Dear all,
I am seeking guidance regarding the appropriate method for analyzing a binary outcome in an observational clinical research study. The primary outcome is a binary variable. One main independent variable needs to be tested for its effect on the outcome, while other variables serve as theoretical confounders.
I am considering several options: traditional logistic regression, propensity score matching using nearest-neighbor (potentially sacrificing sample size), or matching with inverse-probability treatment weights (retaining the entire sample size). Your valuable suggestions would be highly appreciated, and any alternative methods are also welcome.
Thank you sincerely for your input.
Best regards,
Suppadech Tunruttanakul
With a logistic regression you would estimate the conditional effects, with propensity score matching you estimate the marginal effects. I think it depends on what you are looking for.
From a research-like point-of-view I prefer the conditional effects, which allow a specific analysis of interactions (e.g. is males react differently to a treatment than females, and if this difference is also age-dependent etc.). From an economical point-of-view (e.g. what would be the expected net benefit of a treatment for a hospital or a health system), the marginal effects seem more interesting.
Thank you, Dr. Jochen Wilhelm. Your response is very clear and greatly appreciated.
A clinical platform of normalized timeseries by outcome with filters such as: demographics (age, gender etc...), treatments, biological mechanism and pathways. First chart to view the completed statistics, second chart to view new patient to compare with early biomarkers.
Partly successfully created in Excel Power Pivot.
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