So sánh phương pháp

	Nghiên cứu dịch tễ học cắt ngang thích ứng	Lấy mẫu cụm thích nghi
Lĩnh vực	Dịch tễ học	Phương pháp luận khảo sát
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1990s–2000s (formalization of adaptive elements in observational surveys)	1990
Người khởi xướng	Conceptual synthesis of adaptive design methods (Wald, 1947; Bauer & Kohne, 1994) with classical cross-sectional epidemiology (MacMahon & Pugh, 1960s)	Steven Thompson
Loại	Observational epidemiological study design	Probability-based adaptive design
Công trình gốc	Kelsey, J. L., Whittemore, A. S., Evans, A. S., & Thompson, W. D. (1996). Methods in Observational Epidemiology (2nd ed.). Oxford University Press. ISBN: 978-0195083439	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.
Tên gọi khác	adaptive cross-sectional survey, adaptive prevalence study, adaptive epidemiological survey design, adaptive population cross-section	Adaptive Cluster Sampling, Sequential Adaptive Sampling, Network Sampling, Adaptif Küme Örneklemesi
Liên quan	2	3
Tóm tắt	An adaptive cross-sectional epidemiological study combines the core logic of a cross-sectional survey — measuring exposures and outcomes simultaneously in a defined population at one point in time — with pre-specified adaptive rules that allow modifications to sampling strategy, sample size, or subgroup allocation based on accumulating interim data. The approach preserves the efficiency and speed of a standard cross-sectional design while improving precision for rare exposures or heterogeneous populations by redirecting sampling resources in real time.	Adaptive Cluster Sampling (ACS) is a probability-based survey design introduced by Steven K. Thompson in 1990 for estimating the abundance or total of rare, clustered populations. Starting from an initial random sample, the design adaptively adds neighboring units whenever a sampled unit satisfies a predefined condition—such as exceeding a count threshold—thereby concentrating sampling effort exactly where the population of interest occurs. It is most appropriate for ecologists, epidemiologists, and social scientists studying geographically or socially clustered rare phenomena.