2005
DOI: 10.1002/sim.2148
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Latent transition analysis with covariates: pubertal timing and substance use behaviours in adolescent females

Abstract: We investigate the impact of pubertal development, age, and its interaction on female substance use behaviour. An extended latent transition model with two latent variables is used to reflect the dependency of adolescent substance use on pubertal development and age. A sample of females in grades 7-12 is analysed using maximum-likelihood estimation. Analyses indicate that experiencing puberty is related to increased substance use for all age groups. Among females aged 12-15, those who have experienced puberty … Show more

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Cited by 73 publications
(68 citation statements)
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“…Substance use and sensation seeking increase at puberty, with postpubertal individuals engaging in higher levels of the behaviors than prepubertal individuals (Martin et al, 2002;Lanza & Collins, 2002;Patton et al, 2004;Chung et al, 2005;Burt et al, 2006;Costello et al, 2007). However, this study found no evidence that early puberty onset has a long-term relationship to the expression of these behaviors in adulthood.…”
Section: Discussioncontrasting
confidence: 57%
“…Substance use and sensation seeking increase at puberty, with postpubertal individuals engaging in higher levels of the behaviors than prepubertal individuals (Martin et al, 2002;Lanza & Collins, 2002;Patton et al, 2004;Chung et al, 2005;Burt et al, 2006;Costello et al, 2007). However, this study found no evidence that early puberty onset has a long-term relationship to the expression of these behaviors in adulthood.…”
Section: Discussioncontrasting
confidence: 57%
“…We also assume that the sequence L t constitutes a first-order Markov chain for t = 2, …, T. In (1), only the marginal probability of class membership at initial time t = 1, δ l 1 , is estimated; the marginal probabilities of class membership at time t (≥ 2) are not directly estimated, but rather are a function of other parameters. The marginal prevalence of each class at time t (≥ 2) can be calculated as Using (1), the contribution of the ith individual to the likelihood function of Y 1 , …, Y T is given by (2) For simplicity, consider a sample of n individuals who responded to M binary items measured at two time periods. We hereafter consider the constrained LTA model where the item-response probabilities (ρ-parameters) are constrained to be equal across time, although an extension to unconstrained LTA is straightforward.…”
Section: Latent Transition Model and Estimation Algorithmsmentioning
confidence: 99%
“…In Bayesian analysis for LTA, we are interested in describing the posterior, P[θ | y 1 , y 2 ]. The MCMC algorithm treats the class membership of each individual as missing data, and simulates the augmented posterior P[θ | y 1 , y 2 , z] as if class membership were known. Here, the element of z = (z 1 , …, z n ), z i denotes a twodimensional array indicating the latent class in which the ith individual belongs, so that z i(l 1 , l 2 ) ∈ {0, 1} and Σ l 1 Σ l 2 z i(l 1 , l 2 ) = 1.…”
Section: Latent Transition Model and Estimation Algorithmsmentioning
confidence: 99%
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