Hi, I have a dataset where the response for each person on one of the 2 
treatments was a proportion (percentage of certain number of markers being 
positive), I also have the number of positive & negative markers available for 
each person. what is the best way to analyze this kind of data?

I can think of analyzing this data using glm() with the attached dataset:

test<-read.table('test.txt',sep='\t')
fit<-glm(cbind(positive,total-positive)~treatment,test,family=binomial)
summary(fit)
anova(fit, test='Chisq')

First, is this still called logistic regression or something else? I thought 
with logistic regression, the response variable is a binary factor?

Second, then summary(fit) and anova(fit, test='Chisq') gave me different p 
values, why is that? which one should I use?

Third, is there an equivalent model where I can use variable "percentage" 
instead of "positive" & "total"?

Finally, what is the best way to analyze this kind of dataset where it's almost 
the same as ANOVA except that the response variable is a proportion (or success 
and failure)?

Thanks

John



      
"treatment"     "total" "positive"      "percentage"
"1"     "exposed"       11      4       0.363636363636364
"2"     "exposed"       10      4       0.4
"3"     "exposed"       9       4       0.444444444444444
"4"     "exposed"       7       4       0.571428571428571
"5"     "exposed"       7       4       0.571428571428571
"6"     "exposed"       6       5       0.833333333333333
"8"     "exposed"       12      7       0.583333333333333
"9"     "exposed"       8       5       0.625
"10"    "exposed"       13      12      0.923076923076923
"11"    "exposed"       10      5       0.5
"12"    "control"       10      1       0.1
"13"    "control"       11      2       0.181818181818182
"14"    "control"       8       0       0
"16"    "control"       12      1       0.0833333333333333
"15"    "control"       8       0       0
"17"    "control"       10      1       0.1
"18"    "control"       10      1       0.1
"19"    "control"       8       1       0.125
"20"    "control"       8       0       0
"21"    "control"       9       1       0.111111111111111
"22"    "control"       10      1       0.1
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