scholarly journals Assessing Direct Marketing Effectiveness at a Retailer’s: a Mixed-Effects Model of Heterogeneous Responsiveness to Personalized Promotions

2021 ◽  
pp. 21-34
Author(s):  
Roko Pedisic
Keyword(s):  
Direct Marketing ◽  
Regression Tree ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Linear Modeling ◽  
Annual Household Income ◽  
Random Coefficient Models ◽  
Modeling Framework ◽  
Interaction Terms ◽  
One Year

This study proposed a mixed linear modeling framework for revealing the average treatment effects of coupon promotions as well as for figuring out what kind of customers are more responsive to coupon promotions. The analysis used detailed data on 2,469 households who are frequent shoppers at a retailer over one year. A series of models starting from fixed parameters OLS regression to more flexible random coefficient models were built. The results showed that receiving more coupon promotions does not lead to higher spending among households. None of the available demographic characteristics was significantly linearly related to the responsiveness to coupon promotions according the initial model with cross-level interaction terms. However, exploratory analysis of random slopes with the help of a regression tree allowed to correct the specification, which allowed confirming that people aged 25+ with annual household income exceeding $25,000 are more responsive, yet their response is insignificantly different from zero. Keywords: Mixed-Effects Model, Multilevel Analysis, Heterogeneous Responsiveness, Promotions.

scholarly journals A joint modeling approach for longitudinal microbiome data improves ability to detect microbiome associations with disease

2020 ◽  
Vol 16 (12) ◽  
pp. e1008473
Author(s):  
Pamela N. Luna ◽  
Jonathan M. Mansbach ◽  
Chad A. Shaw
Keyword(s):  
Joint Modeling ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Time Dependent ◽  
Time To Event ◽  
Modeling Framework ◽  
Microbial Composition ◽  
Disease Outcomes ◽  
Microbiome Data ◽  
Over Time

Changes in the composition of the microbiome over time are associated with myriad human illnesses. Unfortunately, the lack of analytic techniques has hindered researchers’ ability to quantify the association between longitudinal microbial composition and time-to-event outcomes. Prior methodological work developed the joint model for longitudinal and time-to-event data to incorporate time-dependent biomarker covariates into the hazard regression approach to disease outcomes. The original implementation of this joint modeling approach employed a linear mixed effects model to represent the time-dependent covariates. However, when the distribution of the time-dependent covariate is non-Gaussian, as is the case with microbial abundances, researchers require different statistical methodology. We present a joint modeling framework that uses a negative binomial mixed effects model to determine longitudinal taxon abundances. We incorporate these modeled microbial abundances into a hazard function with a parameterization that not only accounts for the proportional nature of microbiome data, but also generates biologically interpretable results. Herein we demonstrate the performance improvements of our approach over existing alternatives via simulation as well as a previously published longitudinal dataset studying the microbiome during pregnancy. The results demonstrate that our joint modeling framework for longitudinal microbiome count data provides a powerful methodology to uncover associations between changes in microbial abundances over time and the onset of disease. This method offers the potential to equip researchers with a deeper understanding of the associations between longitudinal microbial composition changes and disease outcomes. This new approach could potentially lead to new diagnostic biomarkers or inform clinical interventions to help prevent or treat disease.

scholarly journals Estimating the Evolution of Disease in the Parkinson’s Progression Markers Initiative

2018 ◽  
Vol 18 (4) ◽  
pp. 173-190 ◽  
Author(s):  
Samuel Iddi ◽  
Dan Li ◽  
Paul S. Aisen ◽  
Michael S. Rafii ◽  
Irene Litvan ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Diagnostic Category ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Modeling Framework ◽  
Specific Binding Ratio ◽  
Latent Time ◽  
Progression Markers

