| Statistical Methodology | R | SAS | Python | Comparison |
|---|---|---|---|---|
| Summary Statistics | ||||
| Rounding | R | SAS | Python | R vs SAS |
| Summary statistics | R | SAS | Python | R vs SAS |
| Skewness/Kurtosis | R | SAS | Python | R vs SAS |
| General Linear Models | ||||
| One-sample t-test | R | SAS | Python | R vs SAS |
| Paired t-test | R | SAS | Python | R vs SAS |
| Two-sample t-test | R | SAS | Python | R vs SAS |
| ANOVA | R | SAS | Python | R vs SAS |
| ANCOVA | R | SAS | Python | R vs SAS |
| MANOVA | R | SAS | Python | R vs SAS |
| Linear regression | R | SAS | Python | R vs SAS |
| Generalized Linear Models | ||||
| Logistic regression | R | SAS | Python | R vs SAS |
| Poisson/negative binomial regression | R | SAS | R vs SAS | |
| Non-Parametric Analysis | ||||
| Wilcoxon signed-rank test | R | SAS/ StatXact | R vs SAS | |
| Mann-Whitney U/Wilcoxon rank-sum test | R | SAS | R vs SAS | |
| Kolmogorov-Smirnov test | ||||
| Kruskal-Wallis test | R | SAS | Python | R vs SAS |
| Friedman test | R | SAS | R vs SAS | |
| Jonckheere-Terpstra test | R | SAS | R vs SAS | |
| Hodges-Lehman estimator | R | SAS | ||
| Categorical Data Analysis | ||||
| Binomial test | R | SAS | Python | |
| McNemar's test | R | SAS | R vs SAS | |
| Marginal homogeneity tests | R | |||
| Chi-square test/Fisher's exact test | R | SAS | Python | R vs SAS |
| Cochran-Mantel-Haenszel test | R | SAS | R vs SAS | |
| Confidence intervals for proportions | R | SAS | R vs SAS | |
| Repeated Measures | ||||
| Linear Mixed Model (MMRM) | R | SAS | R vs SAS | |
| Linear Mixed Model (degrees of freedom) | ||||
| Generalized Linear Mixed Model (GLMM) | ||||
| Generalized Estimating Equation (GEE) | ||||
| Bayesian MMRM | ||||
| Multiple Imputation - Continuous Data MAR | ||||
| MCMC imputation | ||||
| Linear regression imputation | R | SAS | ||
| Predictive mean matching | R | |||
| Multiple Imputation - Continuous Data MNAR | ||||
| Tipping point analysis (delta adjustment) | R | SAS | R vs SAS | |
| Reference-based imputation/joint modeling | R | SAS | R vs SAS | |
| Correlation | ||||
| Pearson/Spearman/Kendall's Rank | R | SAS | Python | R vs SAS |
| Survival Models | ||||
| Kaplan-Meier/log-rank test/Cox proportional hazards | R | SAS | R vs SAS | |
| Cause-specific hazards | R | SAS | R vs SAS | |
| Accelerated failure time | R | |||
| Weighted log-rank test | R | |||
| Recurrent events | R | SAS | R vs SAS | |
| Cumulative incidence functions | R | SAS | R vs SAS | |
| Tobit regression | R | SAS | R vs SAS | |
| Restricted Mean Survival Time (RMST) | SAS | |||
| Sample size/ Power calculations | ||||
| Intro to Sample Size | Summary | |||
| Superiority Single timepoint | R | SAS | ||
| Equivalence Single timepoint | R | SAS | ||
| Non-Inferiority Single timepoint | R | SAS | ||
| Average BioEquivalence | R | |||
| Cochran-Armitage Test For Trend | R | SAS/ StatXact | ||
| Group sequential designs | R | East | East vs R | |
| Causal inference/ Machine learning | ||||
| Intro to Machine Learning | Summary | |||
| Propensity Score Matching | R | R vs SAS | ||
| Propensity Score Weighting | R vs SAS | |||
| Clustering | R | |||
| Factor analysis | ||||
| Principal Components Analysis (PCA) | R | |||
| Canonical correlation | ||||
| Partial Least Squares (PLS) | ||||
| Lasso | ||||
| Ridge Regression | ||||
| xgboost | R | |||
| Other Methods | ||||
| Survey statistics | R | SAS | Python | R vs SAS vs Python |
CAMIS - A PHUSE DVOST Working Group
Introduction to CAMIS
Comparing Analysis Method Implementations in Software (CAMIS) is a cross-industry PHUSE DVOST Working Group, run in collaboration with members from PHUSE, PSI, ASA and IASCT. In addition to issue comments, which are hosted in the GitHub Repository, we meet monthly on the 2nd Monday of each month. If you would like to join us please contact us at workinggroups@phuse.global.
Motivation
The goal of this project is to demystify conflicting results in statistical analysis methods and results between primarily SAS, R, and Python programming languages by providing comparisons and comprehensive explanations of similarities and differences. Many discrepancies have been discovered in statistical analysis results between these and other programming languages. The differences in results are due to fundamental approaches implemented by each language, which are each correct in their own right. The fact that these differences exist is a challenge, especially related to sponsor companies when submitting to a regulatory agency.
In its Statistical Software Clarifying Statement, the US Food and Drug Administration (FDA) states that it “FDA does not require use of any specific software for statistical analyses” and that “the computer software used for data management and statistical analysis should be reliable.” Observing differences across languages can reduce the analyst’s confidence in reliability and, by understanding the source of any discrepancies, one can reinstate confidence in reliability. CAMIS seeks to explore and explain some of the differences and similarities in statical analysis methods between these languages to ease these concerns.
Repository
The repository below provides examples of statistical methodology in different software and languages, along with a comparison of the results obtained and description of any discrepancies.