Small sample GEE estimation of regression parameters for longitudinal data

2014 ◽  
Vol 33 (22) ◽  
pp. 3869-3881 ◽  
Author(s):  
Sudhir Paul ◽  
Xuemao Zhang
Keyword(s):  
Longitudinal Data ◽  
Small Sample ◽  
Regression Parameters

scholarly journals EM algorithm for longitudinal data with non-ignorable missing values: An application to health data

2016 ◽  
Vol 27 (2) ◽  
pp. 133-142
Author(s):  
Radia Taisir ◽  
M Ataharul Islam
Keyword(s):  
Longitudinal Data ◽  
Em Algorithm ◽  
Expectation Maximization ◽  
Missing Values ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Binary Response ◽  
Complex Method ◽  
Missing Responses ◽  
Regression Parameters

Longitudinal studies involves repeated observations over time on the same experimental units and missingness may occur in non-ignorable fashion. For such longitudinal missing data, a Markov model may be used to model the binary response along with a suitable non-response model for the missing portion of the data. It is of the primary interest to estimate the effects of covariates on the binary response. Similar model for such incomplete longitudinal data exists where estimation of the regression parameters are obtained using likelihood method by summing over all possible values of the missing responses. In this paper, we propose an expectation-maximization (EM) algorithm technique for the estimation of the regression parameters which is computationally simple and produces similar efficient estimates as compared to the existing complex method of estimation. A comparison of the existing and the proposed estimation methods has been made by analyzing the Health and Retirement Survey (HRS) data of United States.Bangladesh J. Sci. Res. 27(2): 133-142, December-2014

scholarly journals A Longitudinal Normative Dataset and Protocol for Speech and Language Biomarker Research

2021 ◽  
Author(s):  
James W. Schwoebel ◽  
Joel Schwartz ◽  
Lindsay Warrenburg ◽  
Roland Brown ◽  
Ashi Awasthi ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Small Sample ◽  
High Fidelity ◽  
Sample Sizes ◽  
Speech And Language ◽  
Clinical Literature ◽  
Small Sample Sizes ◽  
Biomarker Research ◽  
Survey Protocol ◽  
Research Studies

Although speech and language biomarker (SLB) research studies have shown methodological and clinical promise, some common limitations of these studies include small sample sizes, limited longitudinal data, and a lack of a standardized survey protocol. Here, we introduce the Voiceome Protocol and the corresponding Voiceome Dataset as standards which can be utilized and adapted by other SLB researchers. The Voiceome Protocol includes 12 types of voice tasks, along with health and demographic questions that have been shown to affect speech. The longitudinal Voiceome Dataset consisted of the Voiceome Protocol survey taken on (up to) four occasions, each separated by roughly three weeks (22.80 +/- 20.91 days). Of 6,650 total participants, 1,382 completed at least two Voiceome surveys. The results of the Voiceome Dataset are largely consistent with results from standard clinical literature, suggesting that the Voiceome Study is a high-fidelity, normative dataset and scalable protocol that can be used to advance SLB research.

Using Longitudinal Data to Model Teachers' Ratings of Classroom Behavior as a Dynamic Process

1981 ◽  
Vol 6 (3) ◽  
pp. 237-255 ◽  
Author(s):  
Ian Plewis
Keyword(s):  
Measurement Error ◽  
Longitudinal Data ◽  
Dynamic Process ◽  
Categorical Data ◽  
Continuous Time ◽  
Classroom Behavior ◽  
Markov Models ◽  
Time Measurement ◽  
Small Sample ◽  
Educational Implications

Simple Markov models are fitted to a small sample of longitudinal categorical data of teachers' ratings of children's classroom behavior. Although the data consist only of observations at 5 occasions, it was possible, after dividing the data into two groups, to fit plausible models in continuous time. Measurement error and alternative longitudinal designs are discussed, and some possible educational implications are noted.

