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Review your selected methods side by side; rows that differ are highlighted.
| Peace Duration Analysis× | Survival Analysis× | |
|---|---|---|
| Field≠ | International Relations | Research Statistics |
| Family≠ | Survival analysis | Process / pipeline |
| Year of origin≠ | 2003 | 1958 |
| Originator≠ | Conflict-duration literature (e.g., Caroline Hartzell & Matthew Hoddie on post-civil-war peace) | Edward L. Kaplan and Paul Meier |
| Type≠ | Time-to-event (survival) analysis of peace spells | Method |
| Seminal source≠ | Hartzell, C., & Hoddie, M. (2003). Institutionalizing peace: Power sharing and post-civil war conflict management. American Journal of Political Science, 47(2), 318–332. DOI ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Aliases≠ | Duration of Peace Analysis, Post-Conflict Peace Survival Analysis, Peace Spell Analysis, Time-to-Conflict-Recurrence Analysis | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Related | 3 | 3 |
| Summary≠ | Peace duration analysis applies survival (time-to-event) methods to study how long peace lasts after a conflict ends and what makes it endure or collapse. The unit is the post-conflict peace spell, observed from a settlement or cessation until conflict recurs or the observation is censored. Modeling the hazard that peace fails as a function of how the conflict ended and the structural conditions — as in Hartzell and Hoddie's (2003) study of power-sharing after civil war — reveals which arrangements, such as institutionalized power sharing or peacekeeping, lengthen the survival of peace. | Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters. |
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