I need to calculate the prevalence ratios to show the trend of prevalence of a drug group used by pregnant women over the years (2001-2018). I would like to use the year 2001 with age distribution of pregnant women in 2001 as reference for following years.
Please show me how to calculate the standardized Prevalence ratio (95%CI) with SPSS (I am not familiar with other software).
The outcome (dependent) variable Drug group use (y/n).
Other variables: age at delivery of pregnant women (continuous, but can be re-coded into age groups), years of birth (2001-2018), date of the prescription of the drug group.
I tried to use GEE (generalized estimating equation), but I do not know which model to use: poisson, negative binomial or binary logistic?
GEE: because there are women deliver several times during 2001-2018
Please help me.
Hao Tran With Poisson regression use predicted lambdas , you need to convert them to predicted probabilities to get at least 1, which would be: 1 - exp( - lambda).
Good luck.
Hao Tran With Poisson regression use predicted lambdas , you need to convert them to predicted probabilities to get at least 1, which would be: 1 - exp( - lambda).
Good luck.
Thank you, Pradeep Paraman. Please help me with further details.
Shall I include:
-In tab Response: Dependent variable: Drug group use
-In tab Predictors: Should I include "years of birth" (ranging from 2001-2018) as "offset variable" (without factors and covariates); or as "factors"; or as "covariate"?
-In tab Predictors: Should I include "age at delivery" (category group) as "factors"; or as "covariate"?
- In tab Model: what variable I should put as "main effect"?
- In tab Estimation and Statistics, What should I select to get the result?
- Where should I look at the output to have lamda? or shoud I do any further transformation/recalculation to have 1 - exp( - lambda)? I am counting , and I want to have the prevalence and confidence interval. Do I need any further calculation to have the result that I want? IF yes, please show me further. Thank you very much.
Pradeep Paraman
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