Problems with estimating a proportional error with SAEM

[Dorota-D]

Hello,

I have some problems with fitting a model with a proportional error using saem. At first, I thought it was the problem with my data, but it also occurs with the theo_sd dataset. I've used a simple 1-cmt model for this example:

one.cmt <- function() { ini({ tka <- 0.45 tcl <- log(c(0, 2.7, 100)) tv <- 3.45 eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 prop.sd <- 0.7 }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ prop(prop.sd) }) } fit <- nlmixr(one.cmt, theo_sd, list(print=5), est="saem")

The first couple of iterations look like this: 1: 0.1287 1.2322 3.6174 0.5700 0.2850 0.0950 inf 5: 0.1287 1.2322 3.6174 0.4643 0.2321 0.0774 nan 10: 0.1287 1.2322 3.6174 0.3618 0.1796 0.0602 nan

Interestingly, the proportional error works fine with focei (at least for theo_sd, but my data is more complex). The additive error seems to be ok. When I tried using a combined error, it converged, and the parameters were more or less similar to the ones obtained in Monolix. However, when I observed gradients, I noticed that the proportional error estimates are updated last. This is the output from theo_sd with 1cmt model and the combined error:

1: -0.1035 1.2020 3.5329 0.5700 0.2850 0.1008 8.7751 1.0000 5: -0.2479 1.9252 3.8812 0.6075 0.2904 0.0821 4.6754 1.0000 10: -0.0070 1.9399 3.8867 0.4998 0.2247 0.0957 1.5933 1.4400 15: -0.2010 1.9586 4.1084 0.4667 0.1871 0.0984 0.7435 1.6900 20: -0.1121 1.8804 4.1319 0.3718 0.1447 0.0761 0.5811 1.6900

Is it ok, or are there some problems with the proportional error & saem in the combined model too?

I really appreciate any help you can provide.

[mattfidler]

It is Ok with the combined model.

SAEM didn't swap out a variance of 0 to 1 like focei did; This has been changed since it is common practice to swap these with 1;

With smaller datasets, like theo_sd this can cause an over-inflation of the residuals were the prediction is zero, and optimizing for that value; As always only accept a fit if the diagnostics are reasonable.

library(nlmixr)
one.cmt <- function() {
  ini({
    tka <- 0.45
    tcl <- log(c(0, 2.7, 100))
    tv <- 3.45
    eta.ka ~ 0.6
    eta.cl ~ 0.3
    eta.v ~ 0.1
    prop.sd <- 0.1
  })
  model({
    ka <- exp(tka + eta.ka)
    cl <- exp(tcl + eta.cl)
    v <- exp(tv + eta.v)
    linCmt() ~ prop(prop.sd)
  })
}

fit <- nlmixr(one.cmt, theo_sd, list(print=0), est="saem")
#> RxODE 1.0.5 using 4 threads (see ?getRxThreads)
#> Calculating covariance matrix
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
#> Error : Error calculating covariance via linearization
#> Calculating residuals/tables
#> done
#> Warning in (function (uif, data, est = NULL, control = list(), ..., sum.prod
#> = FALSE, : Covariance matrix non-positive definite, corrected by sqrtm(fim %*%
#> fim)
#> Warning in (function (uif, data, est = NULL, control = list(), ..., sum.prod =
#> FALSE, : Linearization of FIM could not be used to calculate covariance.
#> Warning in (function (uif, data, est = NULL, control = list(), ..., sum.prod =
#> FALSE, : NAs introduced by coercion
#> Warning in (function (uif, data, est = NULL, control = list(), ..., sum.prod =
#> FALSE, : Bounds are ignored in SAEM
print(fit)
#> ── nlmixr SAEM(Solved); OBJF not calculated fit ────────────────────────────────
#> 
#>  Gaussian/Laplacian Likelihoods: AIC() or $objf etc. 
#>  FOCEi CWRES & Likelihoods: addCwres() 
#> 
#> ── Time (sec $time): ───────────────────────────────────────────────────────────
#> 
#>          saem    setup table covariance    other
#> elapsed 0.734 0.879383 0.008      0.006 0.386617
#> 
#> ── Population Parameters ($parFixed or $parFixedDf): ───────────────────────────
#> 
#>          Est.     SE %RSE Back-transformed(95%CI) BSV(CV%) Shrink(SD)%
#> tka     0.406  0.191   47        1.5 (1.03, 2.18)     70.3     0.684% 
#> tcl      1.01  0.071 7.03       2.75 (2.39, 3.16)     24.3     0.705% 
#> tv       3.47 0.0374 1.08       32.2 (29.9, 34.7)     11.3      20.6% 
#> prop.sd 0.171                               0.171                     
#>  
#>   Covariance Type ($covMethod): |fim|
#>   Fixed parameter correlations in $cor
#>   No correlations in between subject variability (BSV) matrix
#>   Full BSV covariance ($omega) or correlation ($omegaR; diagonals=SDs) 
#>   Distribution stats (mean/skewness/kurtosis/p-value) available in $shrink 
#> 
#> ── Fit Data (object is a modified tibble): ─────────────────────────────────────
#> # A tibble: 132 x 16
#>   ID     TIME    DV  PRED    RES IPRED   IRES  IWRES eta.ka eta.cl   eta.v    ka
#>   <fct> <dbl> <dbl> <dbl>  <dbl> <dbl>  <dbl>  <dbl>  <dbl>  <dbl>   <dbl> <dbl>
#> 1 1     0      0.74  0     0.74   0     0.74   0.74  0.0871 -0.500 -0.0546  1.64
#> 2 1     0.25   2.84  3.07 -0.233  3.50 -0.659 -1.10  0.0871 -0.500 -0.0546  1.64
#> 3 1     0.570  6.57  5.56  1.01   6.25  0.317  0.297 0.0871 -0.500 -0.0546  1.64
#> # … with 129 more rows, and 4 more variables: cl <dbl>, v <dbl>, tad <dbl>,
#> #   dosenum <dbl>

