Using Instrumental Variables to Estimate the Parameters in Unconditional and Conditional Second-Order Latent Growth Models

Nestler, Steffen

doi:10.1080/10705511.2014.934948

Cited by 15 publications

(16 citation statements)

References 15 publications

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“…We recommend using all IVs to conduct the specification test. Recently, Nestler () proposed an IV‐based test to detected omitted interaction and quadrature effects. All these studies have shown that the specification test is a promising assessment tool for many SEM models.…”

Section: Discussionmentioning

confidence: 99%

“…Bollen, Kolenikov, and Bauldry () generalized 2SLS/IV to the generalized method of moments for SEMs, which handles heteroscedasticity. 2SLS/IV has been applied by Nestler () to handle equality constraints and by Nestler () to fit latent growth models.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, if equation is correct and misspecification in other equations does not invalidate the IVs, PIV is more robust against model misspecification (Bollen, ). However, robustness only applies to factor loadings, not necessarily to factor covariances (Nestler, ).…”

Section: Introductionmentioning

confidence: 99%

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Selecting polychoric instrumental variables in confirmatory factor analysis: An alternative specification test and effects of instrumental variables

Jin

Cao

2018

Brit J Math & Statis

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The polychoric instrumental variable (PIV) approach is a recently proposed method to fit a confirmatory factor analysis model with ordinal data. In this paper, we first examine the small-sample properties of the specification tests for testing the validity of instrumental variables (IVs). Second, we investigate the effects of using different numbers of IVs. Our results show that specification tests derived for continuous data are extremely oversized at all sample sizes when applied to ordinal variables. Possible modifications for ordinal data are proposed in the present study. Simulation results show that the modified specification tests with all available IVs are able to detect model misspecification. In terms of estimation accuracy, the PIV approach where the IVs outnumber the endogenous variables by one produces a lower bias but a higher variation than the PIV approach with more IVs for correctly specified factor loadings at small samples.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Selecting polychoric instrumental variables in confirmatory factor analysis: An alternative specification test and effects of instrumental variables

Jin

Cao

2018

Brit J Math & Statis

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“…Given the also increased interest in longitudinal latent outcome models , it is important to evaluate how estimates of coefficients for observed predictors are influenced by incorrectly assuming time invariance in the measurement model for a longitudinal latent outcome . While MLE estimates will probably have the similar degree of bias as observed here, the robustness of the IV estimator will largely depend on whether time invariance constraints are imposed or not. Our study focused on continuous exposure surrogates, and we assumed multivariate normality to obtain ML estimates.…”

Section: Discussionmentioning

confidence: 69%

Estimators for longitudinal latent exposure models: examining measurement model assumptions

2017

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Latent variable (LV) models are increasingly being used in environmental epidemiology as a way to summarize multiple environmental exposures and thus minimize statistical concerns that arise in multiple regression. LV models may be especially useful when multivariate exposures are collected repeatedly over time. LV models can accommodate a variety of assumptions, but at the same time present the user with many choices for model specification particularly in the case of exposure data collected repeatedly over time. For instance, the user could assume conditional independence of observed exposure biomarkers given the latent exposure, and, in the case of longitudinal latent exposure variables, time invariance of the measurement model. Choosing which assumptions to relax is not always straightforward. We were motivated by a study of prenatal lead exposure and mental development, where assumptions of the measurement model for the time-changing longitudinal exposure have appreciable impact on (maximum-likelihood) inferences about the health effects of lead exposure. Although we were not particularly interested in characterizing the change of the latent variable itself, imposing a longitudinal latent variable structure on the repeated multivariate exposure measures could result in high efficiency gains for the exposure-disease association. We examine the biases of maximum likelihood estimators when assumptions about the measurement model for the longitudinal latent exposure variable are violated. We adapt existing instrumental variable (IV) estimators to the case of longitudinal exposures and propose them as an alternative to estimate the health effects of a time-changing latent predictor. We show that IV estimators remain unbiased for a wide range of data generating models and have advantages in terms of mean squared error.

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“…Endogeneity often arises when a causal model is poorly specified therby introducing a structural bias in the estimation of its parameters. This may result from a measurement error in variables [ 3 ], an unobserved variable [ 4 ] or an inverse causality between the outcome and some regressors. The general IV method of estimation attempts to remove this bias by using structural equations which incorporate instrumental variables in the model.…”

Section: Introductionmentioning

confidence: 99%

Instrumental variable analysis in the context of dichotomous outcome and exposure with a numerical experiment in pharmacoepidemiology

Koladjo

Escolano

Tubert‐Bitter

2018

BMC Med Res Methodol

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BackgroundIn pharmacoepidemiology, the prescription preference-based instrumental variables (IV) are often used with linear models to solve the endogeneity due to unobserved confounders even when the outcome and the endogenous treatment are dichotomous variables. Using this instrumental variable, we proceed by Monte-Carlo simulations to compare the IV-based generalized method of moment (IV-GMM) and the two-stage residual inclusion (2SRI) method in this context.MethodsWe established the formula allowing us to compute the instrument’s strength and the confounding level in the context of logistic regression models. We then varied the instrument’s strength and the confounding level to cover a large range of scenarios in the simulation study. We also explore two prescription preference-based instruments.ResultsWe found that the 2SRI is less biased than the other methods and yields satisfactory confidence intervals. The proportion of previous patients of the same physician who were prescribed the treatment of interest displayed a good performance as a proxy of the physician’s preference instrument.ConclusionsThis work shows that when analysing real data with dichotomous outcome and exposure, appropriate 2SRI estimation could be used in presence of unmeasured confounding.

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Using Instrumental Variables to Estimate the Parameters in Unconditional and Conditional Second-Order Latent Growth Models

Cited by 15 publications

References 15 publications

Selecting polychoric instrumental variables in confirmatory factor analysis: An alternative specification test and effects of instrumental variables

Selecting polychoric instrumental variables in confirmatory factor analysis: An alternative specification test and effects of instrumental variables

Estimators for longitudinal latent exposure models: examining measurement model assumptions

Instrumental variable analysis in the context of dichotomous outcome and exposure with a numerical experiment in pharmacoepidemiology

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