Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Utafiti wa Kesi-udhibiti× | Utafiti wa Ki-epidemiolojia wa Msalaba× | |
|---|---|---|
| Nyanja | Epidemiolojia | Epidemiolojia |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1950s (formal methodology); precursors in the 1920s | 1960s (formal codification); widely practiced since mid-20th century |
| Mwanzilishi≠ | Janet Lane-Claypon (early precursors, 1926); formalized by Brian MacMahon and Jerome Cornfield in the 1950s–1960s | Classical epidemiology tradition; systematized by Brian MacMahon and Thomas Pugh (1960s) |
| Aina≠ | Observational analytic study design | Observational, descriptive/analytic epidemiological design |
| Chanzo asilia≠ | Schlesselman, J.J. (1982). Case-Control Studies: Design, Conduct, Analysis. Oxford University Press. ISBN: 978-0195027860 | Kelsey, J. L., Whittemore, A. S., Evans, A. S., & Thompson, W. D. (1996). Methods in Observational Epidemiology (2nd ed.). Oxford University Press. ISBN: 978-0195080407 |
| Majina mbadala | case-referent study, case-control design, retrospective case-control, case-control analysis | prevalence study, cross-sectional survey, transversal study, cross-sectional design |
| Zinazohusiana | 6 | 6 |
| Muhtasari≠ | A case-control study is a retrospective observational design in which individuals who have developed a disease or outcome of interest (cases) are compared with individuals who have not (controls) to determine whether prior exposure to a putative risk factor differs between the two groups. The primary measure of association is the odds ratio, which approximates the relative risk when the outcome is rare. Case-control studies are especially efficient for investigating rare diseases and generating etiological hypotheses. | A cross-sectional epidemiological study measures the exposure(s) and outcome(s) of interest simultaneously in a defined population at a single point in time (or over a short period). Because there is no follow-up, it is the most efficient observational design for estimating disease prevalence and for generating hypotheses about associations between risk factors and health outcomes. |
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