Mixed effects models for recurrent events data with partially observed time-varying covariates: Ecological momentary assessment of smoking

Background: Most adolescents are sleep deprived on school days, yet how they self-regulate their sleep-wake behaviours is poorly understood. Using ecological momentary assessment, this intense longitudinal study explored patterns of adolescents’ daily bedtime and risetime planning and execution, and whether these behaviours predicted sleep opportunity.Methods: Every afternoon, for 2 school weeks and the subsequent 2 vacation weeks, 205 (54.1% female, 64.4% non-White) adolescents from Year 10-12 (M±SDage = 16.9±0.9) reported their plans for bedtime (BT) that evening, and for risetimes (RT) the following day. Actual daily sleep was measured via actigraphy and sleep diary.Results: Some adolescents never planned bedtime (school 19.5%, non-school 53.2%) or risetime (school 1.5%, non-school 24.4%). More adolescents planned consistently (≥75% of days) on schooldays (BT=29.9%, RT=61.3%) compared on non-schooldays (BT=3.5%, RT=2.5%). On average adolescents went to bed later than planned, delaying their bedtime longer on non-schooldays (71min) compared to schooldays (46min). Of those who executed their plans within ≤15 minutes, more did it consistently (≥75% of days) on schooldays (BT=40.9%, RT=67.7%) than on non-schooldays (BT=29.7%, RT=58.6%). Mixed effects models utilizing daily data, controlling for sex, race, and study day, showed that bedtime planning predicted longer time in bed (TIB; p < .01) on schooldays and shorter TIB on non-schooldays (p < .01); greater delay in actual (compared to planned) bedtime predicted shorter TIB (p-values < .001).Conclusions: Adolescents may require support during the transition from parent-controlled to autonomous sleep self-regulation. Bedtime planning on school nights and going to bed as planned are two modifiable sleep regulatory behaviours that are protective and may serve as therapeutic targets for increasing sleep opportunity in adolescents.

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Mixed Effects Models with Censored Covariates, with Applications in HIV/AIDS Studies

Journal of Probability and Statistics ◽

10.1155/2018/1581979 ◽

2018 ◽

Vol 2018 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Lang Wu ◽

Hongbin Zhang

Keyword(s):

Longitudinal Studies ◽

Incomplete Data ◽

Mixed Effects ◽

Mixed Effects Models ◽

Time Varying ◽

Response Models ◽

Covariate Data ◽

The Common ◽

Time Varying Covariates ◽

Hiv Aids

Mixed effects models are widely used for modelling clustered data when there are large variations between clusters, since mixed effects models allow for cluster-specific inference. In some longitudinal studies such as HIV/AIDS studies, it is common that some time-varying covariates may be left or right censored due to detection limits, may be missing at times of interest, or may be measured with errors. To address these “incomplete data“ problems, a common approach is to model the time-varying covariates based on observed covariate data and then use the fitted model to “predict” the censored or missing or mismeasured covariates. In this article, we provide a review of the common approaches for censored covariates in longitudinal and survival response models and advocate nonlinear mechanistic covariate models if such models are available.

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A computationally efficient method for nonlinear mixed-effects models with nonignorable missing data in time-varying covariates

Computational Statistics & Data Analysis ◽

10.1016/j.csda.2006.07.036 ◽

2007 ◽

Vol 51 (5) ◽

pp. 2410-2419 ◽

Cited By ~ 7

Author(s):

Lang Wu

Keyword(s):

Missing Data ◽

Efficient Method ◽

Mixed Effects ◽

Mixed Effects Models ◽

Time Varying ◽

Nonignorable Missing Data ◽

Computationally Efficient ◽

Nonlinear Mixed Effects Models ◽

Nonignorable Missing ◽

Time Varying Covariates

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Modeling Intraindividual Variability in Three-Level Multilevel Models

Methodology ◽

10.1027/1614-2241/a000150 ◽

2018 ◽

Vol 14 (3) ◽

pp. 95-108 ◽

Cited By ~ 4

Author(s):

Steffen Nestler ◽

Katharina Geukes ◽

Mitja D. Back

Keyword(s):

Multilevel Model ◽

Multilevel Models ◽

Mixed Effects ◽

Repeated Measurements ◽

Scale Model ◽

Suggested Approach ◽

Psychological Study ◽

Level Data ◽

Ecological Momentary ◽

Momentary Assessment

Abstract. The mixed-effects location scale model is an extension of a multilevel model for longitudinal data. It allows covariates to affect both the within-subject variance and the between-subject variance (i.e., the intercept variance) beyond their influence on the means. Typically, the model is applied to two-level data (e.g., the repeated measurements of persons), although researchers are often faced with three-level data (e.g., the repeated measurements of persons within specific situations). Here, we describe an extension of the two-level mixed-effects location scale model to such three-level data. Furthermore, we show how the suggested model can be estimated with Bayesian software, and we present the results of a small simulation study that was conducted to investigate the statistical properties of the suggested approach. Finally, we illustrate the approach by presenting an example from a psychological study that employed ecological momentary assessment.

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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

BMC Medical Research Methodology ◽

10.1186/s12874-021-01229-6 ◽

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.

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