请问：two-phase linear regression crossover model具体在SAS中如何实现？

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我在做必需氨基酸需要量方面的一个试验，现在数据统计分析上碰到一个难题，就是相关国外文献都是用一个“two-phase linear regression crossover model”的统计模型来确定两条直线的拐点（breakpoint)，从而得出该氨基酸的最低生理需要量。我也看了几本有关回归分析的SAS书籍，可是没有相关内容，更没有现成的程序模板，希望能得到高手的帮助！
相关背景：

This model selects for the minimum residual SE in a stepwise partitioning of data points between 2 regression lines. The first regression line has a slope and the second line is horizontal with minimal or no slope. The mixed procedure takes into account the correlations among observations made on the same participant and the possible heterogeneous variances among the measurements made on the same participant over time and is well suited for repeated-measures study designs (25). Different variancecovariance structures (simple, compound symmetry, unstructured, first order auto-regressive, and first order ante-dependence) were compared using the information criteria (Akaike Information Criteria, the finite sample corrected Akaike Information Criteria, and the Schwarz’s Bayesian Information Criteria). The final model that best fit the data with the lowest SE, lowest root mean square error, and highest r2 identified the breakpoint estimate or requirement for lysine. The safe
intake of lysine (upper 95% CI, equivalent to the Recommended Dietary Allowance) was calculated using Fieller’s theorem (26). Briefly, 95% CI = breakpoint 6 tdf,a/2 3 SE, where SE is the SE of the breakpoint, df is associated with the residual mean square of the best fit model, and a is the 95% confidence level. Upper 95% CI was calculated using SAS (SAS/STAT version 8.2, SAS Institute).

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关键词：regression Crossover regressio regress Linear crossover 回归分析 between minimum points

有关“two-phase linear regression crossover model”背景的进一步补充

本人在一篇83年的文献中，找到了对该统计模型的描述。
Plots of amino acid oxidation v.s dietary amino acid level indicated that the points could be segregated into two lines, one with almost zero slope and the other with a marked slope. The two lines were fitted to the values using the model described by Seber (1977):
Yi = a1 + b1Xi + (a2 –a1)c + (b2 – b1)cXi + ei,
Where Yi is the amino acid oxidation rate, a1 and a2 are the intercepts of the first and second lines respectively, b1 and b2 are the gradients of the first and second lines respectively, c is a coefficient having a value of 0 for the first line and 1 for the second line, and ei is the residual error.
The standard errors were proportional to the means, and so the observations were weighted by an estimate of the sample variance (Draper & Smith, 1981) which was estimated by regressing the observed sample variances over the corresponding sample means.
The allocation of the dietary amino acid levels between the first and second line was selected to give a minimum value for the residual error. Thus the regression procedure allowed and objective assessment of the change-over point, and the 95% confidence limits of the dietary amino acid level corresponding to the change-over point could be calculated (Seber, 1977).
今天在本论坛上成功下载了Seber的《Linear regression analysis》（第二版），在该书的PP159对该模型的统计原理有介绍，但具体在SAS中如何实现还是不清楚，在此求助大家啊。急急急！

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