Peter
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Quantile Regression will be proposed by the joint FDA-Industry working
group of the Product Quality Research Institute for establishing
expiration periods for drugs.
If reader's can identify commercially available programs where Quantile
Regression is available, I would be grateful.
Thank you.
Regards,
Stan Alekman
HI:
Marc
R has the excellent 'quantreg' package by Roger Koenker:
http://cran.us.r-project.org/web/packages/quantreg/index.html
and if you have any concerns about using R for regulated clinical trials, read:
http://www.r-project.org/doc/R-FDA.pdf
The FDA is using R internally on an increasing basis. R was used internally by the FDA for the Avandia safety meta-analysis and it does not get more high profile than that.
HTH,
Marc Schwartz
Stan Alekman
http://cran.us.r-project.org/web/packages/quantreg/index.html
http://www.r-project.org/doc/R-FDA.pdf
HTH,
Marc Schwartz
--
~~~~~~~~~~~
Scott R Millis, PhD, ABPP, CStat, CSci
Professor
Wayne State University School of Medicine
Email: aa3...@wayne.edu
Email: srmi...@yahoo.com
Tel: 313-993-8085
--- On Wed, 11/24/10, Stan Alekman <stanl...@aol.com> wrote:
Diana Kornbrot wrote
<<<<
My mistake.
quantile regression is similar to ordinal regression as implemented in SPSS plum, but not the same
Ordinal regression seems to me more useful in many situation, as the cut-points are determined by the data rather than arbitrary quantiles.
Interested in pros & cons of these two methods from experts.
>>>>
I don’t know SPSS and plum, but in what I’ve seen, ordinal logistic regression uses data that is already categorized when it comes in, whereas quantile regression models the quantiles rather than the mean. In SAS, if you try ordinal regression (PROC LOGISTIC) on continuous data you will get a model with a different intercept for each level of the dependent variable. This is not good, obviously. Quantie regression (PROC QUANTREG) lets you model the specific quantiles that you want, and gives you parameter estimates for all IVs for each quantile. In addition, ordinal logistic regression (in its most common form) makes the proportional odds assumption. Quantile regression does not. In fact, QUANTREG seems more like multinomial than ordinal logistic.
In addition, QUANTREG and LOGISTIC give output in very different forms: the former looks like OLS regression – only it’s modeling something other than the mean; the latter give parameter estimates that need to be plugged into logistic equations, and give odds ratios, which don’t really translate.
Finally, the two PROCs are modeling different things. QUANTREG models a particular quantile; LOGISTIC models being in a particular category.
So, now I am wondering what plum in SPSS does
<<<
But here is an example
Have data set where want to determine survival as a function of some continuous variable that has been categorized into 3 ordinal groups. Should one choose the groups boundaries as the 33rd & 66th quantile? Os should one choose known levels of the continuous variable that approximately divide data into thirds?
If study is replicated or compared with a similar study the tertiles will inevitably fall at different values of the predictor variable, whereas the cutpoints as values on the predictor will remain the same. Hence a more accurate comparison will be possible.
In medical appplications people often seem to use tertiels or quartiles, whereas known values of the predcitor peg a cancer marker would in my view be better.
>>>>
I think these are BOTH bad options. Continuous data should only be categorized based on some strong theoretical grounds. One example I run into often is birthweight data. Babies under 2.5 kg are “low birthweight”, those above are “normal”. This is nonsensical but nearly universal. It treats a baby who weighs 1 kg as identical to one who weighs 2.49 kg, and one who is 2.51 kg as identical to one who is 4 kg. I have explained this to clients who then say “You’re right, that makes no sense, but it’s what everyone does, so we’re going to do it”.
Sometimes there are practical reasons for categorizing data, and these may be important. For instance, if you are asking about income, the responses are nearly always in categories. Not only are people more willing to answer when it is formulated this way, but many do not know their precise incomes, so they will be guessing anyway. In addition, since income data is very highly skew, it will have to be transformed anyway.
Happy Thanksgiving!
Peter