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Georgeson AR, Alvarez-Bartolo D, MacKinnon DP. A sensitivity analysis for temporal bias in cross-sectional mediation. Psychol Methods 2023:2024-37233-001. [PMID: 38127571 PMCID: PMC11190060 DOI: 10.1037/met0000628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
For over three decades, methodologists have cautioned against the use of cross-sectional mediation analyses because they yield biased parameter estimates. Yet, cross-sectional mediation models persist in practice and sometimes represent the only analytic option. We propose a sensitivity analysis procedure to encourage a more principled use of cross-sectional mediation analysis, drawing inspiration from Gollob and Reichardt (1987, 1991). The procedure is based on the two-wave longitudinal mediation model and uses phantom variables for the baseline data. After a researcher provides ranges of possible values for cross-lagged, autoregressive, and baseline Y and M correlations among the phantom and observed variables, they can use the sensitivity analysis to identify longitudinal conditions in which conclusions from a cross-sectional model would differ most from a longitudinal model. To support the procedure, we first show that differences in sign and effect size of the b-path occur most often when the cross-sectional effect size of the b-path is small and the cross-lagged and the autoregressive correlations are equal or similar in magnitude. We then apply the procedure to cross-sectional analyses from real studies and compare the sensitivity analysis results to actual results from a longitudinal mediation analysis. While no statistical procedure can replace longitudinal data, these examples demonstrate that the sensitivity analysis can recover the effect that was actually observed in the longitudinal data if provided with the correct input information. Implications of the routine application of sensitivity analysis to temporal bias are discussed. R code for the procedure is provided in the online supplementary materials. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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MacKinnon DP, Lamp SJ. A Unification of Mediator, Confounder, and Collider Effects. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2021; 22:1185-1193. [PMID: 34164779 DOI: 10.1007/s11121-021-01268-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/09/2021] [Indexed: 12/21/2022]
Abstract
Third-variable effects, such as mediation and confounding, are core concepts in prevention science, providing the theoretical basis for investigating how risk factors affect behavior and how interventions change behavior. Another third variable, the collider, is not commonly considered but is also important for prevention science. This paper describes the importance of the collider effect as well as the similarities and differences between these three third-variable effects. The single mediator model in which the third variable (T) is a mediator of the independent variable (X) to dependent variable (Y) effect is used to demonstrate how to estimate each third-variable effect. We provide difference in coefficients and product of coefficients estimators of the effects and demonstrate how to calculate these values with real data. Suppression effects are defined for each type of third-variable effect. Future directions and implications of these results are discussed.
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Affiliation(s)
- David P MacKinnon
- Department of Psychology, Arizona State University, PO Box 871104, Tempe, AZ, 85287-1104, USA.
| | - Sophia J Lamp
- Department of Psychology, Arizona State University, PO Box 871104, Tempe, AZ, 85287-1104, USA
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Gonzalez O, Georgeson AR, Pelham WE, Fouladi RT. Estimating classification consistency of screening measures and quantifying the impact of measurement bias. Psychol Assess 2021; 33:596-609. [PMID: 33998821 DOI: 10.1037/pas0000938] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Screening measures are used in psychology and medicine to identify respondents who are high or low on a construct. Based on the screening, the evaluator assigns respondents to classes corresponding to different courses of action: Make a diagnosis versus reject a diagnosis; provide services versus withhold services; or conduct further assessment versus conclude the assessment process. When measures are used to classify individuals, it is important that the decisions be consistent and equitable across groups. Ideally, if respondents completed the screening measure repeatedly in quick succession, they would be consistently assigned into the same class each time. In addition, the consistency of the classification should be unrelated to the respondents' background characteristics, such as sex, race, or ethnicity (i.e., the measure is free of measurement bias). Reporting estimates of classification consistency is a common practice in educational testing, but there has been limited application of these estimates to screening