Multistate models are often used in social research to analyze how individuals move between states. A typical application is the estimation of the lifetime spent in a certain state, like the lifetime spent in employment, or the lifetime spent in good health. Unfortunately, the estimation of such quantities is prone to several biases. In this paper, we study the bias due to the often implicitly used assumption that there are no unobserved transitions between states. This assumption does often not hold for the panel data typically used to estimate multistate models, as the states occupied by individuals are only known at specific points in time, and further transitions between panel waves are not recorded. We present partially identified estimates of the lifetime spent in a state, or worse-case bounds, which show the maximum possible level of bias due to unobserved transitions. We provide two examples studying the lifetime spent in disability (disabled life expectancy; DLE). The first example applies our methods to results on cohort trends in DLE in the U.S. taken from Crimmins et al. (2009). In the second example, we replicate findings from Mehta and Myrskylä (2017), and apply our methods to data from the U.S. Health and Retirement Study (HRS) in order to estimate the effects of health behaviors on DLE.

社会研究中经常使用多州模型来分析个人在州之间的迁移情况。一个典型的应用是在某种状态下花费的生命周期的估算值，例如在工作中花费的生命周期或在身体健康方面的生命周期。不幸的是，这种数量的估计容易产生一些偏差。在本文中，我们研究由于经常隐含使用的假设而导致的偏差，即状态之间不存在未观察到的过渡。该假设通常不适用于通常用于估计多状态模型的面板数据，因为只有在特定的时间点才知道个人所占据的状态，并且不会记录面板波之间的进一步转换。我们提供了在状态或更坏情况下花费的生命周期的部分确定的估算值，这些图显示了由于未观察到的过渡而导致的最大可能偏差水平。我们提供了两个示例来研究在残疾中度过的一生（预期寿命； DLE）。第一个例子将我们的方法应用于来自美国Crimmins等人的DLE队列趋势的结果。（2009）。在第二个示例中，我们复制了Mehta和Myrskylä（2017）的发现，并将我们的方法应用于来自美国健康与退休研究（HRS）的数据，以评估健康行为对DLE的影响。