I assume that means you have two treatments, say A and B, can be either absent or present. The standard analysis codes them as -1 or +1 for absent or present, respectively. If you have observations in all 4 cells, you can write the following equation:

y(A,B) = b0 + b1*A + b2*B + b12*A*B + error.

This equation has 4 unknowns, b1, b1, b2 and b12. If you have all 4 cells in the 2x2 table, then you can estimate all 4 unknowns. If you have data for only 3 cells, the standard analysis pretends that b12 = 0 and estimates the other three. If you have only 2 cells, say (both absent) and (both present), the standard analysis can estimate b0 plus either of b1 or b2. However, in fact, these really estimate (b0+b12) and (b1+b2). To understand this, consult any good book that discusses confounding with 2-level fractional factorial designs.

To do this in R, use "lm", as

fit <- lm(y~A+B, data.frame(y=..., A=..., B=..,)

hope this helps. spencer graves

[EMAIL PROTECTED] wrote:

Hello,
I am planning a study with the main point to evaluate the interaction of two treatments, but for ethical reasons one cell is empty, that with patients receaving no treatment at all


Treatment B
+
-


Treatment A
+
a
b

-
c
-------



I am looking for functions in R to estimate the sample size and/or to conduct the analysis. I have just found an article from Byar in Statistics in Medicine for a 2^3 incomplete factorial design, but I would like not to discover again the wheel..
TIA
dr. Giovanni Parrinello
Section of Medical Statistics
Department of Biosciences
University of Brescia
25127 Viale Europa, 11
Brescia Italy
Tel: +390303717528
Fax: +390303701157




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