Statistical Code Review

To review R code for statistical reproducibility, verify proper seeding, data path handling, and session information capture to ensure analyses rerun accurately from raw data to final output.

When auditing model specifications in R scripts, check the correctness of outcome types, degrees of freedom, confounder inclusion, and DLNM parameterization to ensure model choices are justified.

Validating data pipelines in R requires examining data manipulation steps to ensure correct handling of missing data, temporal alignment, and outlier treatment throughout the analysis workflow.

Performing a pull request review for statistical analysis code involves checking reproducibility, auditing model specifications, validating data pipelines, and verifying results integrity against best practices.

Yes, this statistical code review protocol supports DLNM models in R by specifically auditing DLNM parameterization alongside standard model specifications, outcome types, and confounder inclusion.

Results integrity in statistical code review ensures that analysis results are reported comprehensively, including confidence intervals and sensitivity analyses, to verify scientific soundness and robustness.