### Parameter initial estimates and associated methods

Initial values are specified for the fixed effects, for the standard errors of the random effects and for the residual error parameters. These initial values are available through the frame “Initial estimates” of the interface as can be seen on the following figure. It is recommended to initialize the estimation to have faster convergence.

- Initialization of the “Fixed effects”
- What method can I use for the parameters estimations?
- How to initialize your parameters?

### Initialization of the estimates

#### Initialization of the “Fixed effects”

The user can modify all the initial values of the fixed effects. When initializating the project, the values are set by default to 1. To change it, the user can click on the parameter and change the value

Notice that when you click on the parameter, an information is provided to tell what value is possible. The constraint depends on the distribution chosen for the parameter. For exemple, if the volume parameter V is defined as lognormal, its initial value should be strictly greater than 0. In that case, if you set a negative value, an error will be thrown and the previous parameter will be displayed.

When a parameter depends on a covariate, initial values for the dependency (named with prefix, for instance beta_V_SEX_M to add the dependency of SEX, on parameter V) are displayed. The default initial value is 0. In case of a continuous covariate, the covariate is added linearly to the transformed parameter, with a coefficient . For categorical covariates, the initial value for the reference category will be the one of the fixed effect, while for all other categories it will be the initial value for the fixed effect plus the initial value of the , in the transformed parameter space. It is possible to define different initial values for the non-reference categories. The equations for the parameters can be visualized by clicking on button formula in the “Statistical model & Tasks” frame

#### Initialization of the “Standard deviation of the random effects”

The user can modify all the initial values of the standard deviations of the random effects. The default value is set to 1. We recommend to keep these values high in order for SAEM to have the possibility to explore the domain.

#### Initialization of the “Residual error parameters”

The user can modify all the initial values of the residual error parameters. There are as many lines as continuous outputs of the model. The default value depends on the parameter (1 for “a”, 0.3 for “b” and 1 for “c”).

### What method can I use for the parameters estimations?

For all the parameters, there are several methods for the estimation

- “Fixed”: the parameter is kept to its initial value and so, it will not be estimated. In that case, the parameter name is set to orange.
- “Maximum Likelihood Estimation”: The parameter is estimated using maximum likelihood. In that case, the the parameter name remains grey. This is the default option
- “Maximum A Posteriori Estimation”: The parameter is estimated using maximum a posteriori estimation. In that case, the user has to define both a typical value and a standard deviation. For more about this, see here. In that case, the parameter name is colored in purple.

To change the method, click on the right of the parameter as on the following.

A window pops up to choose the method as on the following figure

Notice that you have buttons to fix all the parameters or estimate all on the top right of the window as can be seen on the following figure

### How to initialize your parameters?

#### Check initial fixed effects

When clicking on the “Check the initial fixed effects”, the simulations obtained with the initial population fixed effects values are displayed for each individual together with the data points, in case of continuous observations. This feature is very useful to find some “good” initial values. Although Monolix is quite robust with respect to initial parameter values, good initial estimates speed up the estimation. You can change the values of the parameters and see how the agreement with the data change. In addition, you can change the axis to log-scale and choose the same limit on all axis to have a better comparison of the individuals.

In addition, if you think that there are not enough points for the prediction (if there are a lot of doses for example), you can change the discretization and increase the number of points.

#### On the use of last estimates

If you have already estimated the population parameters for this project, then you can use the “Use the last estimates” buttons to use the previous estimates as initial values. The user has the possibility to use all the last estimates or only the fixed effects. The interest of using only the fixed effects is not to have too low initial standard effects and thus let SAEM explore a larger domain for the next run.