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From |
Paul Seed <paul.seed@kcl.ac.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Re: ancova for repeated designs |

Date |
Fri, 20 Aug 2004 16:45:07 +0100 |

I personally would tackle the problem using a form of regression with

dummy variables, rather than the -anova- command. This has several advantages:

It gives me more control over what is being estimated

It presents the results as estimates with CI, rather than as general tests

Using the robust option, particularly in conjunction with - xtgee-

controls for non-sphericity, heterogeneity etc, rather than just testing for a problem.

Starting with the original data set, a possible series of commands might be:

set matsize 500

* Check that x is indeed equal to y[1]

bys id (t): assert x[1] == x

bys id (t): assert y[1] == x

* This being so, the first time period can go.

* Set up dummy variables

* main effects of the groups

for num 1/3: gen gX = g == X if g ~= .

* main effects of time

* (not strictly needed, as only 2 time points in the model)

for num 1/3: gen tX = t == X if t ~= .

* treatment-time interaction

* (assuming time effect linear, with zero effect at time 1)

for num 1/3: gen gX_t = gX*(t-1)

* Set up the -xt- structure

iis id

tis t

* Produce some estimates & graphs

version 7: qnorm y

tab y

* Clearly not Normal, so we may need to use something more subtle, such as

* interval regression or ordered logistic regression.

* However, that is not the immediate problem.

xtgraph y , group(g) xlab( 1 2 3) ylab offset(.05) list

* Pretty the graph up using version 8 graphics

preserve

tempfile myfile

xtgraph y , group(g) list savdat("`myfile'")

/* Insert gr8 commands to taste */

restore

* Now the main analysis

* Constant effects of treatment

regress y x g2 g3 t if t > 1, cluster(id)

* Treatment effect increasing linearly with time

regress y x g2_t g3_t t if t > 1, cluster(id)

* Both models suggest that group 2 typically has lower scores.

* However the first model seems to fit the data better.

In practice, I would probably stop there & report the

constant effects model; however, xt models can also be fitted.

e.g.

xtgls y x g2 g3 t if t > 1, corr(ar1)

xtgee y x g2 g3 t if t > 1, cluster(id)

etc.

I would be interested in comments on which

approach is most appropriate for a data set such as this

=========================

Paul T Seed (Paul.Seed@kcl.ac.uk)

Division of Reproductive Health, Endocrinology and Development

Guy's Kings and St. Thomas' School of Medicine, King's College London,

St Thomas' Hospital,

Lambeth Palace Road,

London SE1 7EH

tel (+44) (0) 20 7188 3642

fax (+44) (0) 20 7620 1227

Thurs only: (+44) (0) 20 7848 4208

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