in psychology and medicine. In this article, we present two procedures based on item response theory that are used (a) to estimate the classification consistency of a screening measure and (b) to evaluate how classification consistency is impacted by measurement bias across respondent groups. We provide R functions to conduct the procedures, illustrate the procedures with real data, and use Monte Carlo simulations to guide their appropriate use. Finally, we discuss how estimates of classification consistency can help assessment specialists make more informed decisions on the use of a screening measure with protected groups (e.g., groups defined by gender, race, or ethnicity). (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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Affiliation(s)
- Oscar Gonzalez
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill
| | - A R Georgeson
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill
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Tofighi D, Kelley K. Improved inference in mediation analysis: Introducing the model-based constrained optimization procedure. Psychol Methods 2020; 25:496-515. [PMID: 32191106 DOI: 10.1037/met0000259] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
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. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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Affiliation(s)
| | - Ken Kelley
- Department of Information Technology, Analytics, and Operations
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Metzler-Baddeley C, Mole JP, Sims R, Fasano F, Evans J, Jones DK, Aggleton JP, Baddeley RJ. Fornix white matter glia damage causes hippocampal gray matter damage during age-dependent limbic decline. Sci Rep 2019; 9:1060. [PMID: 30705365 PMCID: PMC6355929 DOI: 10.1038/s41598-018-37658-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Accepted: 12/11/2018] [Indexed: 12/21/2022] Open
Abstract
Aging leads to gray and white matter decline but their causation remains unclear. We explored two classes of models of age and dementia risk related brain changes. The first class of models emphasises the importance of gray matter: age and risk-related processes cause neurodegeneration and this causes damage in associated white matter tracts. The second class of models reverses the direction of causation: aging and risk factors cause white matter damage and this leads to gray matter damage. We compared these models with linear mediation analysis and quantitative MRI indices (from diffusion, quantitative magnetization transfer and relaxometry imaging) of tissue properties in two limbic structures implicated in age-related memory decline: the hippocampus and the fornix in 166 asymptomatic individuals (aged 38–71 years). Aging was associated with apparent glia but not neurite density damage in the fornix and the hippocampus. Mediation analysis supported white matter damage causing gray matter decline; controlling for fornix glia damage, the correlations between age and hippocampal damage disappear, but not vice versa. Fornix and hippocampal differences were both associated with reductions in episodic memory performance. These results suggest that fornix white matter glia damage may cause hippocampal gray matter damage during age-dependent limbic decline.
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Affiliation(s)
- Claudia Metzler-Baddeley
- Cardiff University Brain Research Imaging Centre (CUBRIC), Maindy Road, Cathays, Cardiff, CF24 4HQ, UK.
| | - Jilu P Mole
- Cardiff University Brain Research Imaging Centre (CUBRIC), Maindy Road, Cathays, Cardiff, CF24 4HQ, UK
| | - Rebecca Sims
- Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Maindy Road, Cathays, Cardiff, CF24 4HQ, UK
| | - Fabrizio Fasano
- Cardiff University Brain Research Imaging Centre (CUBRIC), Maindy Road, Cathays, Cardiff, CF24 4HQ, UK.,Siemens Healthcare, Head Office, Sir William Siemens Square, Surrey, GU16 8QD, UK
| | - John Evans
- Cardiff University Brain Research Imaging Centre (CUBRIC), Maindy Road, Cathays, Cardiff, CF24 4HQ, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Maindy Road, Cathays, Cardiff, CF24 4HQ, UK.,School of Psychology, Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, 3065, Australia
| | - John P Aggleton
- School of Psychology, Cardiff University, Tower Building, 70 Park Place, Cardiff, CF10 3AT, UK
| | - Roland J Baddeley
- Experimental Psychology, University of Bristol, 12a Priory Road, Bristol, BS8 1TU, UK
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Miočević M, Gonzalez O, Valente MJ, MacKinnon DP. A Tutorial in Bayesian Potential Outcomes Mediation Analysis. STRUCTURAL EQUATION MODELING : A MULTIDISCIPLINARY JOURNAL 2018; 25:121-136. [PMID: 29910595 PMCID: PMC5999040 DOI: 10.1080/10705511.2017.1342541] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example.
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Affiliation(s)
- Milica Miočević
- Department of Methodology and Statistics, Utrecht University
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