MartinM said:
Like some smart fellow said earlier, 'that's why you have statistical analysis'
It's not valid to simply look at the differences and try to eyeball a pattern.
Yes, it is. There are plenty of papers in the scientific literature with data like Byrd's, where you also look at the trend.
Remember, the meaning of statistical insignificance is that we can't, to any reasonable degree of certainty, assert that the sample difference represents a true population difference. From the point of view of the population, those numbers may as well be identical. So trying to eyeball a pattern in the sample data and extrapolate to the population won't work. If there is a real pattern in there, we need a rigourous analysis to get at it.
Martin, remember that we are supposedly looking at a phenomenon that has NO effect. That means that we should see equal variations around the mean. It means that, if we see 6 significant positive effects, we should also see 6 significant NEGATIVE effects.
Now, Byrd used a two-tailed test. If he had used a one-tailed test, which would have been valid since all the anecdotal data say IP is beneficial, never harmful, then he would have had 20 out of 26 categories significantly different.
I'm not entirely sure from your comments, but you seem to be looking at correlations between the significant results only. Since the logistic regression used all 29 parameters from table 2, we need to look at dependence between any and all of them.
It was you and Posner who looked at interdependence between teh significat results only. I was only dealing with the examples you gave.
Remember, multiple regression works by varying one predictor while holding all the others constant.
Let's first remember what Byrd used:
"A stepwise logistic regression was used for the multivariate analysis"
So, it was not multiple regression but stepwise logistic regression for a multivariate analysis.
Your insistence that the variables must be independent doesn't seem to apply to logistic regression. In fact, that is one of its strengths:
"A range of techniques have been developed for analysing data with categorical dependent variables, including discriminant analysis, probit analysis, log-linear regression and logistic regression."
http://www.ex.ac.uk/~SEGLea/multvar2/disclogi.html
http://www.srf.tuwien.ac.at/feil/log.pdf
Of course, Byrd could have tested each category separately using these.
And even if such could be shown, the small sample size is still an issue.
What small sample size?
[/QUOTE] Another, more fundamental, problem occurred to me last night. While Byrd ensured that the groups were well-matched in terms of age, length of stay and various pre-existing medical conditions, he didn't appear to check other confounding factors such as diet, body weight, excercise regime, smoking and drinking habits, etc. etc. Without some control over these factors, the results are weak, to say the least.[/QUOTE]
All of those would be subsumed under what he did test for. Since pre-existing heart conditions (what was tested for) are DEPENDENT on the confounding factors, then by your criteria of dependence, these should have been equally distributed.
Also, as you forget, assignment was random therefore there is no reason to suppose that, in a sample size this large that the parameters you mentioned would not be equally distributed. That the pre-existing medical conditions dependent on these parameters was matched in Table 1 shows this is not a problem. For instance, hypertension is dependent on all the variables you mentioned. Cirrhosis of the liver is dependent on drinking.
You are reaching for straws here. I suppose you want to tell me about problems with C-14 analysis, too?