# Chapter 13 Linear mixed models for more than two measurements

## 13.1 Pre-mid-post intervention designs

In many intervention studies, one has more than two measurement moments. Let’s go back to the example of the effect of aspirin on headache in Chapter 12. Suppose you’d like to know whether there is not only a short-term effect of aspirin, but also a long-term effect. Imagine that the study on headache among NY Times readers was extended by asking patients not only to rate their headache before aspirin and 3 hours after intake, but also 24 hours after intake. In this case our data could look like as presented in Table 13.1.

patient pre post1 post2
1 52 45 47
2 59 50 55
3 65 56 58
4 51 37 42
5 62 50 55
6 61 53 57
7 56 44 55
8 62 48 53
9 56 48 49
10 58 45 44

So for each patient we have three measures: pre, post1 and post2. To see if there is some clustering, it is no longer possible to study this by computing a single correlation. We could however compute 3 different correlations: pre-post1, pre-post2, and post1-post2, but this is rather tedious, and moreover does not give us a single measure of the extent of clustering of the data. But there is an alternative: one could compute not a Pearson correlation, but an intraclass correlation (ICC). To do this, we need to bring the data again into long format, as opposed to wide format, see Chapter 1. This is done in Table 13.2.

1 pre 52
1 post1 45
1 post2 47
2 pre 59
2 post1 50
2 post2 55
3 pre 65
3 post1 56
3 post2 58
4 pre 51

Next, we can perform an analysis with the lmer() function from the lme4 package.

library(lme4)
model1 <- datalong %>%
lmer(headache ~ measure + (1|patient), data = ., REML = FALSE)
model1
## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: headache ~ measure + (1 | patient)
##    Data: .
##      AIC      BIC   logLik deviance df.resid
## 1740.998 1759.517 -865.499 1730.998      295
## Random effects:
##  Groups   Name        Std.Dev.
##  patient  (Intercept) 5.290
##  Residual             2.908
## Number of obs: 300, groups:  patient, 100
## Fixed Effects:
##  (Intercept)  measurepost2    measurepre
##        49.32          2.36          9.85

In the output we see the fixed effects of two automatically created dummy variables measurepost2 and measurepre, and the intercept. We also see the standard deviations of the random effects: the standard deviation of the residuals and the standard deviation of the random effects for the patients.

From this output, we can plug in the values into the equation:

\begin{aligned} \texttt{headache}_{ij} &= 49.32 + patient_i + 2.36 \; \texttt{measurepost2} + 9.85 \; \texttt{measurepre} + e_{ij} \nonumber\\ patient_i &\sim N(0, \sigma_p = 5.29 )\nonumber\\ e_{ij} &\sim N(0, \sigma_e = 2.91)\nonumber\end{aligned}

Based on this equation, the expected headache severity score in the population three hours after aspirin intake is 49.32 (the first post measure is the reference group). Dummy variable measurepost2 is coded 1 for the measurements 24 hours after aspirin intake. Therefore, the expected headache score 24 hours after aspirin intake is equal to $$49.32 + 2.36 = 51.68$$. Dummy variable measurepre was coded 1 for the measurements before aspirin intake. Therefore, the expected headache before aspirin intake is equal to $$49.32 + 9.85 = 59.17$$. In sum, in this sample we see that the average headache level decreases directly after aspirin intake from 59.17 to 49.32, but then increases again to 51.68.

There is quite some variation in individual headache levels: the variance is equal to $$5.29 ^2 = 27.98$$, since the standard deviation (its square root) is equal to 5.29. Therefore, if we look at roughly 95% of the sample, we see that prior to taking aspirin, the scores vary between $$59.17 - 2\times 5.29 = 48.59$$ and $$59.17 + 2 \times 5.29 = 69.75$$. For the short-term effect of aspirin after 3 hours, we see that roughly 95% of the scores lie between $$49.32 -2\times 5.29 = 38.74$$ and $$49.32 + 2 \times 5.29 = 59.90$$. The normal distributions, predicted by this model, are depicted in Figure 13.1.

So, are these distributions significantly different, in other words, do the means differ significantly before aspirin, 3 hrs after aspirin and 24 hrs after aspirin?

