Improved Inference in Mediation Analysis: Introducing the Model-Based Constrained Optimization Procedure

doi:10.1037/met0000259

Improved Inference in Mediation Analysis: Introducing the Model-Based Constrained Optimization Procedure

2020-08-01

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DOI：10.1037/met0000259

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Mediation analysis is an important approach for investigating causal pathways. One approach used in mediation analysis is the test of an indirect effect, which seeks to measure how the effect of an independent variable impacts an outcome variable through 1 or more mediators. However, in many situations the proposed tests of indirect effects, including popular confidence interval-based methods, tend to produce poor Type I error rates when mediation does not occur and, more generally, only allow dichotomous decisions of not significant or significant with regards to the statistical conclusion. To remedy these issues, we propose a new method, a likelihood ratio test (LRT), that uses nonlinear constraints in what we term the model-based constrained optimization (MBCO) procedure. The MBCO procedure (a) offers a more robust Type I error rate than existing methods; (b) provides a p value, which serves as a continuous measure of compatibility of data with the hypothesized null model (not just a dichotomous reject or fail-to-reject decision rule); (c) allows simple and complex hypotheses about mediation (i.e., 1 or more mediators; different mediational pathways); and (d) allows the mediation model to use observed or latent variables. The MBCO procedure is based on a structural equation modeling framework (even if latent variables are not specified) with specialized fitting routines, namely with the use of nonlinear constraints. We advocate using the MBCO procedure to test hypotheses about an indirect effect in addition to reporting a confidence interval to capture uncertainty about the indirect effect because this combination transcends existing methods. Translational Abstract Mediation analysis has become one of the most important approaches for investigating causal pathways. One instrument used in mediation analysis is a test of indirect effects that seeks to measure how the effect of an independent variable impacts an outcome variable through one or more mediators. However, in many situations the proposed tests of indirect effects, including popular confidence interval-based methods. tend to produce too few or too many false positives (Type I errors) and are commonly used to make only dichotomous decisions about acceptance (not significant) or rejection (significant) of a null hypothesis. To remedy these issues, we propose a new procedure to test an indirect effect. We call this new procedure the model-based constrained optimization (MBCO) procedure. The MBCO procedure (a) more accurately controls the false positive rate than existing methods; (b) provides a p value, which serves as a continuous measure of compatibility of data with the hypothesized null model (not just a dichotomous reject or fail-to-reject decision rule); (c) allows simple and complex hypotheses about mediation (i.e., one or more mediators; different mediational pathways); and (d) allows the mediation model to use observed or latent variables common in psychological research. We advocate using the MBCO procedure to test hypotheses about an indirect effect in addition to reporting a confidence interval to capture uncertainty about the indirect effect.

Keyword:

indirect effect

mediation analysis

confidence interval

likelihood ratio

model-comparison test

Improved Inference in Mediation Analysis: Introducing the Model-Based Constrained Optimization Procedure

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