Practical guides · Page 7

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Plan What a Prediction Model Does When an Input Is Missing

Validate the actual missing-input workflow instead of assuming complete predictors at deployment.

Treat Recalibration as a Model Update That Needs Evaluation

Preserve the original validation result while testing a justified update to predicted probabilities.

Scale Predictors for Numerical Stability Without Losing Units

Record transformations and back-transform coefficients so numerical convenience does not change the reported effect.

Handle Preprints and Later Journal Versions as Linked Evidence

Separate eligibility, version changes, and duplicate participant data when a study appears before and after journal publication.

Choose the Propensity-Score Target Before Choosing Weights

Connect ATE, ATT and overlap weighting to the population in the research question.

Keep patient, proxy and observer responses distinct

Record who supplied an outcome and avoid silently pooling different perspectives as one measure.

Check questionnaire permissions before implementation

Match the exact instrument, language and administration mode to its current terms of use.

Manage Random Streams in Parallel Simulation Studies

Assign reproducible streams to replicates so parallel workers do not accidentally repeat the same simulated data.

Combine Range Checks with Cross-Field Consistency Rules

Use explicit logical tests to find contradictory research records without automatically deleting unusual values.

Detect Redundant Columns Before Interpreting Regression Coefficients

Check exact dependencies in the fitted design rather than assuming a returned coefficient table identifies every parameter.

Interpret a Ratio of Arithmetic Means Without Calling It a Geometric Ratio

Check the summary definition and meaningful zero before reporting a relative continuous-outcome comparison.

Align questionnaire recall periods with study visits

Check that the reporting window matches the scientific question and remains consistent across assessments.