Salīdzināt metodes

	Matched Case Report	N-of-1 pētījums
Nozare	Epidemioloģija	Klīniskie pētījumi
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	Late 20th century (widely used from 1990s onward in pharmacovigilance and rare-disease literature)	1990s-2010s
Autors	Evolved from standard clinical case reporting practice; no single originator	Kravitz, Duan, Vohra, and single-patient methodology pioneers
Tips	Observational descriptive design with comparator	Research Design
Pirmavots	Gagnier, J. J., Kienle, G., Altman, D. G., Moher, D., Sox, H., & Riley, D. (2013). The CARE guidelines: consensus-based clinical case reporting guideline development. Journal of Medical Case Reports, 7, 223.	Gabler, N. B., Duan, N., Vohra, S., & Kravitz, R. L. (2011). N-of-1 trials in the medical literature: a systematic review. Medical Care, 49(8), 761–768.
Citi nosaukumi	matched case write-up, case report with matched comparator, matched single-case report, comparator-matched case report	single-patient RCT, n=1 trial, individual RCT, crossover n-of-1
Saistītās	5	3
Kopsavilkums	A matched case report is a structured clinical case write-up in which the index patient is compared against one or more systematically selected matched comparators — typically patients with similar demographics, comorbidities, or clinical settings who did not experience the same unusual outcome. The matched comparator contextualises the index case, strengthening causal inference beyond what a conventional single case report can support, and is used particularly in pharmacovigilance, rare-disease documentation, and novel-intervention reporting.	An N-of-1 trial is a single-patient randomized controlled trial in which a patient alternates between treatment A and treatment B (or active drug and placebo) in repeated, randomized cross-over periods. Developed systematically in the 1990s–2010s by Kravitz, Duan, and Vohra, N-of-1 trials enable personalized medicine by determining which treatment works best for that specific individual, avoiding the assumption that population-average effects apply to all patients. They are ideal for chronic conditions with variable outcomes and heterogeneous treatment response.