# Age-related reference interval

Command: | Statistics Reference intervals Age-related reference interval |

## Description

An age-related (age-specific or age-dependent) reference interval is a reference interval that varies with the patients' age.

The methodology that MedCalc uses to model a continuous age-related reference interval is based on the methods described by Altman (1993), Altman & Chitty, 1993 and Wright & Royston 1997.

The method includes the following steps:

- If the distribution of the measurements (the variable for which to establish a reference interval) shows skewness at different levels of age, the measurements are transformed logarithmically or using a Box-Cox power transformation.
- The transformed measurements are modelled on age using weighted polynomial regression (Altman & Chitty, 1994). This regression model gives the mean of the (transformed) measurements as a function of age: mean(age).
- The residuals of this regression model are calculated.
- The absolute residuals, multiplied by are modelled on age using weighted polynomial regression (Altman, 1993). This second regression model gives the standard deviation of the (transformed) measurements as a function of age: SD(age).
- For every age in the observed range, the reference interval is calculated by taking mean(age) ± z x SD(age). For a 95% reference interval z=1.96. If the data were initially transformed in step 1, the resulting values are backtransformed to their original scale.
- The model is evaluated by analyzing and plotting z-scores for all observations. The z-score for an observed value y is calculated by z = ( y - mean(age)) / SD(age) The z-scores should be normally distributed. If they are not, the model may not be appropriate and other powers for the polynomial model may be selected.

## Required input

The example makes use of the data on biparietal diameter (outer-inner) from Chitty et al., 1994. Data downloaded from http://www.stata.com/stb/stb38/sbe15/bpd.dta

### Measurements and age variables

- In the dialog box you identify the variable for the measurements and the variable for age. You can also enter or select a filter in order to include only a selected subgroup of cases in the statistical procedure, as described in the Introduction part of this manual.

### Reference interval options

- Report centiles: you can select the different centiles of interest. For example, for a 95% double sided reference interval you select the centiles 2.5 and 97.5.

### Powers for polynomial model

- Powers: select the powers for the polynomial model for Mean and for SD. The special value 0 means logarithmic transformation (base 10). For example when you select the powers 0, 1 and 2 the model will include Log(age), age
^{1}=age and age^{2}: Y = b_{0}+ b_{1}Log(age) + b_{2}age + b_{3}age^{2}The values b_{i}are the coefficients to be estimated by the software. The value b_{0}is the*constant*term in the regression model.

### Options for Measurements variable

**Logarithmic transformation**: if the measurements data require a logarithmic transformation (e.g. when the data are positively skewed), select the Logarithmic transformation option.**Box-Cox transformation**: this will allow to perform a Box-Cox transformation with the following parameters:**Lambda**: the power parameter λ**Shift parameter**: the shift parameter is a constant*c*that needs to be added to the data when some of the data are negative.- Button
**Get from data**: click this button to estimate the optimal value for Lambda, and suggest a value for the shift parameter*c*when some of the observations are negative. The program will suggest a value for Lambda with 2 to 3 significant digits.

x(λ) = ( (x+ *c*)^{λ}- 1) / λwhen λ ≠ 0 x(λ) = log(x+ *c*)when λ = 0 **Test for outliers**: select the method based on Reed et al. (1971) or Tukey (1977) to automatically check the data for outliers (alternatively select*none*for no outlier testing). The method by Reed et al. will test only the minimum and maximum observations; the Tukey test can identify more values as outliers. The tests will create a list of possible outlying observations, but these will not automatically be excluded from the analysis. The possible outliers should be inspected by the investigator who can decide to exclude the values (see Exclude & Include). For other methods for outlier detection see Outlier detection.

### z-scores

**Test for Normal distribution**: select a statistical test to evaluate if the distribution of the z-scores is compatible with a Normal distribution (see Tests for Normal distribution).

## Results

### Suspected outliers

The program produces a list of possible outliers of the measurements, detected by the methods based on Reed et al. (1971) or Tukey (1977). The method by Reed et al. tests only the minimum and maximum observations; the Tukey test can identify more values as outliers. Note that this does not automatically exclude any values from the analysis. The observations should be further inspected by the investigator who can decide to exclude the values. Click on the listed values (which are displayed as hyperlinks) to show the corresponding data in the spreadsheet (see Exclude & Include).

### Model summary

This table gives a summary of the model.

