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From |
Constantine Daskalakis <C_Daskalakis@mail.jci.tju.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: ANCOVA for pre post designs |

Date |
Tue, 23 Dec 2003 19:10:24 -0500 |

At 06:12 PM 12/23/2003, David Airey wrote:

This is a question for the biostatisticians on the list.In large trials, (1) should be fine (at least, in terms of no bias). But (2) or (4) may be more efficient.

I'm thinking of formulating a commentary on accepted research procedures in my area that I think could be improved by observing basic statistical arguments presented to researchers by biostatisticians.

It has been suggested that in a randomized clinical trial design with baseline (B) and followup (F) test measures comparing a control and treatment group (G), performing an ANOVA on the ratio pre/post is the worst choice of the 4 ways to deal with baseline differences:

(1) post: analyze F by G

(2) difference: analyze F-B by G

(3) ratio: analyze F/B by G

(4) ancova: analyze F = constant + b1*B + b2*G, for G differences

In light of biostatisticians' suggestion (e.g., Vickers, BMC Medical Research Methodology (2001) 1:6, http://www.biomedcentral.com/1471-2288/1/6) that method (4) above is preferred most and method (3) is least preferred, does it apply to "prepulse inhibition" literature?

(3) above is similar in flavor to (2) if you view it on the log scale, i.e.,

(logF-logB) by G (or, equivalently, log(F/B) by G).

A technical question is whether the original measurements (B and F), or their difference on the original scale, or their log-ratio (ie, difference of logs) more closely conforms to the assumptions of linear regression (normality of residuals, homoskedasticity).

Still, I wouldn't do it on (F/B) but rather on log(F/B) if that looks good.

There is a difference in the underlying scientific model and interpretation, of course.

Does the treatment work additively (ie, adds a fixed amount, no matter where you start)? If so, the difference (F-B) would be a good choice (constant additive treatment effect across all values of B). And you'll be talking about the (arithmetic) mean difference for treatment vs. control.

But if the treatment works multiplicatively (ie, increases/decreases your original B measurement by a certain percent), then log(F-B) would be better. And then, by exponentiating the regression coefficients etc, you'll be talking about geometric mean ratio for treatment vs. control.

Finally, the choice between (2) and (4) depends on the correlation between baseline and follow-up measurements. I think that when corr(B,F) < 0.5, then (4) turns out to be more efficient; otherwise, (2) is better. I believe there's a paper by Liang & Zeger on this.

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________________________________________________________________

Constantine Daskalakis, ScD

Assistant Professor,

Biostatistics Section, Thomas Jefferson University,

211 S. 9th St. #602, Philadelphia, PA 19107

Tel: 215-955-5695

Fax: 215-503-3804

Email: c_daskalakis@mail.jci.tju.edu

Webpage: http://www.jefferson.edu/medicine/pharmacology/bio/

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**References**:**st: ANCOVA for pre post designs***From:*David Airey <david.airey@vanderbilt.edu>

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