To answer a question about the equality of three means, we need an analysis of variance (ANOVA).

model1 %>% anova()
## Analysis of Variance Table
##         npar Sum Sq Mean Sq F value
## measure    2 5289.7  2644.9  312.71

The $$F$$-statistic for the equality of the group means is quite large (remember that the expected value is always 1 if the null-hypothesis is true, see Chapter 6). We see no degrees of freedom. These need to be estimated, for example using Satterthwaite’s method, provided by the lmerTest package:

library(lmerTest)
##
## Attaching package: 'lmerTest'
## The following object is masked from 'package:lme4':
##
##     lmer
## The following object is masked from 'package:stats':
##
##     step
model2 <- datalong %>%
data = .,
REML = FALSE)
model2 %>% anova()
## Type III Analysis of Variance Table with Satterthwaite's method
##         Sum Sq Mean Sq NumDF DenDF F value                Pr(>F)
## measure 5289.7  2644.9     2   200  312.71 < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Using Satterthwaite’s method, we see that the error degrees of freedom is 200. The model degrees of freedom is of course 2, because we needed two dummy variables for the three measurements. The $$p$$-value associated with the $$F$$-value of 312.71 at 2 and 200 degrees of freedom is less than 0.05, so we can reject the null-hypothesis. Thus we can report:

"The null-hypothesis that the mean headache level does not change over time was tested with a linear mixed model, with measure entered as a fixed categorical effect ("before", "3hrs after", "24 hrs after") and random effects for patient. An ANOVA showed that the null-hypothesis could be rejected, $$F(2, 200) = 312.71, p < .001$$."

If one has specific hypotheses regarding short-term and long-term effects, one could perform a planned contrast analysis (see Chapter 10, forthcoming), comparing the first measure with the second measure, and the first measure with the third measure. If one is just interested in whether aspirin has an effect on headache, then the overall $$F$$-test should suffice. If apart from this general effect one wishes to explore whether there are significant differences between the three groups of data, without any prior research hypothesis about this, then one could perform a post hoc analysis of the three means. See Chapter 10 (forthcoming) on how to perform planned comparisons and post hoc tests.

Now recall that we mentioned an intraclass correlation, or ICC. An intraclass correlation indicates how much clustering there is within the groups, in this case, clustering of headache scores within individual NY Times readers. How much are the three scores alike that come from the same patient? This correlation can be computed using the following formula:

\begin{aligned} ICC = \frac{\sigma^2_{patient} } {\sigma^2_{patient} +\sigma^2_e } \end{aligned}

Here, the variance of the patient random effects is equal to $$5.29^2 = 27.98$$, and the variance of the residuals $$e$$ is equal to $$2.91^2 = 8.47$$, so the intraclass correlation for the headache severity scores is equal to

\begin{aligned} ICC = \frac{27.98} {27.98 + 8.47 } = 0.77 \end{aligned}

As this correlation is substantially higher than 0, we conclude there is quite a lot of clustering. Therefore it’s a good thing that we used random effects for the individual differences in headache scores among NY Times readers. Had this correlation been 0 or very close to 0, however, then it would not have mattered to include these random effects. In that case, we might as well use an ordinary linear model, using the lm() function. Note from the formula that the correlation becomes 0 when the variance of the random effects for patients is 0. It approaches 0 as the random effects for patients grows small relative to the residual variance. It approaches 1 as the random effects for patients grows large relative to the residual variance. Because variance cannot be negative, ICCs always have values between 0 and 1.

When is an ICC large and when is it small? This is best thought of as being at a birthday party. Most birthday cakes are intended for 8 to 12 guests. If you therefore have one tenth of a birthday cake, that’s a substantial piece. It will most likely fill you up. Therefore, an ICC of 0.10 is definitely to be reckoned with. A larger piece than the regular size will fill you up for the rest of the day. A somewhat smaller piece, say 0.05, is also to be taken seriously. It is nice to have a fraction of 0.05 of a whole birthday cake (you don’t mind sharing, do you?). However, when the host offers you one percent of the cake (0.01), at least I would be inclined to say no thank you.

## 13.2 Pre-mid-post intervention design: linear effects

In the previous section, we’ve looked at categorical variables: measure ("pre-intervention", "3 hours after", and "24 hours after"). We can use the same type of analysis for numerical variables. In fact, we could have used a linear effect for time in the headache example: using time of measurement as a numeric variable. Let’s look at the headache data again. But now we’ve created a new variable time that is based on the variable measure: all first measurements are coded as time = 0, all second measurements after 3 hours are coded as time = 3, and all third measurements after 24 hours are coded as time = 24. Part of the data are presented in Table 13.3.

