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| Uji Klinis Fase II yang Dicocokkan× | Studi Kohort yang Dicocokkan× | |
|---|---|---|
| Bidang | Epidemiologi | Epidemiologi |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 1960s–1980s (formalized with Simon optimal designs, 1989) | Mid-20th century; propensity-score variant 1983 |
| Pencetus≠ | Gehan (1961) for Phase II designs; matching frameworks adapted from case-control methodology | Established practice; propensity-score matching formalized by Rosenbaum & Rubin (1983) |
| Tipe≠ | Controlled clinical trial design | Observational analytic study design |
| Sumber perintis≠ | Gehan, E. A. (1961). The determination of the number of patients required in a preliminary and a follow-up trial of a new chemotherapeutic agent. Journal of Chronic Diseases, 13(4), 346–353. DOI ↗ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| Alias | matched Phase II trial, historically matched Phase II study, propensity-matched Phase II trial, externally controlled Phase II trial | matched follow-up study, paired cohort study, propensity-matched cohort, matched prospective study |
| Terkait | 5 | 5 |
| Ringkasan≠ | A matched Phase II clinical trial is a single-arm or small-controlled early-efficacy study in which treated patients are paired with matched controls — drawn from historical databases, registries, or concurrent external cohorts — on key prognostic variables such as age, disease stage, and performance status. This design allows preliminary efficacy assessment without a concurrent randomized arm, trading randomization for feasibility while partially controlling for confounding through the matching process. | A matched cohort study is an observational design in which each exposed participant is paired with one or more unexposed counterparts who share key characteristics — such as age, sex, or comorbidity status — before both groups are followed forward in time to compare incident outcomes. Matching controls for measured confounders at the design stage, reducing bias that would otherwise require statistical adjustment alone. |
| ScholarGateSet data ↗ |
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