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Screensize: 640 x 480 800 x 600 1024 x 768 1280 x 1024 UserName: Password: Anti-Spam Code: Format Mode: Basic  Help  Prompt  Format: Font Andale Mono Arial Arial Black Book Antiqua Century Gothic Comic Sans MS Courier New Georgia Impact Lucida Console Script MT Bold Stencil Tahoma Times New Roman Trebuchet MS Verdana   Size 1 2 3 4 5 6   Color Black Red Yellow Pink Green Orange Purple Blue Beige Brown Teal Navy Maroon LimeGreen Message: * HTML is OFF * Forum Code is ON Smilies [quote][i]Originally posted by hauli[/i] [br]Origin Version (Select Help-->About Origin): 7.5 Operating System: windows I`m searching for a method to test for a treatment effect in a series of matched experiments. the whole assay is performed as follows: I got one control group and one treated. The data is fit to a dose response model (with all parameters fixed except the LOGx0, as this makes biologically sense); the whole assay is repeated twice. So overall I got 6 values for the LOGx0 (i.e. 3 for control and 3 for treatment) and calculated the treatment effect as the difference between the LOGx0 of the control and its corresponding treatment. Unfortunately the CI for the average treatment effect calculated like this is very large and often contains the value 0. What I want is to estimate the average treatment effect automatically and based on all of my data points (not only on the 6 values for the LOGx0)and thus based on a lot of degrees of freedom. As I think this can be done by using global fitting models. So overall I need a model that fits all the data and provides automatically one single best fit value for the average treatment effect. This problem sounds difficult, but I hope it is not Thanks for any help and suggestion HAULI [/quote]   Check here to include your profile signature. Check here to subscribe to this topic.