Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchanganuzi wa Unusurika wenye Marekebisho ya Hatari× | Ulinganishaji wa Alama ya Mwelekeo× | |
|---|---|---|
| Nyanja≠ | Epidemiolojia | Takwimu za Utafiti |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1972 (Cox regression); broader covariate-adjusted survival methods developed 1970s–1990s | 1983 |
| Mwanzilishi≠ | D. R. Cox (regression framework); extensions via Kaplan & Meier, Breslow, and others | Paul Rosenbaum and Donald Rubin |
| Aina≠ | Observational and experimental analytical method | Method |
| Chanzo asilia≠ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187–220. link ↗ | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. DOI ↗ |
| Majina mbadala≠ | covariate-adjusted survival analysis, adjusted time-to-event analysis, risk-stratified survival analysis, adjusted Kaplan-Meier / Cox analysis | PSM, propensity score weighting, covariate balance |
| Zinazohusiana≠ | 5 | 3 |
| Muhtasari≠ | Risk-adjusted survival analysis estimates the time to an event of interest — such as death, relapse, or hospital readmission — while simultaneously accounting for baseline differences in patient characteristics (covariates). By incorporating confounders such as age, comorbidities, or disease severity, it produces hazard ratios, survival curves, and median survival estimates that are attributable to the factor of interest rather than to pre-existing risk differences between groups. | Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias. |
| ScholarGateSeti ya data ↗ |
|
|