Table 13.3: Headache measures in NY Times readers in long format with a new variable time.
1 pre 52 0
1 post1 45 3
1 post2 47 24
2 pre 59 0
2 post1 50 3
2 post2 55 24
3 pre 65 0
3 post1 56 3
3 post2 58 24
4 pre 51 0

Instead of using a categorical intervention variable, with three levels, we now use a numeric variable, time, indicating the number of hours that have elapsed after aspirin intake. At point 0 hours, we measure headache severity, and patients take an aspirin. Next we measure headache after 3 hours and 24 hours. Above, we wanted to know if there were differences in average headache between before intake and 3 hrs and 24 hrs after intake. Another question we might ask ourselves: is there a linear reduction in headache severity after taking aspirin?

For this we can do a linear regression type of analysis. We want to take into account individual differences in headache severity levels among patients, so we perform an lmer() analysis, using the following code, replacing the categorical variable measure by numerical variable time:

model3 <- datalongnew %>%
lmer(headache ~ time + (1|patient), data = ., REML = FALSE)
model3
## Linear mixed model fit by maximum likelihood  ['lmerModLmerTest']
## Formula: headache ~ time + (1 | patient)
##    Data: .
##       AIC       BIC    logLik  deviance  df.resid
## 1997.2264 2012.0416 -994.6132 1989.2264       296
## Random effects:
##  Groups   Name        Std.Dev.
##  patient  (Intercept) 4.533
##  Residual             5.546
## Number of obs: 300, groups:  patient, 100
## Fixed Effects:
## (Intercept)         time
##     54.7913      -0.1557

In the output we see that the model for our data is equivalent to

\begin{aligned} \texttt{headache}_{ij} &= 54.79 + patient_i - 0.1557 \times \texttt{time} + e_{ij} \\ patient_i &\sim N(0, \sigma_p = 4.53)\\ e_{ij} &\sim N(0, \sigma_e = 5.55)\end{aligned}

This model predicts that at time 0, the average headache severity score equals 54.79, and that for every hour after intake, the headache level drops by 0.1557 points. So it predicts for example that after 10 hours, the headache has dropped 1.557 points to 53.23.

Is this a good model for the data? Probably not. Look at the variance of the residuals: with a standard deviation of 5.55 it is now a lot bigger than in the previous analysis with the same data (see previous section). Larger variance of residuals means that the model explains the data worse: predictions are worse, so the residuals increase in size.

That the model is not appropriate for this data set is also obvious when we plot the data, focusing on the relationship between time and headache levels, see Figure 13.2.

The line shown is the fitted line based on the output. It can be seen that the prediction for time = 0 is systematically too low, for time = 3 systematically too high, and for time = 24 again too low. So for this particular data set on headache, it would be better to use a categorical predictor for the effect of time on headache, like we did in the previous section.

As an example of a data set where a linear effect would have been appropriate, imagine that we measured headache 0 hours, 2 hours and 3 hours after aspirin intake (and not after 24 hours). Suppose these data would look like those in Figure 13.3. There we see a gradual increase of headache levels right after aspirin intake. Here, a numeric treatment of the time variable would be quite appropriate.

Suppose we would then see the following output.

model4 <- datalongnew2 %>%
lmer(headache ~ time + (1|patient), data = ., REML = FALSE)
model4 %>% summary()
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: headache ~ time + (1 | patient)
##    Data: .
##
##      AIC      BIC   logLik deviance df.resid
##   1745.4   1760.2   -868.7   1737.4      296
##
## Scaled residuals:
##      Min       1Q   Median       3Q      Max
## -2.78967 -0.55395  0.05095  0.56015  2.30971
##
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  patient  (Intercept) 27.889   5.281
##  Residual              8.732   2.955
## Number of obs: 300, groups:  patient, 100
##
## Fixed effects:
##             Estimate Std. Error       df t value            Pr(>|t|)
## (Intercept)  58.9721     0.6000 134.6807   98.29 <0.0000000000000002 ***
## time         -3.3493     0.1368 200.0000  -24.48 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
##      (Intr)
## time -0.380

Because we are confident that this model is appropriate for our data, we can interpret the statistical output. The Satterthwaite error degrees of freedom are 200, so we can construct a 95% confidence interval by finding the appropriate $$t$$-value.

qt(0.975, df = 200)
## [1] 1.971896

The 95% confidence interval for the effect of time is then from $$-3.349 - 1.97 \times 0.1368$$ to $$-3.349 + 1.97 \times 0.1368$$, so from -3.62 to -3.08. We can report:

"A linear mixed model was run on the headache levels, using a fixed effect for the numeric predictor variable time and random effects for the variable patient. We saw a significant linear effect of time on headache level, $$t(200)=-24.42, p < .001$$. The estimated effect of time based on this analysis is negative, $$-3.35$$, so with every hour that elapses after aspirin intake, the predicted headache score decreases with 3.35 points (95% CI: 3.08 to 3.62 points)".

