Background

This vignette provides an overview of the default prior settings and demonstrates how to customize the prior mean and standard deviation for D-score calculations. This is an advanced topic that requires a basic understanding of the D-score calculation process. If you are unfamiliar with the D-score methodology, we recommend reviewing the introductory vignettes before proceeding.

Default Prior Mean and Standard Deviation

The default prior mean and standard deviation for the dscore() function are determined by the key argument. This function searches for the corresponding base_population field in the builtin_keys data frame, which contains several columns including the following:

builtin_keys[, c("key", "base_population")]
##         key       base_population
## 1     dutch                 dutch
## 2      gcdg                  gcdg
## 3  gsed1912                  gcdg
## 8     293_0                phase1
## 10 gsed2212                phase1
## 11 gsed2406 preliminary_standards

For instance, for key = gsed2406, the base_population is identified as "preliminary_standards". The get_mu() function returns the prior mean for the specified key at different ages:

get_mu(t = c(0:12)/12, key = "gsed2406")
##  [1]  9.731266 14.922704 19.282654 23.155214 26.707086 30.031809 33.187157
##  [8] 36.211330 39.130903 41.965115 45.158164 47.235287 49.186179

This code snippet returns the prior mean for ages ranging from 0 to 12 months. These mean values represent the median of the D-score distribution for the specified base_population under the current key.

If the standard deviation of the prior is not specified, the dscore() function defaults to a value of 5.0 across all ages. In comparison, the age-specific standard deviation for the base_population averages around 2.5 to 3.5. Therefore, a standard deviation of 5.0 signifies a relatively broad prior distribution, regardless of age.

It’s crucial to note that altering the key parameter changes both the prior mean and standard deviation. Since these parameters affect the D-score, comparisons should generally be made only between D-scores calculated using the same key, prior mean, and standard deviation.

Setting Your Own Prior Mean and Standard Deviation

In certain situations, you may want to define your own prior mean and standard deviation for the D-score calculations. This can be done by setting the prior_mean and prior_sd arguments in the dscore() function. Below are a few examples that demonstrate how to customize these priors.

Example 1: Custom Prior Mean

In this example, we add a value of 5 to the default prior mean for each child, which results in higher D-scores.

# Calculate the custom prior mean by adding 5 to the default prior mean
data <- milestones
mymean <- get_mu(t = data$age, key = "gsed2406") + 5 # Calculate default D-scores def <- dscore(data) head(def) ## a n p d sem daz ## 1 0.4873 11 0.9091 30.76 3.751319 -0.633 ## 2 0.6571 14 0.6429 29.06 2.518082 -2.716 ## 3 1.1800 19 0.9474 53.35 3.414966 -0.006 ## 4 1.9055 13 0.8462 63.88 2.971594 -0.094 ## 5 0.5503 11 0.8182 28.75 3.476988 -1.863 ## 6 0.7666 14 0.7857 34.21 3.088920 -2.377 # Custom prior, direct specification adj1 <- dscore(data, prior_mean = mymean) head(adj1) ## a n p d sem daz ## 1 0.4873 11 0.9091 33.88 4.146874 0.310 ## 2 0.6571 14 0.6429 30.38 2.629683 -2.423 ## 3 1.1800 19 0.9474 55.93 3.774148 0.787 ## 4 1.9055 13 0.8462 65.78 3.203216 0.438 ## 5 0.5503 11 0.8182 31.43 3.840857 -1.155 ## 6 0.7666 14 0.7857 36.30 3.395857 -1.867 # Custom prior, column specification adj2 <- dscore(cbind(data, mymean), prior_mean = "mymean") head(adj2) ## a n p d sem daz ## 1 0.4873 11 0.9091 33.88 4.146874 0.310 ## 2 0.6571 14 0.6429 30.38 2.629683 -2.423 ## 3 1.1800 19 0.9474 55.93 3.774148 0.787 ## 4 1.9055 13 0.8462 65.78 3.203216 0.438 ## 5 0.5503 11 0.8182 31.43 3.840857 -1.155 ## 6 0.7666 14 0.7857 36.30 3.395857 -1.867 identical(adj1, adj2) ## [1] TRUE In this code, the prior_mean argument shows two forms. The first form directly specifies the custom prior mean, while the second form refers to an additional column in the data frame that contains the user-specified prior means. Both specifications yield identical results. In addition, the user can specify a scalar value for the prior_mean argument, which will be applied to all observations, but this option is unreasonable if ages vary across observations. The next snippet compares the adjusted and default D-scores as a function of the proportion of items passed by the child. # Plot the difference between adjusted and default D-scores plot(y = adj1$d - def$d, x = def$p,
xlab = "Proportion of items passed by the child",
ylab = "Upward drift of D-score",
pch = 16, main = "Impact of Custom Prior Mean on D-score")

