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This procedure is available in both the Analyse-it Standard and the Analyse-it Method Evaluation edition
Altman bland test determines the agreement between two variables.
The requirements of the test are:
Data in existing Excel worksheets can be used and should be arranged in the List dataset layout. The dataset must contain at least two continuous scale variables. If replicates are observed then a List dataset with grouped variables layout should be used to arrange the replicates for each variable.
When entering new data we recommend using New Dataset to create a new 2 variables dataset ready for data entry.
To start the test:
Excel 97, 2000, 2002 & 2003:
Select any cell in the range containing the dataset to analyse, then click Analyse on the Analyse-it toolbar, click Agreement then clickBland-Altman.
The report shows the number of cases analysed, and, if applicable, how many cases were excluded due to missing values. The number of replicates and repeatability SD/CV (depending on the Plot option, see below) of each variable is shown.
The scatter plot (see below) shows the observations of X plotted against Y. The Use replicates option determines how replicates for each variable, if available, are plotted.
The difference plot shows the difference between the variables against the average of the variables. The scatter of the differences around the zero line should be constant, if the scatter shows a funnel effect with the differences been larger for higher values you should consider using the % difference plot to try achieve a constant scatter.
To change the difference plot:
The difference plot (see below) is shown beneath the scatter plot.
A histogram of differences (see below), with a normal curve overlay to assist in judging whether the differences are normally distributed, is shown beneath the difference plot.
Bias is the average difference between the variables and should ideally be zero. Although, correcting for a non-zero bias is a simple matter of subtracting the bias from the variable.
The bias and a confidence interval are shown. A hypothesis test is also shown. The p-value is the probability of rejecting the null hypothesis, that the bias is equal to zero, when it is in fact true. A significant p-value implies that the bias is difference from zero.
The bias and confidence interval can also be overlaid on the difference plot.
To show bias overlay:
The difference plot (see below) shows the bias and confidence interval.
Limits of agreement measure the agreement between the variables. They represent a range in which a given percentage of the differences lie. Large limits of agreement show poor agreement between the variables. In some cases the poor agreement maybe due to poor repeatability (see below).
The limits of agreement and a confidence interval for each are shown.
The limits of agreement and confidence interval can also be overlaid on the difference plot.
To show the limits of agreement overlay:
The difference plot (see below) shows bias, limits of agreement, and confidence intervals.
When a variable is observed in replicate we can measure the repeatability. The repeatability as a SD or CV is shown along with the repeatability coefficient which shows the limit which we would expect the differences between two measurements to lie. If the variable shows poor repeatability we would expect the limits of agreement to be poor.
The repeatability can also be shown as a plot.
To show repeatability plots:
Repeatability plots (see below) are shown for the variables measured in replicate.