The first row shows the outcome variable.

- If no transformation was selected, the outcome variable is the measurements variable (e.g. BPD).
- If a logarithmic transformation was selected, the outcome variable is the base 10 logarithm of the measurements variable, and will be shown as Log(BPD).
- If a Box-Cox transformation was selected, the outcome variable is Box-Cox transformed measurements variable, and will be shown as (BPD+
*c*)^{λ}

Next the regression equation is given for Mean and SD of the outcome variable.

- If no transformation was selected, the equations directly give the estimated Mean and SD of the measurements variable.
- If a logarithmic transformation or Box-Cox transformation was selected, the equations give the estimated Mean and SD of the transformed measurements, and the results must be back-transformed to their original scale. MedCalc back-transforms the results automatically in the following table (Centiles) and graphs.

### Centiles

This table lists the centiles at different ages (for about 6 to 12 values of age).

Below this table there is a hyperlink to get a more comprehensive table in Excel format, for about 60 to 120 values of age. This Excel file includes the formulae for the different centiles and therefore can easily be shortened or expanded to your needs.

### Fitted equations for Mean and Standard Deviation

This table lists the details of the weighted regression for the Mean of the measurements and next for the Standard Deviation.

The different coefficients are listed with their standard error and P-value.

The P-values should not be given too much attention. Specifically they must not be used to decide if a term should remain or should be removed from the model. It is the magnitude of the coefficient itself that is of interest.

### z-scores

The analysis of the z-scores is an important step in the evaluation of how well the model fits the data.

**Range**: the lowest and highest value of z-scores.**Skewness**: the coefficient of Skewness (Sheskin, 2011) is a measure for the degree of symmetry in the variable distribution. The coefficient of Skewness should be close to 0 (see Skewness & Kurtosis).**Kurtosis**: The coefficient of Kurtosis (Sheskin, 2011) is a measure for the degree of tailedness (Westfall, 2014) in the variable distribution. The coefficient of Kurtosis should be close to 0 (see Skewness & Kurtosis).**Test for Normal Distribution**: The result of this test is expressed as 'accept Normality' or 'reject Normality', with P value.

If P is higher than 0.05, it may be assumed that the z-scores follow a Normal distribution and the conclusion 'accept Normality' is displayed.

## Graphs

### Scatter plot with centile curves

This plot shows a scatter diagram of the measurements versus age with the calculated mean (central line) and centile curves.

### z-scores

This graph shows the z-scores plotted against age.

Horizontal lines are drawn at z-scores of -1.645 and 1.645.

The central line (red in the example) is a 80% smoothed LOESS (Local Regression Smoothing) trendline.

The z-scores should not display any pattern and must be randomly scattered about 0 at all ages (Altman & Chitty, 1993). It is expected that 5% of cases lie above the line corresponding to z=1.645 and 5% of cases are expected to lie below the line corresponding with z=-1.645; and these cases should be randomly distributed across the observed age range. Any deviation from this indicates that the model needs modification.

In the graph's Info panel, the exact number of observations below z=-1.645 and above z=1.645 is reported.

## Literature

- Altman DG (1993) Construction of age-related reference centiles using absolute residuals. Statistics in Medicine, 12:917-924. [Abstract]
- Altman DG, Chitty LS (1993) Design and analysis of studies to derive charts of fetal size. Ultrasound in Obstetrics and Gynecology, 3:378-384. [Abstract]
- Altman DG, Chitty LS (1994) Charts of fetal size: 1. Methodology. British Journal of Obstetrics and Gynaecology, 101:29-34. [Abstract]
- Chitty LS, Altman DG, Henderson A, Campbell S (1994) Charts of fetal size: 2. Head Measurements. British Journal of Obstetrics and Gynaecology, 101: 35-43. [Abstract]
- Reed AH, Henry RJ, Mason WB (1971) Influence of statistical method used on the resulting estimate of normal range. Clinical Chemistry, 17:275-284. [Abstract]
- Tukey JW (1977) Exploratory data analysis. Reading, Mass: Addison-Wesley Publishing Company.
- Westfall PH (2014) Kurtosis as Peakedness, 1905 - 2014. R.I.P. The American Statistician 68:191-195. [Abstract]
- Wright EM, Royston P (1997) Simplified estimation of age-specific reference intervals for skewed data. Statistics in Medicine, 16:2785-2803. [Abstract]