{"id":"W3092939691","doi":"10.1101/2020.10.12.20211706","title":"Evaluation of statistical methods used in the analysis of interrupted time series studies: a simulation study","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Monash University","keywords":"Autocorrelation; Statistics; Series (stratigraphy); Ordinary least squares; Mathematics; Restricted maximum likelihood; Econometrics; Population; Autocorrelation technique; Maximum likelihood; Demography; Biology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1519303,0.001134733,0.001871441,0.002140584,0.0007447891,0.002042645,0.002420832,0.001981115,0.004089528],"category_scores_gemma":[0.3285505,0.0006477112,0.002979724,0.002881914,0.001115709,0.001703788,0.001737627,0.002649049,0.0003158459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002229176,"about_ca_system_score_gemma":0.003076887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005308366,"about_ca_topic_score_gemma":0.002664819,"domain_scores_codex":[0.8743213,0.1181014,0.002534713,0.001635005,0.0028323,0.0005752849],"domain_scores_gemma":[0.3294371,0.6336636,0.01426043,0.009476776,0.01205964,0.00110238],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005603382,0.001276607,0.06523799,0.003383165,0.004693923,0.0006442132,0.0009432592,0.7670963,0.001176915,0.04850477,0.004547019,0.09689251],"study_design_scores_gemma":[0.0008317632,0.002247016,0.005465269,0.0009385578,0.0008555749,0.0001958715,0.0002452701,0.9710301,0.001116077,0.01412133,0.002867065,0.00008620998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2769063,0.004340509,0.705137,0.002066699,0.0003905946,0.003848591,0.001826459,0.0006749417,0.004808912],"genre_scores_gemma":[0.7108775,0.001575532,0.2812966,0.0003445769,0.00008741214,0.004449794,0.0007441435,0.0001214666,0.0005029982],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8480697,"threshold_uncertainty_score":0.803494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5710198001624486,"score_gpt":0.6137507411071274,"score_spread":0.04273094094467877,"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."}}