## 13.3 Linear mixed models and interaction effects

Suppose we carry out the aspirin and headache study not only with a random sample of NY Times readers that suffer from regular headaches, but also with a random sample of readers of the Wall Street Journal that suffer from regular headaches. We’d like to know whether aspirin works, but we are also interested to know whether the effect of aspirin is similar in the two groups of readers. Our null-hypothesis is that the effect of aspirin in affecting headache severity is the same in NY Times and Wall Street Journal readers that suffer from headaches.

$$H_0$$: The effect of aspirin is the same for NY Times readers as for Wall Street Journal readers.

Suppose we have the data set in Table 13.4 (we only show the first six patients), and we only look at the measurements before aspirin intake and 3 hours after aspirin intake (pre-post design).

Table 13.4: Headache measures in NY Times and Wall Street Journal readers in wide format.
patient group pre post
1 NYTimes 55 45
2 WallStreetJ 63 50
3 NYTimes 66 56
4 WallStreetJ 50 37
5 NYTimes 63 50
6 WallStreetJ 65 53

In this part of the data set, patients 2, 4, and 6 read the Wall Street Journal, and patients 1, 3 and 5 read the NY Times. We assume that people only read one of these newspapers. We measure their headache before and after the intake of aspirin (a pre-post design). The data are now in what we call wide format: the dependent variable headache is spread over two columns, pre and post. In order to analyse the data with linear models, we need them in long format, as in Table 13.5.

Table 13.5: Headache measures in NY Times and Wall Street Journal readers in long format.
1 NYTimes pre 55
1 NYTimes post 45
2 WallStreetJ pre 63
2 WallStreetJ post 50
3 NYTimes pre 66
3 NYTimes post 56
datalong <- datawide %>%
pivot_longer(cols = -c(patient, group),
names_to = "measure",
values_to = "headache")

The new variable measure now indicates whether a given measurement of headache refers to a measurement before intake (pre) or after intake (post). Again we could investigate whether there is an effect of aspirin with a linear mixed model, with measure as our categorical predictor, but that is not really what we want to test: we only want to know whether the effect of aspirin (being small, large, negative or non-existent) is the same for both groups. Remember that this hypothesis states that there is no interaction effect of aspirin (measure) and group. The null-hypothesis is that group is not a moderator of the effect of aspirin on headache. There may be an effect of aspirin or there may not, and there may be an effect of newspaper (group) or there may not, but we’re interested in the interaction of aspirin and group membership. Is the effect of aspirin different for NY Times readers than for Wall Street Journal readers?

In our model we therefore need to specify an interaction effect. Since the data are clustered (2 measures per patient), we use a linear mixed model. First we show how to analyse these data using dummy variables, later we will show the results using a different approach.

We recode the data into two dummy variables, one for the aspirin intervention (dummy1: 1 if measure = post, 0 otherwise), and one for group membership (dummy2: 1 if group = NYTimes, 0 otherwise):

datalong <- datalong %>%
mutate(dummy1 = ifelse(measure == "post", 1, 0),
dummy2 = ifelse(group == "NYTimes", 1, 0))
datalong %>% head(3)
## # A tibble: 3 × 6
##   patient group       measure headache dummy1 dummy2
##     <int> <chr>       <chr>      <dbl>  <dbl>  <dbl>
## 1       1 NYTimes     pre           55      0      1
## 2       1 NYTimes     post          45      1      1
## 3       2 WallStreetJ pre           63      0      0

Next we need to compute the product of these two dummies to code a dummy for the interaction effect. Since with the above dummy coding, all post measures get a 1, and all NY Times readers get a 1, only the observations that are post aspirin and that are from NY Times readers get a 1 for this product.

datalong <- datalong %>%
mutate(dummy_interact = dummy1*dummy2)
datalong %>% head(3)
## # A tibble: 3 × 7
##   patient group       measure headache dummy1 dummy2 dummy_interact
##     <int> <chr>       <chr>      <dbl>  <dbl>  <dbl>          <dbl>
## 1       1 NYTimes     pre           55      0      1              0
## 2       1 NYTimes     post          45      1      1              1
## 3       2 WallStreetJ pre           63      0      0              0

With these three new dummy variables we can specify the linear mixed model.

