Why don't you solve your personal issues privately?

Thanks!

Marco Costantini

>----Messaggio originale----
>Da: [log in to unmask]
>Data: 27/05/2009 15.59
>A: <[log in to unmask]>
>Ogg: Re: continuous vs. categorical
>
>You still have not even told us though which mistakes these are. So, if you
>are only able to tell people that they are mistaken, but cannot poinpoint
>the mistakes, do not reply at all in the first place and do not waste my
>time.
>
>2009/5/26 Brian G Miller <[log in to unmask]>
>
>> You do need help, but it's not my job to correct your errors, of which
>> there are several.  I could collaborate on your research, but I suspect
>> you couldn't afford my day rates.
>>
>> Sorry I don't have time for this.
>>
>> Brian Miller
>>
>> -----Original Message-----
>> From: - [mailto:[log in to unmask]]
>>  Sent: 21 May 2009 12:06
>> Subject: Fwd: continuous vs. categorical
>>
>> Thank you.
>> Could you be more specific on which misconceptions exactly I harbour on
>> basic statistical concepts and distributional assumptions? The answers
>> are
>> not helpful if they are not specific, and the problem I described is
>> very
>> specific.
>>
>> ---------- Forwarded message ----------
>> From: Brian G Miller <[log in to unmask]>
>> Date: 2009/5/19
>> Subject: RE: continuous vs. categorical
>> To: - <[log in to unmask]>
>>
>>
>> Dear contributor
>>
>> From your description of the problem, it appears that you harbour a
>> number of misconceptions about basic statistical concepts concerning
>> regression and distributional assumptions.  I think you'll continue to
>> struggle unless you get professional advice from someone familiar with
>> the topic.  If you're still at college, that would be your supervisor or
>> statistical adviser.
>>
>> Brian Miller
>>
>> -----Original Message-----
>> From: - [mailto:[log in to unmask]]
>> Sent: 15 May 2009 13:52
>> Subject: continuous vs. categorical
>>
>>  hello all,
>> I wonder if someone could help me with the following:
>> - I am doing some analysis on weight and BMI examining association with
>> gastrointestinal cancer. Weight and BMI are both continuous variables
>> and I
>> am also using them as grouped variables, grouping based on the control
>> distribution. I do logistic regression. To get the p for trend we know
>> that
>> we fit the grouped variable as continuous. In this case I also have the
>> original values of the variables, i.e. I have the continuous variable A
>> and
>> the generated grouped variable B. I tried not only fiting B as
>> continuous,
>> but also using the original variable A. I know that when the
>> distribution is
>> skewed you are better off using the grouped variable. In this case, when
>> I
>> examine weight either using A or B, I get significance for both cases.
>> When
>> I examine BMI using A I get significance, but using B I get no
>> significance.
>> Looking at the distribution of the cases and controls together, it is
>> indeed
>> skewed. So, I'm thinking that the valid results are the ones showing no
>> significance i.e. those I get from fiting the grouped variable as
>> continuous. What do you think?
>>
>> - I have a very large sample so it is expected that the distributions of
>> weight and BMI will be normal, under a null hypothesis. But, since we
>> know
>> that nowadays people tend to be more fat than was the case decades ago,
>> irrespective of medical conditions, is it correct to expect normality
>> under
>> the null? My point being that if the distribution is skewed already
>> under
>> the null then maybe we don't have to remedy the skewness in the actual
>> data,
>> so maybe it's not best to use the grouped variable here instead of the
>> continuous.
>>
>> I would appreciate your comments on this, and will compile the answers I
>> get.
>>
>> Thank you all
>>
>>
>> Dr Brian Miller
>> Principal Epidemiologist
>>
>> Institute of Occupational Medicine
>> Research Avenue North
>> Riccarton
>> Edinburgh
>> EH14 4AP
>>
>> Tel: 0131 449 8044
>> Fax: 0870 850 5132
>> Email: [log in to unmask]
>>
>> Web: http://www.iom-world.org
>>
>>
>>
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>