[R] How to do adjust for sex, age, genotype for a data

[Charles C. Berry]

On Fri, 17 Jul 2009, 1Rnwb wrote:

>
> then what will be the other factors needed to be adjusted

It is NOT an exaggeration to say that hundreds of research 
papers, dozens of books, and many dissertations have been written on how 
to go about answering that question in one context or another.

Given the background you say you have, I doubt that any advice you will 
get from this list will enable you to craft a good answer.

What you really need is collaboration with or mentoring from someone 
who is expert in these matters and willing to dig into the particulars of 
your research area.


and whether I
> should adjust or use them as covariates.

Usually, these amount to the same thing.


Finally how these analysis will be
> done in R

If you are doing this yourself you will probably need guidance from a well 
crafted monograph. Quite a few are listed at

 	http://www.r-project.org/doc/bib/R-books.html


HTH,

Chuck

>
>
> Harrell, Frank E wrote:
>>
>> 1Rnwb wrote:
>>> Hello R gurus,
>>>
>>> I am biologist doing biomarker research and I have a data set where I
>>> have 6
>>> proteins and close to 3000 samples, i have to look for differences
>>> between
>>> disease(Y) and controls(N) along with genetic risk, genotypes, sex and
>>> other
>>> demographic info available. however i do not know any of the statistics
>>> to
>>> do the adjustment for sex, age, genotype, genetic risk. I have been
>>> reading
>>> in papers where the authors are talking about adjusting for age, sex,
>>> genotype, genetic risk. The CDC website suggests for adjusting the age
>>> using
>>> the weights, but I am not sure as this would apply to my data. one
>>> website
>>> says that if the distribution is not equal then one has to model sex, age
>>> and other demographic parameters as co-variates. I would appreciate if
>>> someone can help me to understand this more clearly and provide
>>> directions
>>> on modeling these to do my analysis. I am attaching a sample data file
>>> with
>>> this post. Thanks
>>> http://www.nabble.com/file/p24534963/Sample%2Bdata.csv Sample+data.csv
>>
>> If the only clinical variables you are adjusting for are age and sex
>> this analysis will be misleading at best.
>>
>> Frank
>>
>> --
>> Frank E Harrell Jr   Professor and Chair           School of Medicine
>>                       Department of Biostatistics   Vanderbilt University
>>
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>>
>
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>
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>

Charles C. Berry                            (858) 534-2098
                                             Dept of Family/Preventive Medicine
E mailto:cberry at tajo.ucsd.edu	            UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/  La Jolla, San Diego 92093-0901