model5 <- datalong %>%
lmer(headache ~ dummy1 + dummy2 + dummy_interact + (1|patient),
data = .,
REML = FALSE)
model5
## Linear mixed model fit by maximum likelihood  ['lmerModLmerTest']
## Formula: headache ~ dummy1 + dummy2 + dummy_interact + (1 | patient)
##    Data: .
##       AIC       BIC    logLik  deviance  df.resid
## 1201.6855 1221.4754 -594.8427 1189.6855       194
## Random effects:
##  Groups   Name        Std.Dev.
##  patient  (Intercept) 5.177
##  Residual             2.855
## Number of obs: 200, groups:  patient, 100
## Fixed Effects:
##    (Intercept)          dummy1          dummy2  dummy_interact
##          59.52          -10.66            0.32            0.60

In the output, we recognise the three fixed effects for the three dummy variables. Since we’re interested in the interaction effect, we look at the effect of dummy_interact. The effect is in the order of +0.6. What does this mean?

Remember that all headache measures before aspirin intake are given a 0 for the intervention dummy dummy1. A reader from the Wall Street Journal gets a 0 for the group dummy dummy2. Since the product of $$0\times 0$$ equals 0, all measures before aspirin in Wall Street Journal readers get a 0 for the interaction dummy dummy_interact. Therefore, the intercept of 59.52 refers to the expected headache severity of Wall Street Journal readers before they take their aspirin.

Furthermore, we see that the effect of dummy1 is -10.66. The variable dummy1 codes for post measurements. So, relative to Wall Street Journal readers prior to aspirin intake, the level of post intake headache is 10.66 points lower.

If we look further in the output, we see that the effect of dummy2 equals +0.32. This variable dummy2 codes for NY Times readers. So, relative to Wall Street Journal readers and before aspirin intake (the reference group), NY Times readers score on average 0.32 points higher on the headache scale.

However, we’re not interested in a general difference between those two groups of readers, we’re interested in the effect of aspirin and whether it is different in the two groups of readers. In the output we see the interaction effect: being a reader of the NY Times AND at the same time being a measure after aspirin intake, the expected level of headache is an extra +0.60. The effect of aspirin is -10.66 in Wall Street Journal readers, as we saw above, but the effect is $$-10.66 + 0.60 = -10.06$$ in NY Times readers. So in this sample the effect of aspirin on headache is 0.60 smaller than in Wall Street Journal readers (note that even while the interaction effect is positive, it is positive on a scale where a high score means more headache).

Let’s look at it in a different way, using a table with the dummy codes, see Table ??. For each group of data, pre or post aspirin and NY Times readers and Wall Street Journal readers, we note the dummy codes for the new dummy variables. In the last column we use the output estimates and multiply them with the respective dummy codes (1 and 0) to obtain the expected headache level (using rounded numbers):

Expected headache levels in Wall Street Journal and NY Times readers, before and after aspirin intake.
measure group dummy1 dummy2 dummy_interact exp mean
pre WallStreetJ 0 0 0 $$60$$
post WallStreetJ 1 0 0 $$60 + (-11)=49$$
pre NYTimes 0 1 0 $$60 + 0.3=60.3$$
post NYTimes 1 1 1 $$60 +(-11) + 0.3 + 0.6=49.9$$

{#tab:exp label=“tab:exp”}

The exact numbers are displayed in Figure 13.4.

We see that the specific effect of aspirin in NY Times readers is 0.60 smaller than the effect of aspirin in Wall Street Journal readers. This difference in the effect of aspirin between the groups was not significantly different from 0, as we can see when we let R plot a summary of the results.

model5 %>% summary()
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: headache ~ dummy1 + dummy2 + dummy_interact + (1 | patient)
##    Data: .
##
##      AIC      BIC   logLik deviance df.resid
##   1201.7   1221.5   -594.8   1189.7      194
##
## Scaled residuals:
##      Min       1Q   Median       3Q      Max
## -2.34085 -0.43678 -0.02942  0.50880  2.22534
##
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  patient  (Intercept) 26.80    5.177
##  Residual              8.15    2.855
## Number of obs: 200, groups:  patient, 100
##
## Fixed effects:
##                Estimate Std. Error       df t value            Pr(>|t|)
## (Intercept)     59.5200     0.8361 125.9463  71.192 <0.0000000000000002 ***
## dummy1         -10.6600     0.5710 100.0000 -18.670 <0.0000000000000002 ***
## dummy2           0.3200     1.1824 125.9463   0.271               0.787
## dummy_interact   0.6000     0.8075 100.0000   0.743               0.459
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
##             (Intr) dummy1 dummy2
## dummy1      -0.341
## dummy2      -0.707  0.241
## dummy_ntrct  0.241 -0.707 -0.341

"The null-hypothesis that the effect of aspirin is the same in the two populations of readers cannot be rejected, $$t(100)=0.743, p = .46$$. We therefore conclude that there is no evidence that aspirin has a different effect for NY Times than for Wall Street Journal readers."

