The Impact Of Missing Risk Factor Data On Semiparametric Group-Based Trajectory Models

Keywords

Longitudinal data; Missing data; Monte Carlo; Offending trajectories; Risk factors

Abstract

Purpose: To investigate how missing data (Missing Completely at Random [MCAR] vs. Missing Not at Random [MNAR]) on risk factors can impact trajectory solutions (i.e., latent class probabilities) and coefficient estimates capturing the relationship between covariates and trajectory group solutions using a semiparametric group-based trajectory modeling (GBTM) approach. Methods: To address this issue, we conducted a systematic investigation using Monte Carlo simulation. Data were generated from a population with known growth parameters and risk factors. Observations for risk factors were then systematically deleted in a way that reflects key missing data assumptions (MCAR and MNAR). Models were then estimated to test the sensitivity of the estimates to each missing data scenario. Results: Two key findings emerged: (1) trajectory solutions were largely unaffected by missing data on risk factors; and, (2) there was some degree of bias in estimating relationships between risk factors and trajectory group membership when data were missing on those risk factors. Conclusions: GBTM may be useful for testing etiological explanations of long-term patterns of offending. Missing data on risk factors poses a threat to this approach, however.

Publication Date

9-1-2018

Publication Title

Journal of Developmental and Life-Course Criminology

Volume

4

Issue

3

Number of Pages

276-296

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/s40865-018-0085-x

Socpus ID

85068067869 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85068067869

This document is currently not available here.

Share

COinS