השוואת שיטות

	סדרת מקרים אדפטיבית – מחקר סדרת מקרים אדפטיבית	סדרת מקרים פרוספקטיבית
תחום	אפידמיולוגיה	אפידמיולוגיה
משפחה	Process / pipeline	Process / pipeline
שנת המקור	Late 20th–early 21st century	Late 19th century onward; formalized in modern clinical epidemiology by the 1970s–1980s
הוגה השיטה	Evolved from classical case series methodology combined with adaptive design principles (Chow & Chang, 2008; FDA adaptive design guidance)	Evolved from clinical case reporting traditions in 19th–20th century medicine
סוג	Observational study with adaptive monitoring	Observational study design
מקור מכונן	Chow, S.-C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman & Hall/CRC. ISBN: 978-1584887775	Meinert, C. L. (1996). Clinical Trials: Design, Conduct, and Analysis. Oxford University Press. ISBN: 978-0195035681
כינויים	adaptive case-series design, sequential adaptive case series, adaptive observational case series	prospective case series study, forward-looking case series, prospective uncontrolled study, prospective observational case series
קשורות	5	5
תקציר	An adaptive case series is an observational study design that documents a consecutive group of patients with a shared condition or exposure while incorporating pre-specified rules for modifying data collection, monitoring, or stopping criteria as accumulating evidence warrants. It combines the descriptive richness of traditional case series with the prospective flexibility of adaptive design principles, enabling structured mid-course adjustments without compromising the integrity of the recorded clinical observations.	A prospective case series is an observational study design in which a group of patients with a particular condition, exposure, or intervention is identified in advance and followed forward in time according to a pre-specified protocol. Data on outcomes, adverse events, and clinical course are collected as they occur, yielding higher data quality and temporal clarity than retrospective designs. No control group is included, so causal inference is limited, but the design is valuable for characterizing natural disease history, early safety signals, and feasibility of new interventions.