In general, the statistical analyses based on raw data and the data using propensity score matching (PSM) will be primarily descriptive in nature. The statistics are as in the following:
* Continuous variables: N, mean, standard deviation, lower quartile Q1, median, upper quartile Q3, minimum, maximum;
* Categorical variables: frequencies and percentages of patients at each category.
Before the statistical analyses, the medical history and co-morbidities need to be coded using ICD10. At the same time, the data collected should be clearly understood and provide the following information but not limited to the number of subjects enrolled in each site, the list of variables, the number of observations of each variable and the missing proportion of the variable.
The factors involved into the logistic model for propensity score matching include but not limited to demographic, baseline characteristics, severity of COPD, etc. A factor research will be performed before the matching is performed aiming to include appropriate factors into the model. Matching ratio could be exact or approximate.
Programming software is SAS 9.3 (SAS: Statistical Analysis System) or higher in Windows system. R statistical software could be another tool for the exploration.
