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※ 发信站: 批踢踢实业坊(ptt.cc)
: ◆ From: 120.126.38.177
: → yhliu:因为这些 sub-tables 的边际分布不同, 若不做控制(分层), 02/02 17:25
: → yhliu:合并表的 marginal odds-ratio 会与 conditional odds-ratio 02/02 17:26
: → yhliu:不同. 就像 y=a+b1*x1+b2*x2+e 的 b1 与 y=a'+b'*x1+e' 的 02/02 17:27
: → yhliu:b' 会不同. 02/02 17:28
很感谢您的回答! 我好像有点懂了..(虽然没想到实际的例子)
另外请教个问题，这篇文章
http://udel.edu/~mcdonald/statcmh.html
他说如果只是要做 hypothesis testing 则 C-M-H 不需要要求各
sub-table odd-ratio 相同，如果需要推估 odd ratio 值则需要，这个
说法您觉得可以认同吗?
节录两段於下:
Some statisticians recommend that you test the homogeneity of the
odds ratios in the different repeats, and if different repeats
show significantly different odds ratios, you shouldn't do the
Cochran–Mantel–Haenszel test. [略]
Other statisticians will tell you that it's perfectly okay to use
the Cochran–Mantel–Haenszel test when the odds ratios are
significantly heterogeneous. The different recommendations depend
on what your goal is. If your main goal is hypothesis testing—
you want to know whether legwarmers reduce pain, in our example—
then the Cochran–Mantel–Haenszel test is perfectly appropriate.
A significant result will tell you that yes, the proportion of
people feeling ankle pain does depend on whether or not they're
wearing legwarmers. If your main goal is estimation—you want to
estimate how well legwarmers work and come up with a number like
"people with ankle arthritis are 50% less likely to feel pain if
they wear fluorescent pink polyester knit egwarmers"—then it
would be inappropriate to combine the data using the Cochran–
Mantel–Haenszel test. If legwarmers reduce pain by 70% in the
winter, 50% in the spring, and 30% in the summer, it would be
misleading to say that they reduce pain by 50%; instead, it would
be better to say that they reduce pain, but the amount of pain
reduction depends on the time of year.
我目前想要做得不是要去推估 odd ratio，只是想知道 X, Y 有没有相关
所以依照这个讲法应该可以不管 odd ratio 直接使用 C-M-H。
另外还有一个问题，我觉得我的问题应该是 two-tail，我要测约两万笔
资料(基因)，对某些基因来讲，可能是某个方向，其他可能是别的方向，
，但 two-tail C-M-H 是 significant 的时候，我需要知道他是哪个方向，
但我又不要去计算它统一的 odd ratio 时，我要怎麽判断呢?
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※ 发信站: 批踢踢实业坊(ptt.cc)
◆ From: 120.126.38.177
※ 编辑: huggie 来自: 120.126.38.177 (02/03 14:12)
1F：→ bmka:hypothesis testing 跟estimation 不是同一件事 02/03 20:58
2F：→ yhliu:从 C-M-H 统计量的公式来说, 如果各 sub-table 的关联方向 02/03 23:05
3F：→ yhliu:不一, 或有许多 sub-tables 的关联太小, 则此检定效力受影响 02/03 23:09
4F：→ yhliu:尤其是前一种情形, 正负向关联会相互抵消, 使重要的条件关联 02/03 23:10
5F：→ yhliu:无法检测出来. 02/03 23:11
6F：→ yhliu:Conditional independence against full model 的检定可用 02/03 23:11
7F：→ yhliu:sum of chi-squares 或其他加总型的统计量. 02/03 23:12