Consistency of effects in multilevel models for single-case data

2019 ◽  
Author(s):  
Rumen Manolov ◽  
John M. Ferron
Keyword(s):  
Confidence Interval ◽  
Multilevel Models ◽  
Linear Models ◽  
Single Case ◽  
Real Data ◽  
Fixed Effect ◽  
Individual Treatment ◽  
General Context ◽  
Effect Estimate ◽  
Baseline Design

In the context of single-case experimental designs, replication is crucial. On the one hand, the replication of the basic effect within a study is necessary for demonstrating experimental control. On the other hand, replication across studies is required for establishing the generality of the intervention effect. Moreover, the “replicability crisis” presents a more general context further emphasizing the need for assessing consistency in replications. In the current text, we focus on replication of effects within a study and we specifically discuss the consistency of effects. Our proposal for assessing the consistency of effects refers to one of the promising data analytical techniques: multilevel models, also known as hierarchical linear models or mixed effects models. One option is to check, for each case in a multiple-baseline design, whether the confidence interval for the individual treatment effect excludes zero. This is relevant for assessing whether the effect is replicated as being non-null. However, we consider that it is more relevant and informative to assess, for each case, whether the confidence interval for the random effects includes zero (i.e., whether the fixed effect estimate is a plausible value for each individual effect). This is relevant for assessing whether the effect is consistent in size, with the additional requirement that the fixed effect itself is different from zero. The proposal for assessing consistency is illustrated with real data and it is implemented in free user-friendly software.

Justifying the data analytical choice in single case research in relation to the expected data pattern

2019 ◽  
Author(s):  
Rumen Manolov
Keyword(s):  
Data Analysis ◽  
Visual Analysis ◽  
Multilevel Models ◽  
Single Case ◽  
Analytical Techniques ◽  
Real Data ◽  
Small Scale ◽  
Data User ◽  
Single Case Research ◽  
User Friendly

The lack of consensus regarding the most appropriate analytical techniques for single-case experimental designs data requires justifying the choice of any specific analytical option. The current text mentions some of the arguments, provided by methodologists and statisticians, in favor of several analytical techniques. Additionally, a small-scale literature review is performed in order to explore if and how applied researchers justify the analytical choices that they make. The review suggests that certain practices are not sufficiently explained. In order to improve the reporting regarding the data analytical decisions, it is proposed to choose and justify the data analytical approach prior to gathering the data. As a possible justification for data analysis plan, we propose using as a basis the expected the data pattern (specifically, the expectation about an improving baseline trend and about the immediate or progressive nature of the intervention effect). Although there are multiple alternatives for single-case data analysis, the current text focuses on visual analysis and multilevel models and illustrates an application of these analytical options with real data. User-friendly software is also developed.

A Proposal for the Assessment of Replication of Effects in Single-Case Experimental Designs

2021 ◽  
Author(s):  
Rumen Manolov ◽  
René Tanious ◽  
Belén Fernández
Keyword(s):  
Graphical Representation ◽  
Single Case ◽  
Real Data ◽  
Experimental Designs ◽  
General Context ◽  
Behavioral Sciences ◽  
Intervention Effect ◽  
Definition Of ◽  
User Friendly ◽  
Statistical Criteria

In science in general and, therefore, in the context of single-case experimental designs (SCED), the replication of the effects of the intervention within and across participants is crucial for establishing causality and also for assessing the generality of the intervention effect. Specific developments and proposals for assessing whether an effect has been replicated or not (or to what extent) are scarce, in the general context of behavioral sciences, and practically null in the SCED context. We propose an extension of the modified Brinley plot for assessing how many of the effects replicate. In order to make this assessment possible, a definition of replication is suggested, on the basis of expert judgment, rather than on purely statistical criteria. The definition of replication and its graphical representation are justified, presenting their strengths and limitations, and illustrated with real data. A user-friendly software is made available for obtaining automatically the graphical representation.

