{"id":"W4404668027","doi":"10.6000/1929-6029.2024.13.24","title":"Sample Size and Statistical Power Calculation in Multivariable Analyses: Development and Implementation of \"SampleSizeMulti\" Packages in R","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multivariable calculus; Sample size determination; Sample (material); Power (physics); Statistical power; Statistics; Computer science; Econometrics; Mathematics; Engineering; Control engineering; Physics; Chemistry; Chromatography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07359876,0.002773345,0.002296805,0.004239692,0.0008547295,0.003534173,0.004113356,0.001641982,0.02284801],"category_scores_gemma":[0.3112322,0.002066497,0.003481918,0.004228942,0.002259967,0.0024848,0.00531219,0.005282978,0.01055615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001359657,"about_ca_system_score_gemma":0.006773645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001904895,"about_ca_topic_score_gemma":0.002056718,"domain_scores_codex":[0.9266286,0.05440661,0.005891144,0.004013873,0.008186216,0.0008735027],"domain_scores_gemma":[0.7681616,0.18661,0.01259364,0.01677735,0.01457457,0.001282838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001106101,0.0002807707,0.01077394,0.006510678,0.001815038,0.0005213395,0.002932779,0.03072675,0.003962329,0.09001481,0.1908837,0.6604717],"study_design_scores_gemma":[0.001885264,0.00106237,0.01648954,0.003868312,0.001272907,0.001405125,0.0006386095,0.2043242,0.02862843,0.2478389,0.4918477,0.0007385869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001427995,0.0002805821,0.9798133,0.0007395541,0.0002152776,0.001903555,0.002740153,0.01085052,0.00202899],"genre_scores_gemma":[0.01095267,0.000255826,0.9699863,0.0004374412,0.000103129,0.01116738,0.001208723,0.005213133,0.0006754139],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07359876,"threshold_uncertainty_score":0.3892321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1634124215117863,"score_gpt":0.5796363033632727,"score_spread":0.4162238818514863,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}