Rectangularization of the survival curve reconsidered: The maximum inner rectangle approach.

Rectangularization of the survival curve-a key analytical framework in mortality research-relies on assumptions that have become partially obsolete in high-income countries due to mortality reductions among the oldest old. We propose refining the concept to adjust for recent and potential future mortality changes. Our framework, the 'maximum inner rectangle approach' (MIRA) considers two types of rectangularization.

Modelling and simulating decision processes of linked lives: An approach based on concurrent processes and stochastic race.

Individuals' decision processes play a central role in understanding modern migration phenomena and other demographic processes. Their integration into agent-based computational demography depends largely on suitable support by a modelling language. We are developing the Modelling Language for Linked Lives (ML3) to describe the diverse decision processes of linked lives succinctly in continuous time.

A re-analysis of the long-term effects on life expectancy of the Great Finnish Famine of 1866-68.

The results of studies exploring the long-term consequences of famine during foetal or infant development are inconsistent. We tested the hypothesis that selection forces occurring during a famine change the distribution of frailty in the affected cohorts, possibly hiding negative long-term effects. Using mortality data for Finland, gathered from the Human Mortality Database, we explored the effect of being born during the Great Finnish Famine of 1866-68 by comparing mortality at age 60 and over for the 1850-89 births, taking into account unobserved cohort heterogeneity.

Tri-axial high-resolution acceleration for oxygen uptake estimation: Validation of a multi-sensor device and a novel analysis method.

We validated a multi-sensor chest-strap against indirect calorimetry and further introduced the total-acceleration-variability (TAV) method for analyzing high-resolution accelerometer data. Linear regression models were developed to predict oxygen uptake from the TAV-processed multi-sensor data. Individual correlations between observed and TAV-predicted oxygen uptake (V̇O2) were strong (mean r = 0.