Statistical planning

Flows for this purpose

• Endpoint hypothesis and method

  Endpoint and estimand → objective → hypotheses or estimation target → method → assumptions

• Sample size and power

  Endpoint and method → effect and variability assumptions → scenarios → selected design → rationale

• Multiple-endpoint testing

  Endpoint family → success criteria → testing strategy → decision rules

• Interim monitoring and stopping

  Information schedule → efficacy, futility or safety review → continue, modify or stop decision

• Confounding adjustment strategy

  Causal question → covariates → adjustment method → diagnostics → analysis strategy

• Missing-data strategy

  Expected missingness → reasons and assumptions → handling method → sensitivity plan

• Subgroup and effect-modification plan

  Scientific rationale → subgroup definitions → interaction approach → precision and interpretation plan

• Adaptive design decision plan

  Planned looks → adaptation criteria → permissible changes → subsequent analysis

• Estimation and sensitivity plan

  Specify estimates and robustness checks

• Analysis and missingness plan

  Specify estimates and attrition handling

• Confounding adjustment plan

  Specify covariates adjustment and diagnostics

• Estimation and sensitivity plan

  Specify effects and bias analyses

• Sampling and analysis plan

  Account for matching and sampling

• Bias and sensitivity plan

  Assess selection and measurement errors

• Estimation and sensitivity plan

  Account for sampling and uncertainty

• Accuracy and precision plan

  Specify paired measures and uncertainty

• Bias and applicability assessment

  Assess verification and spectrum effects

• Sample size and testing

  Specify scenarios hypotheses and multiplicity

• Statistical analysis plan

  Specify estimation missingness and sensitivity

• Sample size and decision rules

  Specify precision thresholds and stopping

• Data and analysis plan

  Define populations methods and sensitivity

• Sample size and precision

  Justify information needed for decisions

• Feasibility analysis and refinement

  Summarize findings and required changes

• Emulation adjustment and estimation

  Specify identification and estimation methods

• Emulation sensitivity and feasibility

  Evaluate deviations and identifying assumptions

• Matched risk-set analysis and sensitivity

  Respect sampled sets and assumptions

• Within-person incidence analysis

  Specify incidence contrasts and adjustments

• Self-controlled assumptions and sensitivity

  Assess event-dependent exposure and observation

• Interrupted time-series impact estimation

  Model level slope and correlation

• Time-series diagnostics and sensitivity

  Assess seasonality shocks and specifications

• Difference-in-differences identification and estimation

  Specify comparison assumptions and estimands

• Comparative change diagnostics and sensitivity

  Assess trends anticipation and spillovers

• Prediction model development specification

  Specify fitting tuning and complexity

• Prediction internal validation

  Estimate optimism using appropriate resampling

• Prediction performance and usefulness

  Assess calibration discrimination and utility

• External performance and precision

  Estimate validation metrics and uncertainty

• Transportability and model updating decisions

  Separate evaluation from model modification

• Nonrandomized sample size and confounding

  Plan precision adjustment and overlap

• Nonrandomized intervention data and analysis

  Specify populations estimation and sensitivity

• Crossover sample size and paired precision

  Model within-participant variation and precision

• Crossover analysis specification

  Specify period sequence and sensitivity

• Factorial sample size and multiplicity

  Plan effect precision and testing

• Factorial analysis specification

  Specify interactions contrasts and sensitivity

• Cluster sample size and testing

  Specify correlation sizes and power

• Clustered trial analysis specification

  Specify multilevel estimation and sensitivity

• Stepped-wedge design scenarios and power

  Account for time and clustering

• Stepped-wedge time-adjusted analysis

  Separate intervention effects from trends

• Dose escalation and stopping decisions

  Define permitted decisions and safeguards

• Dose-design simulations and assumptions

  Evaluate operating characteristics across scenarios

• Safety review and dose selection

  Integrate evidence into dose recommendations

• Adaptation and interim decisions

  Specify triggers actions and boundaries

• Adaptive design simulations and sample size

  Evaluate power error and feasibility

• Adaptive final analysis specification

  Account for adaptations in inference

• Platform interim decisions and governance

  Prespecify adaptations and review responsibilities

• Platform simulations and sample-size scenarios

  Evaluate changing platform operating characteristics

• Platform comparison analysis specification

  Specify comparisons across calendar epochs