# Statistical tests

Several statistical tests may be automatically performed to test the different components of the model. These tests use individual parameters drawn from the conditional distribution, which means that you need to run the task “Conditional distribution” in order to get these results. In addition, the tests for the residuals require to have first generated the residuals diagnostic plots (scatter plot or distribution).
The tests are all performed using the individual parameters sampled from the conditional distribution (or the random effects and residuals derived thereof). They are thus not subject to bias in case of shrinkage. For each individual, several samples from the conditional distribution may be used. The used tests include a correction to take into account that these samples are correlated among each other.

Results of the tests are available in the tab “Results”  and selecting “Tests” in the left menu

## The model for the individual parameters

Consider a PK example (warfarin data set from the demos) with the following model for the individual PK parameters (ka, V, Cl): In this example, the different assumptions we make about the model are:

• The 3 parameters are lognormally distributed
• ka is function of age only
• V is function of sex and weight. More precisely, the log-volume log(V) is a linear function of the log-weight $${\rm lw70 }= \log({\rm wt}/70)$$.
• Cl is not function of any of the covariates.
• The random effects $$\eta_V$$ and $$\eta_{Cl}$$ are linearly correlated
• $$\eta_{ka}$$ is not correlated with $$\eta_V$$ and $$\eta_{Cl}$$

Let’s see how each of these assumptions are tested:

### Covariate model

#### Individual parameters vs covariates – Test whether covariates should be removed from the model

If an individual parameter is function of a continuous covariate, the linear correlation between the transformed parameter and the covariate is not 0 and the associated $$\beta$$ coefficient is not 0 either. Then, Pearson’s correlation tests and Wald tests are used to test whether continuous covariates should be removed from the model. ANOVA and Wald tests are performed for categorical covariates in a same way.

##### Pearson’s correlation test and ANOVA

For continuous covariates, the Pearson’s correlation test tests the following null hypothesis:

H0: the person correlation coefficient between the individual parameters sampled from the conditional distribution and the covariate values is zero

For categorical covariates, the one-way ANOVA tests the following null-hypothesis:

H0: the mean of the individual parameters sampled from the conditional distribution is the same for each category of the categorical covariate

A small p-value indicates that the null hypothesis can be rejected and thus that the correlation between the individual parameter values and the covariate values is significant. If this is the case, the covariate should be kept in the model. On the opposite, if the p-value is large, the null hypothesis cannot be rejected and this suggests to remove the covariate from the model. High p-values are colored in yellow (p-value in [0.01-0.05]), orange (p-value in [0.05-0.10]) or red (p-value in [0.10-1]) to draw attention on parameter-covariate relationships that can be removed from the model from a statistical point of view.

In our example, the ANOVA test indicates that the mean of individual ka values is not significantly different for males and females, and this suggest to remove the covariate sex from the parameter ka. Remark: The two covariates weight and sex are strongly dependent. Then, the fact that both lw70 and sex are significant on the parameter V does not mean that these two covariates should be kept in the model.

##### Wald test

The Wald test relies on the standard errors. Thus the task “Standard errors” must have been calculated to see the test results. The test can be performed using the standard errors calculated using either the “linearization method” (indicated as “linearization”) or not (indicated as “stochastic approximation” in the tests).

The Wald test tests the following null hypothesis:

H0: the beta parameter is equal to zero (in case of more than two groups for categorical covariates: all beta parameters are equal to zero)

A small p-value indicates that the null hypothesis can be rejected and thus that the estimated beta parameter is significantly different from zero. If this is the case, the covariate should be kept in the model. On the opposite, if the p-value is large, the null hypothesis cannot be rejected and this suggests to remove the covariate from the model. Note that if beta is equal to zero, then the covariate has no impact on the parameter. High p-values are colored in yellow (p-value in [0.01-0.05]), orange (p-value in [0.05-0.10]) or red (p-value in [0.10-1]) to draw attention on parameter-covariate relationships that can be removed from the model from a statistical point of view.

In our example, the Wald test suggests to remove sex from ka and V: Remark: the Wald test and Pearson/ANOVA tests may suggest different covariates to keep or remove. Note that the null hypothesis tested is not the same.

#### Random effects vs covariates – Test whether covariates should be added to the model

Pearson’s correlation tests and ANOVA are performed to check if some relationships between random effects and covariates not yet included in the model should be added to the model.

For continuous covariates, the Pearson’s correlation test tests the following null hypothesis:

H0: the person correlation coefficient between the random effects (calculated from the individual parameters sampled from the conditional distribution) and the covariate values is zero

For categorical covariates, the one-way ANOVA tests the following null-hypothesis:

H0: the mean of the random effects (calculated from the individual parameters sampled from the conditional distribution) is the same for each category of the categorical covariate

A small p-value indicates that the null hypothesis can be rejected and thus that the correlation between the random effects and the covariate values is significant. If this is the case, it is probably worth considering to add the covariate in the model. Note that the decision of adding a covariate in the model should not only be driven by statistical considerations but also biological relevance. Note also that for parameter-covariate relationships already included in the model, the correlation between the random effects and covariates is not significant (while the correlation between the parameter and the covariate can be – see above). Small p-values are colored in yellow (p-value in [0.05-0.10]), orange (p-value in [0.01-0.05]) or red (p-value in [0.00-0.01]) to draw attention on parameter-covariate relationships that can be considered for addition in the model from a statistical point of view.

