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.

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.

Testing the Significance of Difference in Average Rates of Change in Controlled Longitudinal Studies With High Dropout Rates

Methodology ◽  
2009 ◽  
Vol 5 (2) ◽  
pp. 46-54 ◽  
Author(s):  
John E. Overall ◽  
Scott Tonidandel ◽  
Joy M. Schmitz
Keyword(s):  
Missing Data ◽  
Repeated Measures ◽  
Repeated Measurements ◽  
Dropout Rates ◽  
Sample Sizes ◽  
Error Protection ◽  
Limited Sample ◽  
Rates Of Change ◽  
Type 1 Error

This article concerns methodology for testing the significance of differences in mean rates of change in controlled repeated measurements designs with limited sample sizes, autoregressive error structures, nonlinear patterns of underlying true mean change, dropout rates exceeding 50%, plus other missing data. Each of these is problematic for ordinary repeated measures analysis of variance, and a complex generalized linear mixed model formulation popularly advocated for the ability to deal with autoregressive error structures and missing data is shown to perform poorly in such circumstances. Monte Carlo simulation methods confirm that simple two-stage analyses of dropout-weighted linear slope coefficients provide conservative Type 1 error protection, although adequate power requires the presence of large treatment effects in studies with the limited sample sizes and high proportions of missing data. No other analysis has been documented to provide both conservative Type 1 error protection and competitive power under similarly taxing conditions.

Genetic random effects model for family data with long-term survivors: analysis of diabetic nephropathy in type 1 diabetes

2007 ◽  
Vol 31 (7) ◽  
pp. 697-708 ◽  
Author(s):  
Janne Pitkäniemi ◽  
Elena Moltchanova ◽  
Laura Haapala ◽  
Valma Harjutsalo ◽  
Jaakko Tuomilehto ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Diabetic Nephropathy ◽  
Random Effects ◽  
Family Data ◽  
Random Effects Model ◽  
Long Term Survivors

scholarly journals A Bayesian spatial random effects model characterisation of tumour heterogeneity implemented using Markov chain Monte Carlo (MCMC) simulation

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2082 ◽  
Author(s):  
Martin D. King ◽  
Matthew Grech-Sollars
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Statistical Model ◽  
Spatial Heterogeneity ◽  
Random Effects ◽  
Random Effects Model ◽  
Tumour Heterogeneity ◽  
Spatial Random Effects ◽  
Mcmc Simulation

The focus of this study is the development of a statistical modelling procedure for characterising intra-tumour heterogeneity, motivated by recent clinical literature indicating that a variety of tumours exhibit a considerable degree of genetic spatial variability. A formal spatial statistical model has been developed and used to characterise the structural heterogeneity of a number of supratentorial primitive neuroectodermal tumours (PNETs), based on diffusion-weighted magnetic resonance imaging. Particular attention is paid to the spatial dependence of diffusion close to the tumour boundary, in order to determine whether the data provide statistical evidence to support the proposition that water diffusivity in the boundary region of some tumours exhibits a deterministic dependence on distance from the boundary, in excess of an underlying random 2D spatial heterogeneity in diffusion. Tumour spatial heterogeneity measures were derived from the diffusion parameter estimates obtained using a Bayesian spatial random effects model. The analyses were implemented using Markov chain Monte Carlo (MCMC) simulation. Posterior predictive simulation was used to assess the adequacy of the statistical model. The main observations are that the previously reported relationship between diffusion and boundary proximity remains observable and achieves statistical significance after adjusting for an underlying random 2D spatial heterogeneity in the diffusion model parameters. A comparison of the magnitude of the boundary-distance effect with the underlying random 2D boundary heterogeneity suggests that both are important sources of variation in the vicinity of the boundary. No consistent pattern emerges from a comparison of the boundary and core spatial heterogeneity, with no indication of a consistently greater level of heterogeneity in one region compared with the other. The results raise the possibility that DWI might provide a surrogate marker of intra-tumour genetic regional heterogeneity, which would provide a powerful tool with applications in both patient management and in cancer research.

scholarly journals Better together? Regression analysis of complex survey data after ex-post harmonization

2020 ◽  
Author(s):  
Anna-Carolina Haensch ◽  
Bernd Weiß
Keyword(s):  
Regression Analysis ◽  
Survey Data ◽  
Random Effects ◽  
Two Stage ◽  
Complex Sampling ◽  
One Stage ◽  
Ex Post ◽  
Study Heterogeneity ◽  
Research Questions ◽  
Stage Analysis

An increasing number of researchers pool, harmonize, and analyze survey data from different survey providers for their research questions. They aim to study heterogeneity between groups over a long period or examine smaller subgroups; research questions that can be impossible to answer with a single survey. This combination or pooling of data is known as individual person data (IPD) meta-analysis in medicine and psychology; in sociology, it is understood as part of ex-post survey harmonization (Granda et al 2010).However, in medicine or psychology, most original studies focus on treatment or intervention effect and apply experimental research designs to come to causal conclusions. In contrast, many sociological or economic studies are nonexperimental. In comparison to experimental data, survey-based data is subject to complex sampling and nonresponse. Ignoring the complex sampling design can lead to biased population inferences not only in population means and shares but also in regression coefficients, widely used in the social sciences (DuMouchel and Duncan 1983 and Solon et al. 2013). To account for complex sampling schemes or non-ignorable unit nonresponse, survey-based data often comes with survey weights. But how to use survey weights after pooling different surveys?We will build upon the work done by DuMouchel and Duncan (1983) and Solon et al. (2013) for survey-weighted regression analysis with a single data set. Through Monte Carlo (MC) simulations, we will show that endogenous sampling and heterogeneity of effects models require survey weighting to receive approximately unbiased estimates after ex-post survey harmonization. Second, we focus on a list of methodological questions: Do survey-weighted one-stage and two-stage (meta-)analytical approaches perform differently? Is it possible to include random effects, especially if we have to assume study heterogeneity? Another challenging methodological question is the inclusion of random effects in a one-stage analysis.Our simulations show that two-stage analysis will be biased if the weights' variation is high, whereas one-stage analysis remains unbiased. We also show that the inclusion of random effects in a one-stage analysis is challenging but doable, i.e., weights must be transformed in most cases. Apart from the MC simulations, we also show the difference between two-stage and one-stage approaches with real-world data from same-sex couples in Germany.

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