Analysis of an incomplete longitudinal composite variable using a marginalized random effects model and multiple imputation

2016 ◽  
Vol 27 (7) ◽  
pp. 2200-2215 ◽  
Author(s):  
Masahiko Gosho ◽  
Kazushi Maruo ◽  
Ryota Ishii ◽  
Akihiro Hirakawa
Keyword(s):  
Multiple Imputation ◽  
Random Effects ◽  
Repeated Measures ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Random Effects Model ◽  
Listwise Deletion ◽  
Type 1 Error ◽  
Item Level

The total score, which is calculated as the sum of scores in multiple items or questions, is repeatedly measured in longitudinal clinical studies. A mixed effects model for repeated measures method is often used to analyze these data; however, if one or more individual items are not measured, the method cannot be directly applied to the total score. We develop two simple and interpretable procedures that infer fixed effects for a longitudinal continuous composite variable. These procedures consider that the items that compose the total score are multivariate longitudinal continuous data and, simultaneously, handle subject-level and item-level missing data. One procedure is based on a multivariate marginalized random effects model with a multiple of Kronecker product covariance matrices for serial time dependence and correlation among items. The other procedure is based on a multiple imputation approach with a multivariate normal model. In terms of the type-1 error rate and the bias of treatment effect in total score, the marginalized random effects model and multiple imputation procedures performed better than the standard mixed effects model for repeated measures analysis with listwise deletion and single imputations for handling item-level missing data. In particular, the mixed effects model for repeated measures with listwise deletion resulted in substantial inflation of the type-1 error rate. The marginalized random effects model and multiple imputation methods provide for a more efficient analysis by fully utilizing the partially available data, compared to the mixed effects model for repeated measures method with listwise deletion.

Bayesian inference for two-part mixed-effects model using skew distributions, with application to longitudinal semicontinuous alcohol data

2015 ◽  
Vol 26 (4) ◽  
pp. 1838-1853 ◽  
Author(s):  
Dongyuan Xing ◽  
Yangxin Huang ◽  
Henian Chen ◽  
Yiliang Zhu ◽  
Getachew A Dagne ◽  
...  
Keyword(s):  
Random Effects ◽  
Repeated Measures ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Mixed Effects Models ◽  
Model Specification ◽  
Model Errors ◽  
Skew Distributions ◽  
Semicontinuous Data ◽  
Logistic Mixed Effects Model

Semicontinuous data featured with an excessive proportion of zeros and right-skewed continuous positive values arise frequently in practice. One example would be the substance abuse/dependence symptoms data for which a substantial proportion of subjects investigated may report zero. Two-part mixed-effects models have been developed to analyze repeated measures of semicontinuous data from longitudinal studies. In this paper, we propose a flexible two-part mixed-effects model with skew distributions for correlated semicontinuous alcohol data under the framework of a Bayesian approach. The proposed model specification consists of two mixed-effects models linked by the correlated random effects: (i) a model on the occurrence of positive values using a generalized logistic mixed-effects model (Part I); and (ii) a model on the intensity of positive values using a linear mixed-effects model where the model errors follow skew distributions including skew- t and skew-normal distributions (Part II). The proposed method is illustrated with an alcohol abuse/dependence symptoms data from a longitudinal observational study, and the analytic results are reported by comparing potential models under different random-effects structures. Simulation studies are conducted to assess the performance of the proposed models and method.

scholarly journals Testing for Main Random Effects in Two-Way Random and Mixed Effects Models: Modifying the Statistic

2013 ◽  
Vol 2013 ◽  
pp. 1-11
Author(s):  
Trent Gaugler ◽  
Michael G. Akritas
Keyword(s):  
Random Effects ◽  
Error Term ◽  
Random Effect ◽  
Test Procedure ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Mixed Effects Models ◽  
Random Effects Model ◽  
Mussel Watch ◽  
Unbalanced Designs

A procedure for testing the significance of the main random effect is proposed under a model which does not require the traditional assumptions of symmetry, homoscedasticity, and normality for the error term and random effects. To accommodate this level of model generality, and also unbalanced designs, suitable adjustments to theF-test are made. The extensive simulations performed under the random effects model, and the unrestricted and restricted versions of the mixed effects model, indicate that the classicalFprocedure is extremely liberal under heteroscedasticity and unbalancedness. The proposed test procedure performs well in all settings and is comparable to the classicalF-test when the classical assumptions are met. An analysis of a dataset from the Mussel Watch Project is presented.