Note that we could have done the analysis in another way, not recoding the variables into numeric dummy variables ourselves, but by letting R do it automatically. R does that automatically for factor variables like our variable group. The code is then:

model6 <- datalong %>%
lmer(headache ~ measure + group + measure:group + (1|patient),
data = .,
REML = FALSE)
model6 %>% summary()
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: headache ~ measure + group + measure:group + (1 | patient)
##    Data: .
##
##      AIC      BIC   logLik deviance df.resid
##   1201.7   1221.5   -594.8   1189.7      194
##
## Scaled residuals:
##      Min       1Q   Median       3Q      Max
## -2.34085 -0.43678 -0.02942  0.50880  2.22534
##
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  patient  (Intercept) 26.80    5.177
##  Residual              8.15    2.855
## Number of obs: 200, groups:  patient, 100
##
## Fixed effects:
##                             Estimate Std. Error       df t value
## (Intercept)                  49.7800     0.8361 125.9463  59.542
## measurepre                   10.0600     0.5710 100.0000  17.619
## groupWallStreetJ             -0.9200     1.1824 125.9463  -0.778
## measurepre:groupWallStreetJ   0.6000     0.8075 100.0000   0.743
##                                        Pr(>|t|)
## (Intercept)                 <0.0000000000000002 ***
## measurepre                  <0.0000000000000002 ***
## groupWallStreetJ                          0.438
## measurepre:groupWallStreetJ               0.459
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
##             (Intr) mesrpr grpWSJ
## measurepre  -0.341
## grpWllStrtJ -0.707  0.241
## msrpr:grWSJ  0.241 -0.707 -0.341

R has automatically created dummy variables, one dummy measurepre that codes 1 for all measurements before aspirin intake, one dummy groupWallStreetJ that codes 1 for all measurements from Wall Street Journal readers, and one dummy measurepre:groupWallStreetJ that codes 1 for all measurements from Wall Street Journal readers before aspirin intake. Because the dummy coding is different from the hand coding we did ourselves earlier, the intercept and the main effects of group and measure are now different. We also see that the significance level of the interaction effect is still the same. You are always free to choose to either construct your own dummy variables and analyse them in a quantitative way (using numeric variables), or to let R construct the dummy variables for you (by using a factor variable): the $$p$$-value for the interaction effect will always be the same (this is not true for the intercept and the main effects).

Because the two analyses are equivalent (they end up with exactly the same predictions, feel free to check!), we can safely report that we have found a non-significant group by measure interaction effect, $$t(100)=0.74, p = .46$$. We therefore conclude that we found no evidence that in the populations of NY Times readers and Wall Street Journal readers, the short-term effect of aspirin on headache is any different.

## 13.4 Mixed designs

The design in the previous section, where we had both a grouping variable and a pre-post or repeated measures design, is often called a mixed design. It is a mixed design in the sense that there are two kinds of variables: one is a between-individuals variable, and one variable is a within-individual variable. Here the between-individuals variable is group: two different populations of readers. It is called between because one individual can only be part of one group. When we study the effect of the group effect we are essentially comparing the scores of one group of individuals with the scores of another group of individuals, so the comparison is between different individuals. The two groups of data are said to be independent, as we knew that none of the readers in this data set reads both journals.

The within-variable in this design is the aspirin intervention, indicated by the variable measure. For each individual we have two observations: all individuals are present in both the pre condition data as well as in the post condition data. With this intervention variable, we are comparing the scores of a group of individuals with the scores of that same group of individuals at another time point. The comparison of scores is within a particular individual, at time point 1 and at time point 2. So the pre and post sets of data are not independent: the headache scores in both conditions are coming from the same individuals.

Mixed designs are often seen in psychological experiments. For instance, you want to know how large the effect of alcohol intake is on driving performance. You want to know whether the effect of alcohol on driving performance is the same in a Fiat 600 as in a Porsche 918. Suppose you have 100 participants for your study. There are many choices you can make regarding the design of your study. Here we discuss 4 alternative research designs:

1. One option is to have all participants participate in all four conditions: they all drive a Fiat with and without alcohol, and they all drive a Porsche, with and without alcohol. In this case, both the car and the alcohol are within-participant variables.

2. The second option is to have 50 participants drive a Porsche, with and without alcohol, and to have the other 50 participants drive the Fiat, with and without alcohol. In this case, the car is the between-participants variable, and alcohol is the within-participant variable.