fit <- nlmixr(one.cmt, theo_sd, list(print=0), est="focei")
#> calculating covariance matrix
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
#> done
#> Calculating residuals/tables
#> done
#> Warning in (function (uif, data, est = NULL, control = list(), ...,
#> sum.prod = FALSE, : Hessian reset during optimization; (can control by
#> foceiControl(resetHessianAndEta=.))
#> Warning in (function (uif, data, est = NULL, control = list(), ..., sum.prod
#> = FALSE, : initial ETAs were nudged; (can control by foceiControl(etaNudge=.,
#> etaNudge2=))
#> Warning in (function (uif, data, est = NULL, control = list(), ..., sum.prod
#> = FALSE, : ETAs were reset to zero during optimization; (Can control by
#> foceiControl(resetEtaP=.))
#> Warning in (function (uif, data, est = NULL, control = list(), ..., sum.prod
#> = FALSE, : last objective function was not at minimum, possible problems in
#> optimization
#> Warning in (function (uif, data, est = NULL, control = list(), ..., sum.prod =
#> FALSE, : S matrix non-positive definite
#> Warning in (function (uif, data, est = NULL, control = list(), ..., sum.prod =
#> FALSE, : The variance of all elements are unreasonably small, <1e-7
#> Warning in (function (uif, data, est = NULL, control = list(), ..., sum.prod =
#> FALSE, : covariance step failed
#> Warning in (function (uif, data, est = NULL, control = list(), ..., sum.prod =
#> FALSE, : gradient problems with initial estimate and covariance; see $scaleInfo
print(fit)
#> ── nlmixr FOCEi (outer: nlminb) fit ────────────────────────────────────────────
#> 
#>           OBJF      AIC      BIC Log-likelihood
#> FOCEi 232.9682 489.5679 509.7476       -237.784
#> 
#> ── Time (sec $time): ───────────────────────────────────────────────────────────
#> 
#>            setup optimize covariance table    other
#> elapsed 0.264582 0.758061   0.758063 0.017 0.777294
#> 
#> ── Population Parameters ($parFixed or $parFixedDf): ───────────────────────────
#> 
#>          Est. Back-transformed BSV(CV%) Shrink(SD)%
#> tka      0.45             1.57     90.7      12.9% 
#> tcl     0.868             2.38     59.1      21.3% 
#> tv       3.43               31     32.4    -0.121% 
#> prop.sd   0.1              0.1                     
#>  
#>   Covariance Type ($covMethod): failed
#>   No correlations in between subject variability (BSV) matrix
#>   Full BSV covariance ($omega) or correlation ($omegaR; diagonals=SDs) 
#>   Distribution stats (mean/skewness/kurtosis/p-value) available in $shrink 
#>   Minimization message ($message):  
#>     false convergence (8) 
#>   In an ODE system, false convergence may mean "useless" evaluations were performed.
#>   See https://tinyurl.com/yyrrwkce
#>   It could also mean the convergence is poor, check results before accepting fit
#>   You may also try a good derivative free optimization:
#>     nlmixr(...,control=list(outerOpt="bobyqa"))
#> 
#> ── Fit Data (object is a modified tibble): ─────────────────────────────────────
#> # A tibble: 132 x 20
#>   ID     TIME    DV     PRED    RES      WRES IPRED   IRES  IWRES     CPRED
#>   <fct> <dbl> <dbl>    <dbl>  <dbl>     <dbl> <dbl>  <dbl>  <dbl>     <dbl>
#> 1 1     0      0.74 1.83e-15  0.740  4.41e+14  0     0.74   0.74  -3.29e-16
#> 2 1     0.25   2.84 3.31e+ 0 -0.473 -2.00e- 1  3.43 -0.588 -1.72   3.31e+ 0
#> 3 1     0.570  6.57 5.95e+ 0  0.623  1.82e- 1  6.08  0.486  0.799  5.95e+ 0
#> # … with 129 more rows, and 10 more variables: CRES <dbl>, CWRES <dbl>,
#> #   eta.ka <dbl>, eta.cl <dbl>, eta.v <dbl>, ka <dbl>, cl <dbl>, v <dbl>,
#> #   tad <dbl>, dosenum <dbl>

^{Created on 2021-03-09 by the reprex package (v1.0.0)}

[Dorota-D]

@mattfidler Thank you for the reply. Indeed, the proportional error was the worst one, and even in Monolix, it did not provide realistic results. I have another question about handling BLQ data and censoring. In the RxODE documentation, I've found that the 'CENS' and 'LIMIT' columns in the dataset should be recognized. However, I could not confirm it in the nlmixR documentation. If so, will all algorithms handle it, or is it limited to 'focei'?

[mattfidler]

focei and saem both support the cens and limit columns in the dataset.

The nlmixr documentation is lagging behind the RxODE documentation, but it is on my todo list.

[mattfidler]

nlme, on the other hand, does not support cens or limit.

[Dorota-D]

Thank you so much! I'm looking forward to seeing this project grow.

[mattfidler]

Great