Analysis of Data from a Controlled Repeated Measurements Design with Baseline-Dependent Dropouts

Methodology ◽  
2007 ◽  
Vol 3 (2) ◽  
pp. 58-66 ◽  
Author(s):  
John E. Overall ◽  
Scott Tonidandel
Keyword(s):  
Monte Carlo ◽  
Random Effects ◽  
Repeated Measurements ◽  
Random Effects Model ◽  
Two Stage ◽  
Linear Interaction ◽  
Rates Of Change ◽  
Type 1 Error ◽  
Stage Analysis

Abstract. Differences in mean rates of change are of primary interest in many controlled treatment evaluation studies. Generalized linear mixed model (GLMM) procedures are widely conceived to be the preferred method of analysis for repeated measurement designs when there are missing data due to dropouts, but systematic dependence of the dropout probabilities on antecedent or concurrent factors poses a problem for testing the significance of differences in mean rates of change across time in such designs. Controlling for the dependence of dropout probabilities on baseline values poses a special problem because a theoretically correct GLMM random-effects model does not permit including the same baseline score as both covariate and dependent variable. Monte Carlo methods are used herein to evaluate the actual Type 1 error rates and power resulting from two commonly-illustrated GLMM random-effects model formulations for testing the GROUPS × TIMES linear interaction effect in group-randomized repeated measurements designs. The two GLMM model formulations differ by either including or not including baseline scores as a covariate in the attempt to control for imbalance caused by the baseline-dependent dropouts. Results from those analyses are compared with results from a simpler two-stage analysis in which dropout-weighted slope coefficients fitted separately to the available repeated measurements for each subject serve as the dependent variable for an ordinary ANCOVA test for difference in mean rates of change. The Monte Carlo results confirm modestly superior Type 1 error protection but quite superior power for the simpler two-stage analysis of dropout-weighted slope coefficients as compared with those for either of the more mathematically complex GLMM analyses.

Conditionally Linear Mixed-Effects Models With Latent Variable Covariates

1999 ◽  
Vol 24 (3) ◽  
pp. 245-270 ◽  
Author(s):  
Shelley A. Blozis ◽  
Robert Cudeck
Keyword(s):  
Random Effects ◽  
Repeated Measures ◽  
Latent Variable ◽  
Missing At Random ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Variable Model ◽  
Nonlinear Mixed Effects Model ◽  
Linear Mixed Effects ◽  
Model Features

A version of the nonlinear mixed-effects model is presented that allows random effects only on the linear coefficients. Nonlinear parameters are not stochastic. In nonlinear regression, this kind of model has been called conditionally linear. As a mixed-effects model, this structure is more flexible than the popular linear mixed-effects model, while being nearly as straightforward to estimate. In addition to the structure for the repeated measures, a latent variable model ( Browne, 1993 ) is specified for a distinct set of covariates that are related to the random effects in the second level. Unbalanced data are allowed on the repeated measures, and data that are missing at random are allowed on the repeated measures or on the observed variables of the factor analysis sub-model. Features of the model are illustrated by two examples.

