{"id":"W2596575759","doi":"10.1287/msom.2017.0616","title":"Clinical Trials for New Drug Development: Optimal Investment and Application","year":2017,"lang":"en","type":"article","venue":"Manufacturing & Service Operations Management","topic":"Pharmaceutical Economics and Policy","field":"Economics, Econometrics and Finance","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Interim; Clinical trial; Revenue; Interim analysis; Drug development; Investment (military); Value (mathematics); Computer science; Net present value; Actuarial science; Medicine; Test (biology); Operations management; Operations research; Drug; Business; Economics; Finance; Microeconomics; Mathematics; Production (economics); Pharmacology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01282198,0.001590163,0.002587053,0.0021317,0.001172797,0.007672643,0.001695228,0.004829412,0.01724157],"category_scores_gemma":[0.05830318,0.001907197,0.0009789454,0.003414578,0.003666674,0.007758818,0.002485221,0.004775055,0.001932308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008657337,"about_ca_system_score_gemma":0.00921475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003579039,"about_ca_topic_score_gemma":0.003342613,"domain_scores_codex":[0.9907311,0.006437243,0.0002672594,0.0008262011,0.001185983,0.0005521913],"domain_scores_gemma":[0.9644324,0.02944812,0.00290236,0.001085539,0.001166416,0.0009652189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001827214,0.0001534274,0.00128175,0.000433841,0.00007201155,0.0001111384,0.0001011821,0.1802405,0.0002499817,0.7206982,0.01505847,0.0814168],"study_design_scores_gemma":[0.0001349314,0.0001276573,0.0006777557,0.0003071326,0.00004458835,0.00008938772,0.00008362061,0.1979477,0.0002158063,0.784843,0.01549112,0.00003743667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03348613,0.02343897,0.7278811,0.06143787,0.0006599199,0.001778522,0.001880663,0.0007364838,0.1487005],"genre_scores_gemma":[0.7654521,0.02294914,0.1893765,0.002182666,0.001000826,0.001664726,0.0007600831,0.0001896869,0.01642422],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01724157,"threshold_uncertainty_score":0.06780994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1940947930245319,"score_gpt":0.3905905963536664,"score_spread":0.1964958033291345,"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."}}