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Mixture analysis of longitudinal binary data.

Anton K Formann¹

¹Department of Psychological Basic Research, University of Vienna, Liebiggasse 5, A-1010 Wien, Austria. anton.formann@univie.ac.at

Statistics in Medicine

|September 24, 2005

Summary

Maternal smoking did not significantly impact children's wheeze status in the Harvard Six-Cities study. Analysis revealed no significant differences in wheeze patterns between exposed and unexposed children.

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Area of Science:

Epidemiology
Biostatistics
Respiratory Health

Background:

Longitudinal binary outcomes and their covariation are often modeled using logistic or probit models.
Latent class models offer an alternative by capturing heterogeneity and assuming independence within classes.
The multi-group method of latent class analysis can incorporate categorical covariates.

Purpose of the Study:

To investigate the effect of maternal smoking on children's wheeze status using latent class analysis.
To determine if there are significant differences in wheeze patterns between children exposed and unexposed to maternal smoking.

Main Methods:

Applied the multi-group method of latent class analysis to wheeze data from the Harvard Six-Cities study.
Analyzed data from 537 children, with 187 exposed to maternal smoking and 350 not exposed.
Compared unrestricted and restricted models with up to three latent classes for each subgroup.

Main Results:

No statistically significant effect of maternal smoking on children's wheeze status was found.
No statistically significant differences were detected between the wheeze pattern distributions of exposed and unexposed children.
The multi-group latent class analysis approach was suitable for this binary covariate scenario.

Conclusions:

Maternal smoking does not appear to be a significant factor influencing children's wheeze status in this cohort.
There is no evidence of differing wheeze patterns based on maternal smoking exposure.
Latent class analysis provides a robust framework for analyzing such health outcomes with covariates.

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