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 Improved Small Sample Inference Methods for a Mixed-Effects Model for Repeated Measures Approach in Incomplete Longitudinal Data Analysis

Stats ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 174-188
Author(s):  
Yoshifumi Ukyo ◽  
Hisashi Noma ◽  
Kazushi Maruo ◽  
Masahiko Gosho
Keyword(s):  
Clinical Trial ◽  
Repeated Measures ◽  
Type I Error ◽  
Mixed Effects ◽  
Error Rates ◽  
Small Sample ◽  
Mixed Effects Model ◽  
Model Complexity ◽  
Type I ◽  
Inference Methods

The mixed-effects model for repeated measures (MMRM) approach has been widely applied for longitudinal clinical trials. Many of the standard inference methods of MMRM could possibly lead to the inflation of type I error rates for the tests of treatment effect, when the longitudinal dataset is small and involves missing measurements. We propose two improved inference methods for the MMRM analyses, (1) the Bartlett correction with the adjustment term approximated by bootstrap, and (2) the Monte Carlo test using an estimated null distribution by bootstrap. These methods can be implemented regardless of model complexity and missing patterns via a unified computational framework. Through simulation studies, the proposed methods maintain the type I error rate properly, even for small and incomplete longitudinal clinical trial settings. Applications to a postnatal depression clinical trial are also presented.

Assessing the Effect of Mansionization on Suburban Single-Family House Sales Prices

2019 ◽  
pp. 0739456X1983315
Author(s):  
Suzanne Lanyi Charles
Keyword(s):  
Random Effects ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Single Family ◽  
Socioeconomic Characteristics ◽  
House Values ◽  
Crossed Random Effects ◽  
Family House

Using observed single-family house sales in the inner-ring suburbs of Chicago from 2010 through 2017, this paper uses a multilevel mixed-effects model with crossed random effects to estimate the effect that millennium mansions—new, large single-family houses—have on the sales prices of nearby single-family houses. Controlling for property, sale timing, and surrounding neighborhood socioeconomic characteristics, the study finds that mansionization is associated with an increase in the sales prices of neighboring houses. Long-term residents of a neighborhood undergoing mansionization should not fear a decrease in their house values; however, decreases in neighborhood affordability may result in exclusionary displacement.

Effects of Quercetin Supplementation on Endurance Performance and Maximal Oxygen Consumption: A Meta-Analysis

2013 ◽  
Vol 23 (1) ◽  
pp. 73-82 ◽  
Author(s):  
Denis M. Pelletier ◽  
Guillaume Lacerte ◽  
Eric D.B. Goulet
Keyword(s):  
Oxygen Consumption ◽  
Random Effects ◽  
Meta Analysis ◽  
Maximal Oxygen Consumption ◽  
Daily Intake ◽  
Endurance Performance ◽  
Mixed Effects ◽  
Random Effects Model ◽  
Weighted Mean ◽  
Meta Regression

Lately, the effect of quercetin supplementation (QS) on endurance performance (EP) and maximal oxygen consumption (VO2max) has been receiving much scientific and media attention. Therefore, a meta-analysis was performed to determine QS’s ergogenic value on these variables. Studies were located with database searches (PubMed and SPORTDiscus) and cross-referencing. Outcomes represent mean percentage changes in EP (measured via power output) and VO2max between QS and placebo. Random-effects model meta-regression, mixed-effects model analog to the ANOVA, random-effects weighted mean effect summary, and magnitudebased inferences analyses were used to delineate the effects of QS. Seven research articles (representing 288 subjects) were included, producing 4 VO2max and 10 EP effect estimates. Mean QS daily intake and duration were, respectively, 960 ± 127 mg and 26 ± 24 d for the EP outcome and 1,000 ± 0 mg and 8 ± 23 d for the VO2max outcome. EP was assessed during exercise with a mean duration of 79 ± 82 min. Overall, QS improved EP by 0.74% (95% CI: 0.10–1.39, p = .02) compared with placebo. However, only in untrained individuals (0.83% ± 0.78%, p = .02) did QS significantly improve EP (trained individuals: 0.09% ± 2.15%, p = .92). There was no relationship between QS duration and EP (p = .69). Overall, QS increased VO2max by 1.94% (95% CI: 0.30–3.59, p = .02). Magnitude-based inferences suggest that the effect of QS on EP and VO2max is likely to be trivial for both trained and untrained individuals. In conclusion, this meta-analysis indicates that QS is unlikely to prove ergogenic for aerobic-oriented exercises in trained and untrained individuals.

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