In our example, we already have sex on ka and V, and lw70 on V in the model. The only remaining relationship  that could possibly be worth investigating is between weight (or the log-transformed weight “lw70”) and clearance. ### The model for the random effects

#### Distribution of the random effects – Test if the random effects are normally distributed

In the individual model, the distributions for the parameters assume that the random effects follow a normal distribution. Shapiro-Wilk tests are performed to test this hypothesis. The null hypothesis is:

H0: the random effects are normally distributed

If the p-value is small, there is evidence that the random effects are not normally distributed and this calls the choice of the individual model (parameter distribution and covariates) into question. Small p-values are colored in yellow (p-value in [0.05-0.10]), orange (p-value in [0.01-0.05]) or red (p-value in [0.00-0.01]).

In our example, there is no reason to reject the null-hypothesis and no reason to question the chosen log-normal distributions for the parameters.

#### Joint distribution of the random effects – Test if the random effects are correlated

Pearson’s correlation tests are performed to test if the random effects (calculated from the individual parameters sampled from the conditional distribution) are linearly correlated. The null-hypothesis is:

H0: the correlation between the random effects of the first and second parameter is zero

For correlations not yet included in the model, a small p-value indicates that there is a significant correlation between the random effects of two parameters and that this correlation should be estimated as part of the model (otherwise simulations from the model will assume that the random effects of the two parameters are not correlated, which is not what is observed for the random effects estimated using the data). Small p-values are colored in yellow (p-value in [0.05-0.10]), orange (p-value in [0.01-0.05]) or red (p-value in [0.00-0.01]).
For correlations already included in the model, a large p-value indicates that one cannot reject the hypothesis that the correlation between the random effects is zero. If the correlation is not significantly different from zero, it may not be worth estimating it in the model. High p-values are colored in yellow (p-value in [0.01-0.05]), orange (p-value in [0.05-0.10]) or red (p-value in [0.10-1])

In our example, we have assumed in the model that $$\eta_V$$ and $$\eta_{Cl}$$ are correlated. The high p-value indicated that the correlation between the random effects of V and Cl is not significantly different from zero and suggests to remove this correlation from the model. Remark: as correlations can only be estimated by groups (i.e if a correlation is estimated between (ka, V) and between (V, Cl), then one must also estimate the correlation between (ka, Cl)), it may happen that it is not possible to remove a non-significant correlation without removing also a significant one.

Starting from the 2019 version, the test is a t-test that checks:

H0: the expectation of the correlations between the random effects of the first and second parameter over the replicates is zero

The p-values have the same meaning and color code as with the previous test, however this method is more powerful. Correlations already included in the model are now highlighted in blue. ### The distribution of the individual parameters

#### Distribution of the individual parameters not dependent on covariates – Test if transformed individual parameters are normally distributed

When an individual parameter doesn’t depend on covariates, its distribution (normal, lognormal, logit or probit) can be transformed into the normal distribution. Then, a Shapiro-Wilk test can be used to test the normality of the transformed parameter. The null hypothesis is:

H0: the transformed individual parameter values (sampled from the conditional distribution) is normally distributed

If the p-value is small, there is evidence that the transformed individual parameter values are not normally distributed and this calls the choice of the parameter distribution into question. Small p-values are colored in yellow (p-value in [0.05-0.10]), orange (p-value in [0.01-0.05]) or red (p-value in [0.00-0.01]).

In our example, there is no reason to reject the null hypothesis of lognormality for Cl. Remark:  testing the normality of a transformed individual parameter that does not depend on covariates is equivalent to testing the normality of the associated random effect. We can check in our example that the  Shapiro-Wilk tests for $$\log(Cl)$$ and $$\eta_{Cl}$$ are equivalent.

#### Distribution of the individual parameters dependent on covariates – test the marginal distribution of each individual parameter

Individual parameters that depend on covariates are not anymore identically distributed. Each transformed individual parameter is normally distributed, with its own mean that depends on the value of the individual covariate. In other words, the distribution of an individual parameter is a mixture of (transformed) normal distributions. A Kolmogorov-Smirnov test is used for testing the distributional adequacy of these individual parameters. The null-hypothesis is:

H0: the individual parameters are samples from the mixture of transformed normal distributions (defined by the population parameters and the covariate values)

A small p-value indicates that the null hypothesis can be rejected. Small p-values are colored in yellow (p-value in [0.05-0.10]), orange (p-value in [0.01-0.05]) or red (p-value in [0.00-0.01]).

With our example, we obtain: ## The model for the observations

A combined1 error model with a normal distribution is assumed in our example: ### Distribution of the residuals

Different tests are performed for the individual residuals (IWRES), the NPDE and for the population residuals (PWRES).

#### Test if the distribution of the residuals is symmetrical around 0

A Van Der Waerden test is used for testing the symmetry of the residuals. Indeed, symmetry of the residuals around 0 is an important property that deserves to be tested, in order to decide, for instance, if some transformation of the observations should be done. The null hypothesis tested is:

H0: the distribution of the residual is symmetrical around 0

A small p-value indicates that the null hypothesis can be rejected. Small p-values are colored in yellow (p-value in [0.05-0.10]), orange (p-value in [0.01-0.05]) or red (p-value in [0.00-0.01]).

With our example, we obtain: Starting from the 2019 version, a more powerful symmetry test (Miao, Gel and Gastwirth (2006)) is used instead of the Van Der Waerden test. #### Test if the residuals are normally distributed

A Shapiro Wilk test is used for testing the normality of the residuals. The null hypothesis is:

H0: the residuals are normally distributed

If the p-value is small, there is evidence that the residuals are not normally distributed. The Shapiro Wilk test is known to be very powerful. Then, a small deviation of the empirical distribution from the normal distribution may lead to a very significant test (i.e. a very small p-value), which does not necessarily means that the model should be rejected. Thus, no color highlight is made for this test.

In our example, we obtain: 