scholarly journals Disaggregating Associations of Between-Person Differences in Change over Time from Associations of Within-Person Fluctuation in Longitudinal Data

2021 ◽  
Author(s):  
Lesa Hoffman
Keyword(s):  
Longitudinal Data ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Time Varying ◽  
Simulation Methods ◽  
Longitudinal Models ◽  
Time Specific ◽  
Over Time

In longitudinal models with time-varying predictors, the need to distinguish their within-person (WP) relations of time-specific residuals from their between-person (BP) relations of individual means is relatively well-known. In contrast, the need to further distinguish their BP relations of individual time slopes has received much less attention. This article addresses the deleterious impact that ignoring effects of individual time slopes in time-varying predictors can have on the recovery of BP intercept and WP residual relations in commonly used variants of longitudinal models. Using simulation methods and analyses of example data, this problem is demonstrated within univariate longitudinal models (i.e., multilevel or mixed-effects models using observed predictors), as well as in multivariate longitudinal models (i.e., structural equation models using latent predictors, including those for cross-lagged relations). Recommendations are provided for how to avoid conflating the BP and WP associations of longitudinal variables in practice.

scholarly journals Disaggregating Between-Person Time Slope Effects from Within-Person Effects

2020 ◽  
Author(s):  
Lesa Hoffman
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Longitudinal Research ◽  
Mixed Effects Models ◽  
Residual Effects ◽  
Levels Of Analysis ◽  
Longitudinal Models ◽  
Mean Centering ◽  
Auto Regressive ◽  
Multilevel Structural Equation

A primary motivation for conducting longitudinal research is to examine variable relationships at both the between-person (BP) and within-person (WP) levels of analysis. In models with time-varying predictors, the problems of conflating WP residual effects with BP intercept effects are relatively well-known, whereas the potential for additional conflation by BP effects of time has received much less attention. The present study used simulation methods to demonstrate the deleterious impact that ignoring BP relations for time slopes across variables can have on the recovery of contemporaneous or lagged WP effects, as well as BP intercept effects, within common longitudinal models (i.e., in mixed-effects models using person-mean-centering, single-level and multilevel structural equation models, and auto-regressive cross-lag panel models). Recommendations are provided for how to use different options for univariate or multivariate longitudinal models to avoid conflating effects across BP and WP levels of analysis in practice.

scholarly journals Accounting for confounding by time, early intervention adoption, and time-varying effect modification in the design and analysis of stepped-wedge designs: application to a proposed study design to reduce opioid-related mortality

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lior Rennert ◽  
Moonseong Heo ◽  
Alain H. Litwin ◽  
Victor De Gruttola
Keyword(s):  
Mixed Effects ◽  
Effect Modification ◽  
External Factors ◽  
Mixed Effects Models ◽  
Time Varying ◽  
Intervention Effect ◽  
Type 1 Error ◽  
And Control ◽  
Related Mortality

Abstract Background Beginning in 2019, stepped-wedge designs (SWDs) were being used in the investigation of interventions to reduce opioid-related deaths in communities across the United States. However, these interventions are competing with external factors such as newly initiated public policies limiting opioid prescriptions, media awareness campaigns, and the COVID-19 pandemic. Furthermore, control communities may prematurely adopt components of the intervention as they become available. The presence of time-varying external factors that impact study outcomes is a well-known limitation of SWDs; common approaches to adjusting for them make use of a mixed effects modeling framework. However, these models have several shortcomings when external factors differentially impact intervention and control clusters. Methods We discuss limitations of commonly used mixed effects models in the context of proposed SWDs to investigate interventions intended to reduce opioid-related mortality, and propose extensions of these models to address these limitations. We conduct an extensive simulation study of anticipated data from SWD trials targeting the current opioid epidemic in order to examine the performance of these models in the presence of external factors. We consider confounding by time, premature adoption of intervention components, and time-varying effect modification— in which external factors differentially impact intervention and control clusters. Results In the presence of confounding by time, commonly used mixed effects models yield unbiased intervention effect estimates, but can have inflated Type 1 error and result in under coverage of confidence intervals. These models yield biased intervention effect estimates when premature intervention adoption or effect modification are present. In such scenarios, models incorporating fixed intervention-by-time interactions with an unstructured covariance for intervention-by-cluster-by-time random effects result in unbiased intervention effect estimates, reach nominal confidence interval coverage, and preserve Type 1 error. Conclusions Mixed effects models can adjust for different combinations of external factors through correct specification of fixed and random time effects. Since model choice has considerable impact on validity of results and study power, careful consideration must be given to how these external factors impact study endpoints and what estimands are most appropriate in the presence of such factors.

scholarly journals Joint mixed-effects models for causal inference with longitudinal data

2017 ◽  
Vol 37 (5) ◽  
pp. 829-846 ◽  
Author(s):  
Michelle Shardell ◽  
Luigi Ferrucci
Keyword(s):  
Longitudinal Data ◽  
Causal Inference ◽  
Mixed Effects ◽  
Mixed Effects Models

scholarly journals PSVI-24 Evolution and risk factors of pulmonary lesions evaluated with thoracic ultrasonography in pre-weaned beef calves at arrival at the rearing facilities

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 208-209
Author(s):  
Carolina Tejero ◽  
Maria Devant ◽  
Joan Pujols ◽  
Sonia Marti
Keyword(s):  
Risk Factors ◽  
Respiratory Disease ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Beef Calves ◽  
Pulmonary Lesions ◽  
Current Health ◽  
D 12 ◽  
Over Time ◽  
Transport Type

Abstract Bovine respiratory disease (BRD) is one of the main health problems in in pre-weaning calves at rearing farms. The aim of this study was to evaluate the evolution of pulmonary lesions using a thoracic ultrasonography and its association with predictable risk factors. Thoracic ultrasonography was performed in 811 calves (45–56 kg of BW and 3–4 wk of age) on 5 different rearing facilities at arrival and from d 12 to d 27 after arrival. Thoracic ultrasonography score was classified based on Adams and Buczinski (2016). Weight (light vs heavy), breed (dairy vs crossbred), source (national vs international), season, and number of calves arriving at the facility were recorded and used to evaluate risk factors. Data were analyzed using mixed effects models. At arrival 31% of the calvespresented pulmonary lesions, 21% were mild and 10% severe. After 12–27 d the number of calves without lesion decreased (P < 0.001; 70.3 ± 3.93% vs 34.0 ± 3.93%), number of severe lesions increased (P < 0.001; 8.9% ± 4.78% vs 40.1% ± 4.78%), however mild lesions were constant over time. Percentage of severe pulmonary lesions were greater (P < 0.001) when calves arrived in fall than spring, summer and winter; when calf source (P < 0.001) was international than national; and when their breeds (P < 0.001) were crossbred than dairy. A tendency (P = 0.09) was observed with the increase of severe pulmonary lesions with an increase of animals received by batch. At arrival, calves already had pulmonary lesions that increased in severity over time, therefore current health protocols do not mitigate BRD and more attention should be taken on risk factors such as breed, transport type and season.

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