The University of Southampton
University of Southampton Institutional Repository

Investigating the application of generalized additive models to discrete-time event history analysis for birth events

Investigating the application of generalized additive models to discrete-time event history analysis for birth events
Investigating the application of generalized additive models to discrete-time event history analysis for birth events
Discrete-time event history analysis (EHA) is the standard approach taken when modelling fertility histories collected in surveys, where the date of birth is often recorded imprecisely. This method is commonly used to investigate the factors associated with the time to a first or subsequent conception or birth. Although there is an emerging trend towards the smooth incorporation of continuous covariates in the broader literature, this is yet to be formally embraced in the context of birth events. We investigate the formal application of smooth methods implemented via generalized additive models (GAMs) to the analysis of fertility histories. We also determine whether and where GAMs offer a practical improvement over existing approaches. We fit parity-specific logistic GAMs to data from the UK Household Longitudinal Study, learning about the effects of age, period, time since last birth, educational qualification and country of birth. First we select the most parsimonious GAMs that fit the data sufficiently well. Then we compare them with corresponding models that use the existing methods of categorical, polynomial and piecewise linear spline representations in terms of fit, complexity, and substantive insights gained. We find that smooth terms can offer considerable improvements in precision and efficiency, particularly for highly non-linear effects and interactions between continuous variables. Their flexibility enables the detection of important features that are missed or estimated imprecisely by comparator methods. Our findings suggest that GAMs are a useful addition to the demographer’s toolkit. They are highly relevant for motivating future methodological developments in EHA, both for birth events and more generally.
1435-9871
647-694
Ellison, Joanne
d1560ac9-2c6c-49e8-b5c4-aa2258624e97
Berrington, Ann
bd0fc093-310d-4236-8126-ca0c7eb9ddde
Dodd, Erengul
b3faed76-f22b-4928-a922-7f0b8439030d
Forster, Jonathan
e3c534ad-fa69-42f5-b67b-11617bc84879
Ellison, Joanne
d1560ac9-2c6c-49e8-b5c4-aa2258624e97
Berrington, Ann
bd0fc093-310d-4236-8126-ca0c7eb9ddde
Dodd, Erengul
b3faed76-f22b-4928-a922-7f0b8439030d
Forster, Jonathan
e3c534ad-fa69-42f5-b67b-11617bc84879

Ellison, Joanne, Berrington, Ann, Dodd, Erengul and Forster, Jonathan (2022) Investigating the application of generalized additive models to discrete-time event history analysis for birth events. Demographic Research, 47, 647-694, [22]. (doi:10.4054/DemRes.2022.47.22).

Record type: Article

Abstract

Discrete-time event history analysis (EHA) is the standard approach taken when modelling fertility histories collected in surveys, where the date of birth is often recorded imprecisely. This method is commonly used to investigate the factors associated with the time to a first or subsequent conception or birth. Although there is an emerging trend towards the smooth incorporation of continuous covariates in the broader literature, this is yet to be formally embraced in the context of birth events. We investigate the formal application of smooth methods implemented via generalized additive models (GAMs) to the analysis of fertility histories. We also determine whether and where GAMs offer a practical improvement over existing approaches. We fit parity-specific logistic GAMs to data from the UK Household Longitudinal Study, learning about the effects of age, period, time since last birth, educational qualification and country of birth. First we select the most parsimonious GAMs that fit the data sufficiently well. Then we compare them with corresponding models that use the existing methods of categorical, polynomial and piecewise linear spline representations in terms of fit, complexity, and substantive insights gained. We find that smooth terms can offer considerable improvements in precision and efficiency, particularly for highly non-linear effects and interactions between continuous variables. Their flexibility enables the detection of important features that are missed or estimated imprecisely by comparator methods. Our findings suggest that GAMs are a useful addition to the demographer’s toolkit. They are highly relevant for motivating future methodological developments in EHA, both for birth events and more generally.

Text
gam_paper_accepted - Accepted Manuscript
Available under License Creative Commons Attribution.
Download (2MB)

More information

Accepted/In Press date: 8 August 2022
Published date: 28 October 2022

Identifiers

Local EPrints ID: 469595
URI: http://eprints.soton.ac.uk/id/eprint/469595
ISSN: 1435-9871
PURE UUID: dfe68668-8919-49fd-892e-698d9c2e667f
ORCID for Joanne Ellison: ORCID iD orcid.org/0000-0002-6973-8797
ORCID for Ann Berrington: ORCID iD orcid.org/0000-0002-1683-6668
ORCID for Erengul Dodd: ORCID iD orcid.org/0000-0001-6658-0990
ORCID for Jonathan Forster: ORCID iD orcid.org/0000-0002-7867-3411

Catalogue record

Date deposited: 21 Sep 2022 16:31
Last modified: 31 Jan 2023 03:04

Export record

Altmetrics

Contributors

Author: Joanne Ellison ORCID iD
Author: Ann Berrington ORCID iD
Author: Erengul Dodd ORCID iD
Author: Jonathan Forster ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×