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Dec 3, 2020 at 0:04 history edited Rodrigo_BC CC BY-SA 4.0
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Dec 2, 2020 at 23:39 answer added user215517 timeline score: 1
Dec 2, 2020 at 23:28 history edited Rodrigo_BC CC BY-SA 4.0
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Dec 2, 2020 at 23:08 history edited Rodrigo_BC CC BY-SA 4.0
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Dec 2, 2020 at 22:54 history edited Rodrigo_BC CC BY-SA 4.0
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Dec 2, 2020 at 22:53 comment added Demetri Pananos The problem exists in the estimation of the effect. The significance doesn't really matter all that much. You're free to try and demonstrate that the cut off threshold of p=0.25 leads to the type of behaviour you expect, but I'm rather confident it will not.
Dec 2, 2020 at 22:51 history edited Rodrigo_BC CC BY-SA 4.0
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Dec 2, 2020 at 22:50 comment added Rodrigo_BC They use: "bivariable analysis is greater than an arbitrary value (often p = 0.05)"... I use p-value= 0.25 in bivariable analysis... I know that stepwise have a lot of critics but main interest is in bivariate regression...
Dec 2, 2020 at 22:47 comment added Demetri Pananos You're still subject to the criticisms in those papers. Enough evidence exists that these methods do not accomplish what users think they do. Use them at your own peril.
Dec 2, 2020 at 22:46 comment added Rodrigo_BC Only I use manual stepwise and main effects model. Thanks for papers...
Dec 2, 2020 at 22:46 history edited Rodrigo_BC CC BY-SA 4.0
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Dec 2, 2020 at 22:43 comment added Demetri Pananos I would not do this. Pre-filtering fails to account for confounding variables, and so your selection method could miss important effects. See this paper for more pubmed.ncbi.nlm.nih.gov/8699212. Finally, stepwise is a method fraught with faults. See Frank Harrell's problems with stepwise regression or these artilces journalofbigdata.springeropen.com/articles/10.1186/…
Dec 2, 2020 at 22:10 history edited Rodrigo_BC CC BY-SA 4.0
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Dec 2, 2020 at 22:00 history edited Rodrigo_BC CC BY-SA 4.0
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Dec 2, 2020 at 21:55 review First posts
Dec 2, 2020 at 23:01
Dec 2, 2020 at 21:54 history edited Rodrigo_BC CC BY-SA 4.0
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Dec 2, 2020 at 21:48 history asked Rodrigo_BC CC BY-SA 4.0