Hello Vijay,
Is the smoking variable your response variable i.e Do you have an ordinal categorical outcome? or is smoking an explanatory variable for some other dependent variable?
If smoking were the outcome, you could use proportional odds logistic regression with ologit for each study and then perform random-effects meta-analysis of the study-specific odds-ratios.
Alternatively, oglm (written by Richard Williams) will allow you to account for variability in the residual variance i.e. fit a non-proportional odds logistic regression. In that case you will have to perform bivariate random-effects meta-analysis of the study-specific location and scale parameters.
These two-stage methods allow studies to have different set and number of categories.
Ben
>>> "Jayaprakash, Vijay" <[email protected]> 12/21/2007 3:52 PM >>>
Hi Stata users,
I'm trying to do a meta analysis of data from 10 different studies. The smoking variable in each study is stratified into categories (Category 1:1-10 cigarettes, Category 2: 10-20 cigs, Category3: 20-30 cigs etc.). I calculated the OR for each category (of smoking) by study. I then did a meta analysis and calculated the OR for each category by random effects model using the meta command:
meta or1 lcl1 ucl1, ci eform
meta or2 lcl2 ucl2, ci eform
I found that metap command can provide the p-value for each category (from p-values of each study for that specific category). Is there a way to get the p for trend BETWEEN the categories from the random effects estimate? I would like to report from the meta-analysis results if there is a dose-response relationship of smoking to the disease and if so what the p for trend is.
I will very much appreciate your help.
Thanks
Vijay
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