# Add a smoothed line to visualize the trend
lines(lowess(x = def$p, y = adj1$d - def$d, f = 0.5), col = "grey", lwd = 2) The plot illustrates that the upward bias is more pronounced when less informative items are administered, i.e., when the proportion of items passed is either very low (not shown) or very high. The bias is relatively mild (one D-score unit increase) when the child can perform about half of the items. Example 2: Setting a Custom Prior Standard Deviation In some situations, we may have strong prior beliefs about the variability of the D-scores based on factors such as the trajectory of a child’s D-score or expert knowledge. Incorporating this information can lead to more robust or smooth results by better reflecting our understanding of the variability. The following code snippet demonstrates how to set a custom prior standard deviation. Here, the prior_sd argument is specified using a constant value or values derived from the data. # Filter data for a specific child boy <- milestones[milestones$id == 111, ]

# Calculate default D-scores
def <- dscore(boy)
def
##        a  n      p     d      sem    daz
## 1 0.4873 11 0.9091 30.76 3.751319 -0.633
## 2 0.6571 14 0.6429 29.06 2.518082 -2.716
## 3 1.1800 19 0.9474 53.35 3.414966 -0.006
## 4 1.9055 13 0.8462 63.88 2.971594 -0.094

Suppose we want to inform the estimation process by the previous observation. We can use the location of the last observation (in DAZ units) and calculate an informative mean and standard deviation for the next time point as follows:

# Calculate expected D-scores and standard deviations
exp_d <- zad(z = c(0, def$daz[1:3]), x = def$a)
exp_sd <- c(5, def$sem[1:3]) # Calculate adjusted D-scores using the custom prior mean and standard deviation adj1 <- dscore(boy, prior_mean = exp_d, prior_sd = exp_sd) The code snippet below plots the raw and informed DAZ trajectories for child 111: # Plotting the raw and informed DAZ trajectories plot(x = def$a, y = def$daz, type = "b", pch = 16, ylab = "DAZ", xlab = "Age (years)", main = "Standard (black) and Informed (red) DAZ-trajectory for child 111") points(x = adj1$a, y = adj1$daz, col = "red", type = "b", lwd = 2, pch = 16) This plot illustrates the DAZ trajectory using standard estimates (in black) and the adjusted estimates (in red) for child 111, highlighting the impact of incorporating more informative prior knowledge into the analysis. Of course, the examples provided here are simplified and may not fully capture the complexity of real-world scenarios. However, they demonstrate how to customize the prior mean and standard deviation in the dscore() function to better reflect your prior knowledge and improve the accuracy of the D-score estimates. Handling Missing Ages By default, the D-score of observations with missing ages will be NA. It is possible to force D-score calculation by setting prior_mean_NA and prior_sd_NA to a specific value. The documentation for the dscore() function states that prior_mean_NA = 50 and prior_sd_NA = 20 as reasonable choices for samples between 0-3 years. If these defaults are not suitable for your data, you can customize them to better reflect your expectations. Example 3: Customizing Prior Mean and Standard Deviation for Missing Ages # Set missing ages for specific observations boy$age[2:3] <- NA

# Calculate D-scores using default
def <- dscore(boy)
def
##        a  n      p     d      sem    daz
## 1 0.4873 11 0.9091 30.76 3.751319 -0.633
## 2     NA 14 0.6429    NA       NA     NA
## 3     NA 19 0.9474    NA       NA     NA
## 4 1.9055 13 0.8462 63.88 2.971594 -0.094

This call to dscore() produces a D-score of NA when age data is missing, which effectively excludes these cases from downstream analyses. This is the safest option, and the default behavior.

# Calculate D-scores for missing ages using age-independent priors
adj1 <- dscore(boy, prior_mean_NA = 50, prior_sd_NA = 20)
##        a  n      p     d      sem    daz
## 1 0.4873 11 0.9091 30.76 3.751319 -0.633
## 2     NA 14 0.6429 26.51 2.693178     NA
## 3     NA 19 0.9474 54.25 5.061741     NA
## 4 1.9055 13 0.8462 63.88 2.971594 -0.094

This call to dscore() uses custom settings prior_mean_NA = 50 and prior_sd_NA = 20, which are suggested age-independent values for children with missing ages between 0 and 3 years.

# Forcing D-scores for missing ages to value -1
adj2 <- dscore(boy, prior_mean_NA = -1, prior_sd_NA = 0.001)