Re: Fw: Iowa Controls - Not Matched

[hflong@postoffice.pacbell.net]

Hormesis Defenders,

Field, representative of  authoritarian teaching, seems hopeless.



1. Location. Field ignores the fact that, whether intentional or not, selecting

Iowa,  the 1% outlier location not refuting LNT, determines outcome as surely as

selecting the one drug study in 20 that, by chance, will show benefit.

2.Timing. Field obviously does believe, "We conducted a superior study,

independent of Cohen's design or results" - but he did it after Iowa was known

to be atypical and perhaps fit LNT.

3. Biased controls. "The model included continuous variables for ---". He

trusted calculations, using "controls" smoking 32% when cases smoked 86%! Why do

we bother with double blind, placebo-controlled studies? Why not statistically

"adjust"?



I would not be alive today, if I had trusted calculations for the foot-launched

flying wing I flight-tested. The test pilots' guideline is, "Design is nothing,

construction is nothing, TESTING is everything."

I would have been unable to pull out of a dive (because air pressure deactivated

a too-flexible stabilizer). Field seems unable to pull out of a dive because of

pessures and too flexible a scientific stabilizer.



Next? How about a column, Robert? How about exploring SF General Hospital HIV +

outpatients already having T testing, Myron and John?



Howard Long



field wrote:



> Dr. Long.

>

> > >

> > >   Howard Long MD MPH, Family Doctor and Epidemiologist

> > >   363 St. Mary St., Pleasanton CA, 94566

> > >   (925) 846-4411, Fax 4524, Page 787-0253 hflong@pacbell.net

> > >

> > > Dear Professor Field,

> > >

> > > I have great respect for your scientific honor and the quality of the

> > > Iowa study.

> > > I am happy that you write, "I AM NOT A DEFENDER OF THE LNT THEORY".

> > >

> > > When opposite inferences are reasonable from similar studies,

> > > like your

> > > Iowa study and the N Shipyard Worker Study (also case control and 10 x

> > > as large as yours), I believe an experiment is needed.

> > >

> > > First, even if a drug company were to fund 100 clinical trials,

> > > (prospective and double blind) with N in each such that some trials

> > > would likely show p<.05 of chance results,but publish only  studies

> > > showing benefit, would it show the medicine effective?

> > > You studied the one location in 100 (Iowa women) having no negative

> > > correlation of radon and lung cancer mortality, in Cohen's

> > > study - 200 x

> > > as large, albeit ecologic.

> >

> > I trust that he is not suggesting that we chose to do our study in Iowa

> > because this was one of the states in the 50 or so in Cohen's study that

> had

> > a positive association.  The study was funded and done in Iowa because

> Iowa

> > is the ideal state to do such a study.  Perhaps, this later reason is why

> a

> > positive association has been observed in Iowa.

> >

> > The main argument against Cohen's studies is and always has been this:

> > aggregation in an ecologic study can lead to biased estimates.  The bias

> can

> > be so bad as to yield estimates with the wrong sign, as Jay Lubin has

> shown.

> > Increasing the sample size does not reduce the bias in any way; it simply

> > leads to more precise estimates.  In other words, you would be more

> > precisely estimating the wrong thing.

> >

> > I don't understand the analogy.  It only makes sense if we purposely

> picked

> > Iowa because it happened to have the change association, and we reported

> > Cohen's findings for Iowa.  Neither case is true.  We conducted a superior

> > study independent of Cohen's design or results.  Whether our findings

> agree

> > with the hypotheses generated in Cohen's ecologic study is irrelevant to

> me.

> > I am intimately aware of how the data was collected and analyzed in both

> > studies.  The results from Cohen's ecologic study are dubious because of

> the

> > increased risk of biases inherent in his study design.  Larger sample

> sizes

> > can not overcome bias (a clear case of quality versus quantity).

> >

> > >   .

> > > Second, in Topics Under Debate, Radiation Protection

> > > Dosimetry V95,1,p77

> > > you write in Rebuttal, "The participants' smoking histories

> > > do not need

> > > to match the smoking histories of the controls since the effect of

> > > smoking can be adjusted for using standard statistical methods." This

> > > follows Klaus Becker's Argument that "- in the Iowa Lung

> > > Cancer Study by

> > > Field et al 86% of the ling cancer cases were smokers, but only 32% of

> > > the controls." Ibid, p79. Our Professor of statistics at UCB PH, Bill

> > > Gaffney, would often remind us,

> > > "Know your assumptions!" In your study, the controls are not

> > > matched. I

> > > do not believe that here, "smoking can be adjusted for using standard

> > > statistical methods".

> >

> > Multiple logistic regression was used to model the effect of residential

> > radon exposure on lung cancer risk.  Included in the regression model were

> > variables to adjust for the effects of smoking.  Specifically, the model

> > included continuous variables for the length of time that individuals

> > smoked, the number of cigarettes smoked during that time, and time since

> > smoking cessation (for ex-smokers).

> >

> > Quoting Hosmer and Lemeshow's authoritative book, "Applied Logistic

> > Regression":

> >

> > "One generally considers a multivariate analysis for a more comprehensive

> > modeling of the data.  One goal of such an analysis is to statistically

> > adjust the estimated effects of each variable in the model for differences

> > in the DISTRIBUTION of and associations among the other independent

> > variables.  Applying this concept to a multivariate logistic regression

> > model, we may surmise that each estimated coefficient provides an estimate

> > of the log odds [of lung cancer] adjusting for all other variables

> [smoking]

> > included in the model."

> >

> > I would further suggest that there is no practical way to match cases and

> > controls so as to adjust for the effects of smoking on lung cancer risk.

> > Smoking is so strongly and intricately associated with lung cancer that

> one

> > would have to match on a multitude of factors, including intensity,

> > duration, and time since cessation.  Furthermore, there would undoubtedly

> be

> > other covariates such as age, education, second hand smoke, family

> history,

> > and occupational exposures that would not be matched and would have to be

> > controlled for in some fashion; i.e. multiple logistic regression.

> >

> > >

> > > I, unlike some cynical colleagues, believe that your intent,

> > > like mine,

> > > is to prevent lung cancer and advance science. I have in the past

> > > rationalized projects (like a method of colon cancer detection) more

> > > than I believe you have rationalized this power of statistics

> > > to correct

> > > for the difficulty in finding controls that match for smoking. I hope

> > > you will come to believe, as I do, that less smoking by controls may

> > > have been the reason they had less cancer, rather than the less radon.

> > >

> > > With continued respect and best wishes

> > >

> > > Howard Long

> > >