR = TRUE,
               aux_regression = ~.trt)

    refdat <- (...)

    hazard_ratios <- multinma::marginal_effects(fit,
                                                type = "hazard",
                                                mtype = "ratio",
                                                baseline = "Study-1",
                                                aux = "Study-1",
                                                newdata = refdat,
                                                times = refdat$times,
                                                all_contrasts = FALSE,
                                                trt_ref = reference_treatment)

ERROR:

Error in `as.array()`:
! No parameter "beta_aux".
Run `rlang::last_trace()` to see where the error occurred.

> rlang::last_trace()
<error/rlang_error>
Error in `as.array()`:
! No parameter "beta_aux".
---
Backtrace:
    ▆
 1. ├─multinma::marginal_effects(...)
 2. │ └─rlang::eval_tidy(...)
 3. ├─stats (local) `<fn>`(...)
 4. ├─multinma:::predict.stan_nma_surv(...)
 5. ├─base::NextMethod(...)
 6. └─multinma:::predict.stan_nma(...)
 7.   ├─base::as.array(object, pars = "beta_aux")
 8.   └─multinma:::as.array.stan_nma(object, pars = "beta_aux")
dmphillippo commented 1 month ago

Hi @dkarletsos, thanks for reporting this.

I can recreate the error with likelihood = "exponential"; the issue is caused by predict() trying to access the auxiliary regression parameters beta_aux when there are none, because the exponential model has no shape parameters. Both aux_regression and aux_by have no effect for the exponential model, and are ignored.

This is fixed in the latest development version, and I have also added informative warning messages when aux_regression or aux_by are being ignored (e.g. for the exponential model).

You can install this latest version from r-universe:

install.packages("multinma", repos = c("https://dmphillippo.r-universe.dev", getOption("repos")))

I don't see errors for any other likelihoods, these are behaving as expected in my testing. Are you still seeing other errors?