Force of mortality
In actuarial science and demography, force of mortality, also known as death intensity,[1][2] is a function, usually written , that gives the instantaneous rate at which deaths occur at age x, conditional on survival to age x.[3] In survival analysis it corresponds to the hazard function, and in reliability theory it corresponds to the failure rate.[4][5] It has units of inverse time, and integrating it over an interval gives the survival probability over that interval.[4]
Definition
Let be a non-negative random variable representing an individual's age at death (or lifetime). Write for its cumulative distribution function and for its survival function.[4]
The force of mortality at age , written , is defined as the instantaneous conditional rate of death at age . Formally, it is the limit of the conditional probability of dying in a short interval after , divided by the interval length[3]:
When is continuous with probability density function , the force of mortality can be written in terms of and as[4]
Equivalently, where is differentiable, it is the negative derivative of the log-survival function[4]:
Interpretation and related quantities
The force of mortality is an instantaneous rate rather than a probability. For a short interval , the conditional probability of dying shortly after age is approximately , provided is small enough that the rate does not change much over the interval.[3]
In survival analysis, is the hazard function.[4] In reliability theory, the same mathematical object is commonly called the failure rate.[5]
The cumulative force of mortality (also called the cumulative hazard) is the integral of the force over age. Writing then the survival function can be expressed as[4]
These identities imply the differential relationship and, for a continuous lifetime distribution, the density can be written as[4]
Survival probabilities and life tables
In actuarial notation, the probability that a life aged survives for a further years is written . In terms of the lifetime random variable , it is[3]
Using the force of mortality, this conditional survival probability can be expressed as an exponential of the integrated force[3][4]:
Life tables often tabulate survival and death probabilities at integer ages. In that setting, the one-year survival probability is and the one-year death probability is .[3] The force of mortality provides a continuous-age description that can be used to relate probabilities over different intervals through the integral relationship above.[3]
Examples of mortality models
Several parametric models are used to describe how the force of mortality varies with age. A constant force of mortality, for , corresponds to an exponential distribution for and gives a memoryless survival pattern.[4]
In actuarial work, the Gompertz–Makeham law of mortality is often written as the sum of an age-independent component and an exponentially increasing component, for example with , , and .[3] The Gompertz model is the special case , giving:[3]
A common model in survival analysis and reliability uses a Weibull hazard, which has the form for shape and scale . This family includes decreasing, constant, and increasing forces of mortality depending on the value of .[4][5]
See also
- Failure rate
- Hazard function
- Actuarial present value
- Actuarial science
- Reliability theory
- Life expectancy
References
- ↑ Andersen, Per Kragh; Borgan, Ørnulf; Hjort, Nils Lid; Arjas, Elja; Stene, Jon; Aalen, Odd (1985). "Counting Process Models for Life History Data: A Review [with Discussion and Reply"]. Scandinavian Journal of Statistics 12 (2): 97–158. ISSN 0303-6898. https://www.jstor.org/stable/4615980.
- ↑ Ioannidis, John P. A. (2013). "Expressing Death Risk as Condensed Life Experience and Death Intensity" (in en). Medical Decision Making 33 (6): 853–859. doi:10.1177/0272989X13484389. ISSN 0272-989X. https://journals.sagepub.com/doi/10.1177/0272989X13484389.
- ↑ 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 Dickson, David C. M.; Hardy, Mary R.; Waters, Howard R. (2013). Actuarial Mathematics for Life Contingent Risks (2nd ed.). Cambridge University Press. ISBN 9781107044074.
- ↑ 4.00 4.01 4.02 4.03 4.04 4.05 4.06 4.07 4.08 4.09 4.10 Klein, John P.; Moeschberger, Melvin L. (2003). Survival Analysis: Techniques for Censored and Truncated Data (2nd ed.). Springer. ISBN 9780387953991.
- ↑ 5.0 5.1 5.2 Rausand, Marvin; Høyland, Arnljot (2004). System Reliability Theory: Models, Statistical Methods, and Applications (2nd ed.). Wiley-Interscience. ISBN 9780471471332.