Parkinson’s disease is the second most common neurological disease and affects about 1% of persons over the age of 60 years. Due to the lack of approved surrogate markers, confirmation of the disease still requires postmortem examination. Identifying and validating biomarkers are essential steps toward improving clinical diagnosis and accelerating the search for therapeutic drugs to ameliorate disease symptoms. Until recently, statistical analysis of multicohort longitudinal studies of neurodegenerative diseases has usually been restricted to a single analysis per outcome with simple comparisons between diagnostic groups. However, an important methodological consideration is to allow the modeling framework to handle multiple outcomes simultaneously and consider the transitions between diagnostic groups. This enables researchers to monitor multiple trajectories, correctly account for the correlation among biomarkers, and assess how these associations may jointly change over the long-term course of disease. In this study, we apply a latent time joint mixed-effects model to study biomarker progression and disease dynamics in the Parkinson’s Progression Markers Initiative (PPMI) and examine which markers might be most informative in the earliest phases of disease. The results reveal that, even though diagnostic category was not included in the model, it seems to accurately reflect the temporal ordering of the disease state consistent with diagnosis categorization at baseline. In addition, results indicated that the specific binding ratio on striatum and the total Unified Parkinson’s Disease Rating Scale (UPDRS) show high discriminability between disease stages. An extended latent time joint mixed-effects model with heterogeneous latent time variance also showed improvement in model fit in a simulation study and when applied to real data.

Dimension reduction and mixed-effects model for microarray meta-analysis of cancer

10.2741/2878 ◽  
2008 ◽  
Vol 13 (13) ◽  
pp. 2714 ◽  
Author(s):  
Tianwei Yu
Keyword(s):  
Dimension Reduction ◽  
Meta Analysis ◽  
Mixed Effects ◽  
Mixed Effects Model

scholarly journals A flexible nonlinear mixed effects model for HIV viral load rebound after treatment interruption

2020 ◽  
Vol 39 (15) ◽  
pp. 2051-2066 ◽  
Author(s):  
Rui Wang ◽  
Ante Bing ◽  
Cathy Wang ◽  
Yuchen Hu ◽  
Ronald J. Bosch ◽  
...  
Keyword(s):  
Viral Load ◽  
Treatment Interruption ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Nonlinear Mixed Effects ◽  
Hiv Viral Load ◽  
Nonlinear Mixed Effects Model ◽  
Viral Load Rebound

scholarly journals The state of Spanish /s/ variation in Concepción, Chile: Linguistic and social trends

2020 ◽  
Vol 6 (1) ◽  
pp. 132-153
Author(s):  
Brandon M. A. Rogers
Keyword(s):  
Social Factors ◽  
Central City ◽  
Mixed Effects ◽  
The State ◽  
Mixed Effects Model ◽  
Proportional Odds ◽  
Random Factor ◽  
Social Trends ◽  
Southern Central ◽  
Social Demographics

AbstractThe current study examines /s/ variation in the southern-central city of Concepción, Chile and its relation to a variety of linguistic and social factors. A proportional-odds mixed effects model, with the random factor of “speaker”, was used to treat the categorically coded data on a continuum of acoustical variation ([s] > [h] > ∅). The results presented show that contrary to the previous assertions, heavy sibilant reduction, especially elision, in Concepción, Chile is the rule, rather than the exception, to the extent that it is no longer a marker of certain social demographics as has been reported previously. Furthermore, based on the trends reported, it is likely that this has been the case for several decades. Finally, the overall observed trends are indicative that the rates of /s/ elision will continue to increase across social demographics and different phonetic and phonological contexts in Concepción, Chile.

Estimating local arterial stiffness using mixed-effects model-based residuals: a novel approach

2021 ◽  
Author(s):  
Avinash Chandran ◽  
Derek W. Brown ◽  
Gabriel H. Zieff ◽  
Zachary Y. Kerr ◽  
Daniel Credeur ◽  
...  
Keyword(s):  
Arterial Stiffness ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Model Based ◽  
Novel Approach

An autoregressive linear mixed effects model for the analysis of longitudinal data which include dropouts and show profiles approaching asymptotes

2008 ◽  
Vol 27 (30) ◽  
pp. 6351-6366 ◽  
Author(s):  
Takashi Funatogawa ◽  
Ikuko Funatogawa ◽  
Masahiro Takeuchi
Keyword(s):  
Longitudinal Data ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Linear Mixed Effects Model ◽  
Linear Mixed Effects

Mixed effects model on driver steering behavior under emergency

2011 ◽  
Author(s):  
Chen Xue-mei ◽  
Wei Zhong-hua ◽  
Gao Li ◽  
Zhang Zhenghua
Keyword(s):  
Mixed Effects ◽  
Mixed Effects Model
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