3. The third option is to have 50 participants without alcohol drive both the Porsche and the Fiat, and to have the other 50 participants drive the Porsche and the Fiat with alcohol. Now the car is the within-participant variable, and the alcohol is the between-participants variable.

4. The fourth option is to have 25 participants drive the Porsche with alcohol, 25 other participants drive the Porsche without alcohol, 25 participants drive the Fiat with alcohol, and the remaining 25 participants drive the Fiat without alcohol. Now both the car variable and the alcohol variable are between-participant variables: none of the participants is present in more than 1 condition.

Only the second and the third design described here are mixed designs, having at least one between-participants variable and at least one within-participant variable.

Remember that when there is at least one within variable in your design, you have to use a linear mixed model. If all variables are between variables, one can use an ordinary linear model. Note that the term mixed in linear mixed model refers to the effects in the model that can be both random and fixed. The term mixed in mixed designs refers to the mix of two kinds of variables: within variables and between variables.

Also note that the within and between distinction refers to the units of analysis. If the unit of analysis is school, then the denomination of the school is a between-school variable. An example of a within-school variable could be time: before a major curriculum reform and after a major curriculum reform. Or it could be teacher: classes taught by teacher A or by teacher B, both teaching at the same school.

## 13.5 Mixed design with a linear effect

In an earlier section we looked at a mixed design where the between variable was group and the within variable was measure: pre or post. It was a 2 by 2 design ($$2 \times 2$$) design: 2 measures and 2 groups, where we were interested in the interaction effect. We wanted to know whether group moderated the effect of measure, that is, the effect of aspirin on headache. We used the categorical within-individual variable measure in the regression by dummy-coding it.

In an earlier section in this chapter we saw that we can also model linear effects of numeric variables in linear mixed models, where we treated the time variable numerically: 0hrs, 3hrs after aspirin intake and 24 hrs after intake. Here we will give an example of a $$3 \times 20$$ mixed design: we have a categorical group (between-individuals) variable with 3 levels and a numeric time (within) variable with 20 levels. The example is about stress in athletes that are going to take part in the 2018 Winter Olympics. Stress can be revealed in morning cortisol levels. In the 20 days preceding the start of the Olympics, each athlete was measured every morning after waking and before breakfast by letting them chew on cotton. The cortisol level in the saliva was then measured in the lab. Our research question is by how much cortisol levels rise in athletes that prepare for the Olympics.

Three groups were studied. One group consisted of 50 athletes who were selected to take part in the Olympics, one group consisted of 50 athletes that were very good but were not selected (Control group I) and one group consisted of 50 non-athlete spectators that were going to watch the games (Control group II). The research question was about what the differences are in average cortisol increase in these three groups: the Olympians, Control group I and Control group II.

In Table 13.6 you see part of the fictional data, the first 6 measurements on person 1 that belongs to the group of Olympians.

Table 13.6: Cortisol measures over time.
person group measure cortisol
1 Olympian 1 19
1 Olympian 2 20
1 Olympian 3 22
1 Olympian 4 23
1 Olympian 5 24
1 Olympian 6 22

When we plot the data, and use different colours for the three different groups, we already notice that the Olympians show generally higher cortisol levels, particularly at the end of the 20-day period (Figure 13.5).

We want to know to what extent the linear effect of time is moderated by group. Since for every person we have 20 measurements, the data are clustered so we use a linear mixed model. We’re looking for a linear effect of time, so we use the measure variable numerically (i.e., it is numeric, and we do not transform it into a factor). We also use the categorical variable group as a predictor. It is a factor variable with three levels, so R will automatically make two dummy variables. Because we’re interested in the interaction effects, we include both main effects of group and measure as well as their interaction in the model. Lastly, we control for individual differences in cortisol levels by introducing random effects for person.