scholarly journals A comparative study of estimators in multilevel linear models

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259960
Author(s):  
Sabz Ali ◽  
Said Ali Shah ◽  
Seema Zubair ◽  
Sundas Hussain
Keyword(s):  
Regression Models ◽  
Multilevel Models ◽  
Linear Models ◽  
Real Data ◽  
Standard Errors ◽  
Minimum Norm ◽  
Linear Regression Models ◽  
Organizational Research ◽  
Data Application ◽  
Multilevel Linear Models

Multilevel Models are widely used in organizational research, educational research, epidemiology, psychology, biology and medical fields. In this paper, we recommend the situations where Bootstrap procedures through Minimum Norm Quadratic Unbiased Estimator (MINQUE) can be extremely handy than that of Restricted Maximum Likelihood (REML) in multilevel level linear regression models. In our simulation study the bootstrap by means of MINQUE is superior to REML in conditions where normality does not hold. Moreover, the real data application also supports our findings in terms of accuracy of estimates and their standard errors.

scholarly journals Assessing consistency of effects when applying multilevel models to single-case data

2020 ◽  
Vol 52 (6) ◽  
pp. 2460-2479
Author(s):  
Rumen Manolov ◽  
John M. Ferron
Keyword(s):  
Multilevel Models ◽  
Single Case ◽  
Case Data

scholarly journals Bias in two-sample Mendelian randomization when using heritable covariable-adjusted summary associations

2021 ◽  
Author(s):  
Fernando Pires Hartwig ◽  
Kate Tilling ◽  
George Davey Smith ◽  
Deborah A Lawlor ◽  
Maria Carolina Borges
Keyword(s):  
Waist Circumference ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Real Data ◽  
Sensitivity Analyses ◽  
Effect Estimate ◽  
Genome Wide Association Studies ◽  
Residual Confounding

Abstract Background Two-sample Mendelian randomization (MR) allows the use of freely accessible summary association results from genome-wide association studies (GWAS) to estimate causal effects of modifiable exposures on outcomes. Some GWAS adjust for heritable covariables in an attempt to estimate direct effects of genetic variants on the trait of interest. One, both or neither of the exposure GWAS and outcome GWAS may have been adjusted for covariables. Methods We performed a simulation study comprising different scenarios that could motivate covariable adjustment in a GWAS and analysed real data to assess the influence of using covariable-adjusted summary association results in two-sample MR. Results In the absence of residual confounding between exposure and covariable, between exposure and outcome, and between covariable and outcome, using covariable-adjusted summary associations for two-sample MR eliminated bias due to horizontal pleiotropy. However, covariable adjustment led to bias in the presence of residual confounding (especially between the covariable and the outcome), even in the absence of horizontal pleiotropy (when the genetic variants would be valid instruments without covariable adjustment). In an analysis using real data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank, the causal effect estimate of waist circumference on blood pressure changed direction upon adjustment of waist circumference for body mass index. Conclusions Our findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. When that is not possible, careful consideration of the causal relationships underlying the data (including potentially unmeasured confounders) is required to direct sensitivity analyses and interpret results with appropriate caution.

A Parent-Implemented Shared Reading Intervention via Telepractice

2021 ◽  
pp. 105381512110322
Author(s):  
Yusuf Akemoglu ◽  
Vanessa Hinton ◽  
Dayna Laroue ◽  
Vanessa Jefferson
Keyword(s):  
Time Delay ◽  
Single Case ◽  
Multiple Baseline ◽  
Video Editing ◽  
Baseline Design ◽  
Communicative Acts ◽  
Use Of Time ◽  
Young Children With Disabilities ◽  
Evidenced Based ◽  
Feedback Delivery

