Assessing and comparing models for sequence data by microsimulation (with Supplementary Material)

Abstract

We consider the study of the occurrence of different states over (discrete) time for a set of subjects, focusing on the resulting trajectories as a whole rather than on the occurrence of specific events. Such situation occurs commonly in a variety of settings, for example in the social and biomedical sciences. Model based approaches, such as multi-state models or hidden Markov models, designed to study the transitions across states, are being used increasingly to analyze trajectories and to study their relationships with a set of explanatory variables. Comparing the performance of competing models, typically based upon different assumptions, is an open problem. To accomplish this task, we introduce a novel approach based on microsimulation, i.e. the model-based generation of trajectories, and on dissimilarities. In particular, we discuss some criteria to compare competing models with respect to their ability to generate trajectories similar to the ones in the data. We illustrate the methods using life course trajectories arising from the Fertility and Family Survey study.

Sequence analysis: Its past, present, and future

“…Although SA has benefitted from several other methodological traditions-cluster analysis, principal coordinate analysis, network analysis (Cornwell, 2015(Cornwell, , 2018, Markov models (Helske et al, 2018;Helske and Helske, 2019;Piccarreta and Bonetti, 2019), analysis of variance (Studer et al, 2011), and the combination of traditions (such as cluster with qualitative comparative analysis (Borgna and Struffolino 2018)-three major problems have remained unaddressed. First, in most applications it is advisable to use sequences of equal length, as a result, censored observations (e.g., individuals followed for a different number of years or otherwise with different record lengths) must be excluded, thus having several methodological and substantive implications for sample selection.…”