کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1150273 | 957921 | 2006 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
The adjustment of random baseline measurements in treatment effect estimation
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
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چکیده انگلیسی
The analysis of covariance (ANCOVA) is often used in analyzing clinical trials that make use of “baseline” response. Unlike Crager [1987. Analysis of covariance in parallel-group clinical trials with pretreatment baseline. Biometrics 43, 895-901.], we show that for random baseline covariate, the ordinary least squares (OLS)-based ANCOVA method provides invalid unconditional inference for the test of treatment effect when heterogeneous regression exists for the baseline covariate across different treatments. To correctly address the random feature of baseline response, we propose to directly model the pre- and post-treatment measurements as repeated outcome values of a subject. This bivariate modeling method is evaluated and compared with the ANCOVA method by a simulation study under a wide variety of settings. We find that the bivariate modeling method, applying the Kenward-Roger approximation and assuming distinct general variance-covariance matrix for different treatments, performs the best in analyzing a clinical trial that makes use of random baseline measurements.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Statistical Planning and Inference - Volume 136, Issue 12, 1 December 2006, Pages 4161-4175
Journal: Journal of Statistical Planning and Inference - Volume 136, Issue 12, 1 December 2006, Pages 4161-4175
نویسندگان
Xun Chen,