We describe a study of the internet-based Parent-Implemented Communication Strategies–Storybook (i-PiCSS), an intervention designed to train and coach parents to use evidenced-based naturalistic communication teaching (NCT) strategies (i.e., modeling, mand-model, and time delay) and RTs while reading storybooks with their young children with disabilities. Three participating parents were trained and coached via telepractice technologies (videoconferences, video editing software). Zoom software was used for videoconferencing and Camtasia software was used to record the training and coaching sessions and to edit the recorded session for feedback delivery purposes. Using a single-case multiple-baseline design across NCT strategies within each family, we examined (a) parents’ fidelity use of the three NCT strategies, (b) parents’ use of book RTs, and (c) child language and communication outcomes. The entire intervention period lasted 8 weeks. After training and coaching, parents used the modeling, mand-model, and time delay strategies with higher rates and higher quality (accuracy). Children initiated more communicative acts upon parents’ use of time delay and increased their numbers of single- and multiple-word responses.

scholarly journals Identification of Health Expenditures Determinants: A Model to Manage the Economic Burden of Cardiovascular Disease

2021 ◽  
Vol 18 (9) ◽  
pp. 4652
Author(s):  
Fiorella Pia Salvatore ◽  
Alessia Spada ◽  
Francesca Fortunato ◽  
Demetris Vrontis ◽  
Mariantonietta Fiore
Keyword(s):  
Cardiovascular Disease ◽  
Hospital Discharge ◽  
Random Effects ◽  
Economic Burden ◽  
Linear Models ◽  
Health Management ◽  
Model Performance ◽  
Real Data ◽  
Large Set ◽  
Regional Health

The purpose of this paper is to investigate the determinants influencing the costs of cardiovascular disease in the regional health service in Italy’s Apulia region from 2014 to 2016. Data for patients with acute myocardial infarction (AMI), heart failure (HF), and atrial fibrillation (AF) were collected from the hospital discharge registry. Generalized linear models (GLM), and generalized linear mixed models (GLMM) were used to identify the role of random effects in improving the model performance. The study was based on socio-demographic variables and disease-specific variables (diagnosis-related group, hospitalization type, hospital stay, surgery, and economic burden of the hospital discharge form). Firstly, both models indicated an increase in health costs in 2016, and lower spending values for women (p < 0.001) were shown. GLMM indicates a significant increase in health expenditure with increasing age (p < 0.001). Day-hospital has the lowest cost, surgery increases the cost, and AMI is the most expensive pathology, contrary to AF (p < 0.001). Secondly, AIC and BIC assume the lowest values for the GLMM model, indicating the random effects’ relevance in improving the model performance. This study is the first that considers real data to estimate the economic burden of CVD from the regional health service’s perspective. It appears significant for its ability to provide a large set of estimates of the economic burden of CVD, providing information to managers for health management and planning.

scholarly journals Predicting Stock Market Index Using Hybrid Intelligence Model

2018 ◽  
Vol 7 (3.15) ◽  
pp. 36 ◽  
Author(s):  
Sarah Nadirah Mohd Johari ◽  
Fairuz Husna Muhamad Farid ◽  
Nur Afifah Enara Binti Nasrudin ◽  
Nur Sarah Liyana Bistamam ◽  
Nur Syamira Syamimi Muhammad Shuhaili
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Linear Models ◽  
Forecast Error ◽  
Arima Model ◽  
Real Data ◽  
Stock Index ◽  
Support Vector ◽  
Ann Model ◽  
Stock Market Index

Predicting financial market changes is an important issue in time series analysis, receiving an increasing attention due to financial crisis. Autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting but ARIMA model cannot capture nonlinear patterns easily. Generalized autoregressive conditional heteroscedasticity (GARCH) model applied understanding of volatility depending to the estimation of previous forecast error and current volatility, improving ARIMA model. Support vector machine (SVM) and artificial neural network (ANN) have been successfully applied in solving nonlinear regression estimation problems. This study proposes hybrid methodology that exploits unique strength of GARCH + SVM model, and GARCH + ANN model in forecasting stock index. Real data sets of stock prices FTSE Bursa Malaysia KLCI were used to examine the forecasting accuracy of the proposed model. The results shows that the proposed hybrid model achieves best forecasting compared to other model.  

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