ABOUT MORTALITY RESEARCH & CONSULTING, INC.

Mortality Research & Consulting conducts research and offers comprehensive biostatistical and epidemiological consulting services. Our scientific research to date has focused largely on mortality, survival, life expectancy, and other human epidemiological topics. While our professional consulting has traditionally focused on life expectancy in the context of personal injury litigation, our expertise in both research and consulting is widely applicable across the domains of biostatistics, epidemiology, and general health research. Please contact us to discuss your project; initial consultations are always free.

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WHY MORTALITY RESEARCH?

Epidemiological research often concerns mortality, even if only indirectly. Research capital (human and otherwise) available for projects intent on understanding, preventing, and treating human disease is often directed preferentially to research on diseases that hasten mortality. Understanding patterns and determinants of human mortality is an essential part of medical research.

Furthermore, mortality must be accounted for in many longitudinal analyses that are not directly focused on it as an outcome. All longitudinal studies of human growth, development, or change may be influenced by the potential for and timing of death. For example, a study of factors affecting transition from job to job throughout life would be influenced by the mortality of workers during the period of follow-up. While in this sense death may be treated as a kind of censoring event (in the technical jargon of survival analysis), in other cases it may be an endpoint of interest in its own right. Finally, for a proper analysis of the likelihood of any particular outcome occurring (e.g., a change in jobs, a loss of independence, remission or onset of a disease), a thorough understanding of the methods for survival analysis is crucial.

Day SM, Reynolds RJ, Kush SJ. The relationship of life expectancy to the development and valuation of life care plans. NeuroRehabilitation. 2015;36(3):253-266.
Reynolds RJ, Kush SJ, Day SM, Vachon P. Comparative mortality and risk factors for death among Justices of the Supreme Court of the United States, 1789-2013: experience from an occupational cohort with over two centuries of follow-up. J Insur Med. 2015;45:9-16.
Reynolds RJ, Day SM, Kanikkannan L. Viability of internal comparisons for epidemiological research in the US astronaut corps. npj Microgravity. 2023 May 12;9:36.
Day SM, Reynolds RJ, Kush SJ. Extrapolating published survival curves to obtain evidence-based estimates of life expectancy in cerebral palsy. Dev Med Child Neurol. 2015 Dec;57(12):1105-18.
Day SM, Reynolds RJ. Survival, mortality, and life expectancy. In: Al-Zwaini IJ, editor. Cerebral Palsy – Clinical and Therapeutic Aspects. London: IntechOpen; 2018. p. 45-64.

Areas of research

We have extensive experience in areas such as:

Life Expectancy
Neurological disorders, injuries and developmental disabilities
Cerebral palsy (CP)
Oncology
Epidemiology
Environmental health
Biostatistics
Health services research
Major League Baseball

Methods we know

  • Sampling techniques
  • Measures of epidemiological effect: AR, PAR, AF, PAF, prevalence, incidence, RR, RD, OR, SMR
  • Crude and adjusted mortality rates
  • Standardized mortality ratios
  • Life tables and life expectancy calculations
  • Product-limit method of survival analysis (Kaplan-Meier survival analysis)
  • Cox proportional hazards regression
  • Parametric survival regression
  • Aalen-Johansen estimator of multistate probabilities of transition
  • Ordinary Least-Squares (OLS) regression
  • Generalized linear models: Logistic, Poisson, and negative binomial regressions
  • Generalized additive models for location, scale, and shape (GAMLSS)
  • Box-Cox power exponential distributions
  • Zero-Inflated Poisson (ZIP) models and hurdle models
  • Linear mixed models
  • Generalized linear mixed models
  • Hierarchical models
  • Generalized estimating equations
  • Principle components analysis
  • Path models, factor analysis (FA), and full structural equation models (SEM)
  • Latent class models
  • Propensity score and instrumental variable analysis
  • Bayesian analysis
  • Statistical power analysis