BGPT: Create Tables: Performance Table Schema For Longitudinal rsGSVA Variants

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Performance Table Schema — Longitudinal rsGSVA Variants

A rigorous, analysis-ready schema for reporting variant performance over time—without assuming any specific dataset, study design, or scoring definition.

Design goal (what the tables will support)

Variant-level traceability: exactly which longitudinal rsGSVA variant produced which performance numbers.
Time-aware metrics: evaluation at each timepoint and/or across time (slopes, stability, monotonicity, temporal generalization).
Reproducibility-ready audit columns: normalization, missingness handling, cohort filters, aggregation method.
Model- and rank-based performance: supports both effect-size accuracy and ranking quality (AUC/PR, correlations, calibration summaries) as separate columns.

Table 1 — Variant Registry (one row per longitudinal rsGSVA variant)

variant_id	rsGSVA_variant_name	gene_set_source_id	gene_set_collection	aggregation_axis	windowing_scheme_id	time_reference_rule	normalization_id	score_scaling	missingness_policy_id	replicate_aggregation_id	cell/sample_filter_id	batch_effect_handling_id	random_seed_id	runs_count	notes

Table 2 — Longitudinal Data Specification (evaluation context)

spec_id	cohort_id	study_design_type	outcome_type	outcome_definition_id	labeling_timepoint_rule	time_indexing_id	time_units	timepoint_list	num_subjects_total	num_subjects_complete_cases	num_subjects_by_timepoint	train_test_split_scheme_id	crossval_scheme_id	leakage_protection_rule	notes

Table 3 — Performance by Timepoint (one row per variant × timepoint × evaluation target)

variant_id	spec_id	evaluation_target_id	target_name	timepoint_id	timepoint_label	n_train	n_test	metric_class	metric_id	metric_name	metric_definition	stat_estimate	stat_ci_low	stat_ci_high	stat_se	stat_sd_across_runs	bootstrap_method_id	calibration_id	ranking_strategy_id	thresholding_rule_id	notes

Table 4 — Cross-time Performance & Temporal Robustness (one row per variant × evaluation target)

variant_id	spec_id	evaluation_target_id	target_name	temporal_metric_class	temporal_metric_id	temporal_metric_name	temporal_metric_definition	window_start_id	window_end_id	estimate	ci_low	ci_high	se	sd_across_runs	directionality_rule_id	stability_id	monotonicity_id	time_generalization_id	notes

Table 5 — Statistical Testing Output (comparative variant performance)

comparison_id	spec_id	evaluation_target_id	target_name	metric_id	timepoint_id_or_window	variant_a_id	variant_b_id	comparison_type	effect_direction	effect_estimate	p_value	adjustment_method_id	q_value	test_id	test_assumptions_check_id	notes

Table 6 — QC, Robustness, and Failure Modes (diagnostic audit)

variant_id	spec_id	qc_id	qc_name	qc_definition	time_scope	trigger_threshold_id	qc_flag	qc_value	qc_ci_low	qc_ci_high	n_effective	missingness_rate	outlier_rate	stability_violation_rate	leakage_risk_score	notes

Common blind spots this schema is designed to reduce

Time leakage: Table 2 includes a dedicated leakage_protection_rule field so evaluation does not accidentally reuse future information.
Inconsistent preprocessing: Table 1 forces normalization, missingness, filtering, and replicate aggregation to be explicitly declared.
Ill-defined metrics: Table 3 and 4 separate metric_id from a text-only metric_definition so reviewers can verify what was computed.
Over-interpreting single timepoints: Table 4 supports temporal generalization and stability checks instead of rank-only conclusions.

If you want, I can tailor the schema further

Tell me (a) your timepoint structure (discrete visits vs continuous time), (b) your outcome type (binary/continuous/time-to-event), and (c) what you mean by “performance” (prediction accuracy, ranking, effect recovery, or both). Then I can lock metric columns precisely to your target.

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Updated: April 18, 2026

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Discussion

BGPT Bias

I bias toward auditability and reproducibility (explicit IDs/definitions/QC) because longitudinal evaluation is easy to get subtly wrong.

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