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⏳ '''Immortal time bias''' is a methodological error that occurs in insurance and actuarial analyses when a period of follow-up during which the outcome of interest cannot occur is misclassified or improperly attributed, artificially inflating the apparent benefit of a treatment, program, or exposure. The term originates from clinical epidemiology, but the problem appears regularly in insurance research — for example, when evaluating [[Definition:Claims management | claims management]] interventions, [[Definition:Wellness program | wellness programs]], or [[Definition:Fraud detection | fraud detection]] tools where there is a gap between policy inception and the start of the intervention during which no outcome (claim, lapse, or loss event) can logically be attributed to the treatment.

🔬 Consider a [[Definition:Health insurance | health insurer]] assessing whether members who enroll in a disease management program have lower hospitalization costs. If the analysis counts the time between policy effective date and program enrollment as "treated" time, it introduces immortal time: during that window, the member had to survive (remain enrolled and event-free) long enough to join the program. Members who were hospitalized or who lapsed before enrollment never had the chance to be classified as participants. The result is a comparison that systematically favors the treatment group, not because the program works, but because of the study's flawed time accounting. The same issue arises in [[Definition:Motor insurance | motor insurance]] when measuring the impact of a [[Definition:Telematics | telematics]] device that is installed weeks after policy inception, or in [[Definition:Workers' compensation insurance | workers' compensation]] studies evaluating return-to-work programs that begin only after a qualifying period of disability. Correcting for this bias typically requires time-dependent analysis methods — such as Cox regression with time-varying covariates or landmark analysis — that properly align exposure windows.

🎯 Overlooking immortal time bias can lead insurers to invest heavily in interventions whose apparent effectiveness is a statistical illusion. [[Definition:Actuary | Actuarial]] teams building [[Definition:Experience rating | experience rating]] models or evaluating [[Definition:Loss ratio | loss ratio]] impacts must structure their study designs to avoid this trap, particularly as the industry increasingly relies on observational data from [[Definition:Insurtech | insurtech]] platforms and digital health ecosystems. [[Definition:Reinsurer | Reinsurers]] reviewing cedants' claims for program-driven loss improvement should be alert to this bias when assessing [[Definition:Treaty reinsurance | treaty]] performance. Across markets — from sophisticated analytics teams at large European [[Definition:Insurance carrier | carriers]] to emerging data science functions in Asian and Latin American markets — awareness of immortal time bias is a marker of analytical maturity that ultimately protects [[Definition:Reserving | reserve]] adequacy and [[Definition:Pricing | pricing]] integrity.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Selection bias]]
* [[Definition:Healthy user bias]]
* [[Definition:Causal inference]]
* [[Definition:Experience rating]]
* [[Definition:Predictive model]]
* [[Definition:Internal validity]]
{{Div col end}}

Definition:Immortal time bias - Revision history

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