Version Compatibility

Reference examples tested with: R mmrm 0.3+ (Roche/openpharma), R rbmi 1.5+ (Roche/Bayer via insightsengineering), R mice 3.16+, R mitools 2.4+, Python sklearn 1.4+, statsmodels 0.14+.

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name
• Python: pip show <package> then help(module.function)

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Missing Data Sensitivity for Confirmatory Trials

"Handle missing data in a confirmatory clinical trial" -> Pre-specify the missing-data assumption per ICH E9(R1); execute the primary analysis under the chosen assumption (typically MAR via MMRM or MI); run clinically-articulable MNAR sensitivity analyses (reference-based MI per Carpenter-Roger 2013); report the tipping delta that would overturn the conclusion (Permutt 2016).

The Foundation -- NRC 2010 and ICH E9(R1)

The U.S. National Research Council Panel ("The Prevention and Treatment of Missing Data in Clinical Trials," 2010; chaired by Roderick Little; Little, D'Agostino, Cohen et al 2012 NEJM 367:1355): 18 recommendations grouped as prevention (Recs 1-7), analysis (Recs 8-14), sensitivity (Recs 15-18).

Key recommendations: