Sas 简明教程

SAS - Bland Altman Analysis

Bland-Altman 分析是一个验证两个旨在测量相同参数的方法之间的一致或不一致程度的过程。方法之间的相关性越高，表明数据分析中选择了足够好的样本。在 SAS 中，我们通过计算变量值的均值、上限和下限来创建 Bland-Altman 图。然后，我们使用 PROC SGPLOT 创建 Bland-Altman 图。

The Bland-Altman analysis is a process to verify the extent of agreement or disagreement between two methods designed to measure same parameters. A high correlation between the methods indicate that good enough sample has been chosen in data analysis. In SAS we create a Bland-Altman plot by calculating the mean, upper limit and lower limit of the variable values. We then use PROC SGPLOT to create the Bland-Altman plot.

Syntax

在 SAS 中应用 PROC SGPLOT 的基本语法是 −

The basic syntax for applying PROC SGPLOT in SAS is −

PROC SGPLOT DATA = dataset;
SCATTER X = variable Y = Variable;
REFLINE value;

以下是所用参数的描述 -

Following is the description of the parameters used −

  1. Dataset is the name of the dataset.

  2. SCATTER statement cerates the scatter plot graph of the value supplied in form of X and Y.

  3. *REFLINE * creates a horizontal or vertical reference line.

Example

在以下示例中，我们采用由新旧两种方法产生的两个实验结果。我们计算变量值的差异以及相同观测值的变量均值。我们还计算标准差值，以用于计算的上限和下限。

In the below example we take the result of two experiments generated by two methods named new and old. We calculate the differences in the values of the variables and also the mean of the variables of the same observation. We also calculate the standard deviation values to be used in the upper and lower limit of the calculation.

结果显示 Bland-Altman 图为散点图。

The result shows a Bland-Altman plot as a scatter plot.

data mydata;
input new old;
datalines;
31 45
27 12
11 37
36 25
14 8
27 15
3 11
62 42
38 35
20 9
35 54
62 67
48 25
77 64
45 53
32 42
16 19
15 27
22 9
8 38
24 16
59 25
;

data diffs ;
set mydata ;
/* calculate the difference */
diff = new-old ;
/* calculate the average */
mean = (new+old)/2 ;
run ;
proc print data = diffs;
run;

proc sql noprint ;
select mean(diff)-2*std(diff),  mean(diff)+2*std(diff)
into   :lower,  :upper
from diffs ;
quit;

proc sgplot data = diffs ;
scatter x = mean y = diff;
refline 0 &upper &lower / LABEL = ("zero bias line" "95% upper limit" "95%
lower limit");
TITLE 'Bland-Altman Plot';
footnote 'Accurate prediction with 10% homogeneous error';
run ;
quit ;

在执行以上代码后，我们将得到以下结果：

When the above code is executed, we get the following result −

bland altman 1

Enhanced Model

在上述程序的增强模型中，我们得到了 95% 置信水平曲线拟合。

In an enhanced model of the above program we get 95 percent confidence level curve fitting.

proc sgplot data = diffs ;
reg x = new y = diff/clm clmtransparency = .5;
needle x = new y = diff/baseline = 0;
refline 0 / LABEL = ('No diff line');
TITLE 'Enhanced Bland-Altman Plot';
footnote 'Accurate prediction with 10% homogeneous error';
run ;
quit ;

在执行以上代码后，我们将得到以下结果：

When the above code is executed, we get the following result −

bland altman 2