{"id":"W3006196254","doi":"10.1093/ije/dyz277","title":"Power calculations for cluster randomized trials (CRTs) with right-truncated Poisson-distributed outcomes: a motivating example from a malaria vector control trial","year":2019,"lang":"en","type":"article","venue":"International Journal of Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Medical Research Council; Department for International Development; Bill and Melinda Gates Foundation","keywords":"Truncation (statistics); CRTS; Sample size determination; Statistics; Cluster randomised controlled trial; Poisson distribution; Population; Type I and type II errors; Mathematics; Medicine; Randomized controlled trial; Computer science; Surgery; Environmental health","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01595969,0.0002649213,0.003190415,0.000183668,0.00005256413,0.00004161362,0.0004181702,0.0002156668,0.001636544],"category_scores_gemma":[0.2193142,0.0001528843,0.0007400444,0.00008005906,0.0001294944,0.0001437757,0.00003737483,0.0003403887,0.000008641748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001280939,"about_ca_system_score_gemma":0.0001630186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002251039,"about_ca_topic_score_gemma":0.00001337219,"domain_scores_codex":[0.991286,0.004559187,0.003191956,0.0002980053,0.0003414616,0.0003234391],"domain_scores_gemma":[0.7727101,0.2229698,0.002913275,0.0002021585,0.001077444,0.0001271038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.4366238,0.0002525057,0.003352164,0.00001223389,0.003258309,0.00001853902,0.0001571556,0.000101869,0.0002574973,0.5530846,0.002475389,0.000405928],"study_design_scores_gemma":[0.4475891,0.0004029956,0.002388597,0.0001272231,0.0003680908,0.0000354476,0.00003130261,0.006191952,0.00002882,0.5421653,0.0005101872,0.0001610484],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08115298,0.00002878942,0.9089527,0.005726101,0.001778805,0.001597227,0.0006555885,0.00001859132,0.00008925307],"genre_scores_gemma":[0.5135943,0.000002784461,0.4847377,0.0009995278,0.0004425753,0.00007785641,0.00008152576,0.00002308527,0.00004063307],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4324413,"threshold_uncertainty_score":0.9992761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1383667190110728,"score_gpt":0.4419526530612022,"score_spread":0.3035859340501295,"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."}}