Timeline for Dealing with poorly estimated/missing explanatory variable values in GLMs
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Mar 24, 2014 at 16:27 | vote | accept | JupiterM104 | ||
| Mar 21, 2014 at 14:27 | comment | added | whuber♦ | Whether you count cases of mortality or not doesn't matter, because they are two equivalent ways of representing the same outcome. Your argument, applied to counting the number of survivors (rather than the number of deaths), implies that with a sample size of 0 you should use 0% as your estimate of survivorship. The obvious inconsistency between this and the 0% mortality claim made in (2) shows that your argument is inherently flawed. | |
| Mar 21, 2014 at 14:15 | comment | added | JupiterM104 | I don't think I can follow your argument/logic. Are you able to state this in a different way? | |
| Mar 19, 2014 at 14:49 | answer | added | StasK | timeline score: 2 | |
| Mar 19, 2014 at 14:23 | history | edited | JupiterM104 | CC BY-SA 3.0 |
edited title
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| Mar 19, 2014 at 14:19 | comment | added | whuber♦ | When the sample size is $0$, the disease has $100\%$ prevalence because--by your very argument--the absence of disease has $0\%$ prevalence :-). | |
| Mar 19, 2014 at 12:44 | history | asked | JupiterM104 | CC BY-SA 3.0 |