model7 <- datalong %>%
lmer(cortisol ~ measure + group + measure:group + (1|person),
data = .,
REML = FALSE)
model7 %>% summary()
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: cortisol ~ measure + group + measure:group + (1 | person)
##    Data: .
##
##      AIC      BIC   logLik deviance df.resid
##   8969.8   9017.8  -4476.9   8953.8     2992
##
## Scaled residuals:
##     Min      1Q  Median      3Q     Max
## -3.5823 -0.6405 -0.0116  0.6621  3.3154
##
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  person   (Intercept) 0.9655   0.9826
##  Residual             0.9960   0.9980
## Number of obs: 3000, groups:  person, 150
##
## Fixed effects:
##                                  Estimate  Std. Error          df t value
## (Intercept)                     20.094018    0.153650  202.476943 130.778
## measure                          0.596989    0.005473 2850.000014 109.078
## groupControl group II           -0.226802    0.217293  202.476944  -1.044
## groupOlympian                   -0.402950    0.217293  202.476944  -1.854
## measure:groupControl group II    0.006383    0.007740 2850.000006   0.825
## measure:groupOlympian            0.411896    0.007740 2850.000006  53.216
##                                          Pr(>|t|)
## (Intercept)                   <0.0000000000000002 ***
## measure                       <0.0000000000000002 ***
## groupControl group II                      0.2978
## groupOlympian                              0.0651 .
## measure:groupControl group II              0.4096
## measure:groupOlympian         <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
##             (Intr) measur grCgII grpOly m:CgII
## measure     -0.374
## grpCntrlgII -0.707  0.264
## groupOlympn -0.707  0.264  0.500
## msr:grpCgII  0.264 -0.707 -0.374 -0.187
## msr:grpOlym  0.264 -0.707 -0.187 -0.374  0.500

In the output we see an intercept of 20.09, a slope of about 0.60 for the effect of measure, two main effects for the variable group (Control group I is the reference group), and two interaction effects (one for Control group II and one for the Olympian group). Let’s fill in the linear model equation based on this output:

\begin{aligned} \texttt{cortisol}_{ij} &= 20.09 + person_i + 0.60 \; \texttt{measure} - 0.23 \; \texttt{ContrGrII} - 0.40\; \texttt{Olympian} + \nonumber\\ &0.006 \; \texttt{ContrGrII} \; \texttt{measure} + 0.41 \; \texttt{Olympian} \; \texttt{measure} + e_{ij} \nonumber\\ person_i &\sim N(0, \sigma_p^2 = 0.97)\nonumber\\ e_{ij} &\sim N(0, \sigma_e^2 = 1.00) \nonumber\end{aligned}

We see a clear intraclass correlation of around $$\frac{0.9655}{0.9655+0.9960}= 0.49$$ so it’s a good thing we’ve included random effects for person. The expected means at various time points and for various groups can be made with the use of the above equation.

It’s aiding interpretation when we look at what linear effects we have for the three different groups. Filling in the above equation for Control group I (the reference group), we get:

\begin{aligned} \texttt{cortisol}_{ij} = 20.09 + person_i + 0.60 \times \texttt{measure} + e_{ij} \nonumber\end{aligned}

For Control group II we get:

\begin{aligned} \texttt{cortisol}_{ij} &= 20.09 + person_i + 0.60 \; \texttt{measure} - 0.23 + 0.006 \; \texttt{measure} + e_{ij} \nonumber \\ &= 19.86 + person_i + 0.606 \times \texttt{measure} + e_{ij} \nonumber\end{aligned}

And for the Olympians we get:

\begin{aligned} \texttt{cortisol}_{ij} &= 20.09 + person_i + 0.60 \; \texttt{measure} - 0.40 + 0.41 \; \texttt{measure} + e_{ij} \nonumber \\ &= 19.69 + person_i + 1.01 \times \texttt{measure} + e_{ij} \nonumber\end{aligned}

In these three equations, all intercepts are close to 20. The slopes are about 0.6 in both Control groups I and II, whereas the slope is around 1 in the group of Olympian athletes. For illustration, these three implied linear regression lines are depicted in Figure 13.6.

So based on the linear model, we see that in this sample the rise in cortisol levels is much steeper in Olympians than in the two control groups. But is this true for all Olympians and the rest of the populations of high performing athletes and spectators? Note that in the regression table we see two interaction effects: one for measure:groupControl group II and one for measure:groupOlympian. Here we’re interested in the rise in cortisol in the three different groups and to what extent these sample differences extend to the three populations.

A possible answer we find in the $$F$$-statistic of an ANOVA. When we run an ANOVA on the results,

model7 %>% anova()
## Type III Analysis of Variance Table with Satterthwaite's method
##               Sum Sq Mean Sq NumDF   DenDF    F value              Pr(>F)
## measure        54095   54095     1 2850.00 54313.3049 <0.0000000000000002 ***
## group              3       2     2  202.48     1.7285              0.1802
## measure:group   3703    1852     2 2850.00  1859.1587 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

we see a significant measure by group interaction effect, $$F(2, 2850) = 1859.16, p<.001$$. The null-hypothesis of the same cortisol change in three different populations can be rejected, and we conclude that Olympian athletes, non-Olympian athletes and spectators show a different change in cortisol levels in the weeks preceding the games.