Reliability of Regression-Corrected Climate Forecasts

2014 ◽  
Vol 27 (9) ◽  
pp. 3393-3404 ◽  
Author(s):  
Michael K. Tippett ◽  
Timothy DelSole ◽  
Anthony G. Barnston
Keyword(s):  
Sample Size ◽  
Climate Model ◽  
Sampling Error ◽  
Small Sample Size ◽  
Small Sample ◽  
Climate Forecast ◽  
Regression Parameters ◽  
Climate Forecasts ◽  
Probability Forecasts ◽  
The Impact

Abstract Regression is often used to calibrate climate model forecasts with observations. Reliability is an aspect of forecast quality that refers to the degree of correspondence between forecast probabilities and observed frequencies of occurrence. While regression-corrected climate forecasts are reliable in principle, the estimated regression parameters used in practice are affected by sampling error. The low skill and small sample sizes typically encountered in climate prediction imply substantial sampling error in the estimated regression parameters. Here the reliability of regression-corrected climate forecasts is analyzed for the case of joint-Gaussian distributed ensemble forecasts and observations with regression parameters estimated by least squares. Hypothesis testing of the regression parameters provides direct information about the skill and reliability of the uncorrected ensemble-based probability forecasts. However, the regression-corrected probability forecasts with estimated parameters are systematically “overconfident” because sampling error causes a positive bias in the regression forecast signal variance, despite the fact that the estimates of the regression parameters are themselves unbiased. An analytical description of the reliability diagram of a generic regression-corrected climate forecast is derived and is shown to depend on sample size and population correlation skill, with small sample size and low skill being factors that increase overconfidence. The analytical reliability estimate is shown to capture the effect of sampling error in synthetic data experiments and in a 29-yr dataset of NOAA Climate Forecast System version 2 predictions of seasonal precipitation totals over the Americas. The impact of sampling error on the reliability of regression-corrected forecast has been previously unrecognized and affects all regression-based forecasts. The use of regression parameters estimated by shrinkage methods such as ridge regression substantially reduces overconfidence.

scholarly journals A Robust Statistical method to Estimate the Intervention Effect with Longitudinal Data

2020 ◽  
Vol 8 (1) ◽  
pp. 318-327
Author(s):  
Mohammad M Islam ◽  
Erik L Heiny
Keyword(s):  
Longitudinal Data ◽  
Statistical Method ◽  
Small Sample ◽  
Sampling Distribution ◽  
Outcome Variable ◽  
Policy Intervention ◽  
Segmented Regression ◽  
Nonparametric Bootstrap ◽  
Intervention Effect ◽  
Robust Statistical Method

Segmented regression is a standard statistical procedure used to estimate the effect of a policy intervention on time series outcomes. This statistical method assumes the normality of the outcome variable, a large sample size, no autocorrelation in the observations, and a linear trend over time. Also, segmented regression is very sensitive to outliers. In a small sample study, if the outcome variable does not follow a Gaussian distribution, then using segmented regression to estimate the intervention effect leads to incorrect inferences. To address the small sample problem and non-normality in the outcome variable, including outliers, we describe and develop a robust statistical method to estimate the policy intervention effect in a series of longitudinal data. A simulation study is conducted to demonstrate the effect of outliers and non-normality in the outcomes by calculating the power of the test statistics with the segmented regression and the proposed robust statistical methods. Moreover, since finding the sampling distribution of the proposed robust statistic is analytically difficult, we use a nonparametric bootstrap technique to study the properties of the sampling distribution and make statistical inferences. Simulation studies show that the proposed method has more power than the standard t-test used in segmented regression analysis under the non-normality error distribution. Finally, we use the developed technique to estimate the intervention effect of the Istanbul Declaration on illegal organ activities. The robust method detected more significant effects compared to the standard method and provided shorter confidence intervals.

Small-Sample Inference for Incomplete Longitudinal Data with Truncation and Censoring in Tumor Xenograft Models

Biometrics ◽  
2002 ◽  
Vol 58 (3) ◽  
pp. 612-620 ◽  
Author(s):  
Ming Tan ◽  
Hong-Bin Fang ◽  
Guo-Liang Tian ◽  
Peter J. Houghton
Keyword(s):  
Longitudinal Data ◽  
Small Sample ◽  
Tumor Xenograft ◽  
Xenograft Models

Bayesian analysis of longitudinal data using growth curve models

2007 ◽  
Vol 31 (4) ◽  
pp. 374-383 ◽  
Author(s):  
Zhiyong Zhang ◽  
Fumiaki Hamagami ◽  
Lijuan Lijuan Wang ◽  
John R. Nesselroade ◽  
Kevin J. Grimm
Keyword(s):  
Longitudinal Data ◽  
Growth Curve ◽  
Bayesian Methods ◽  
Prior Information ◽  
Likelihood Estimation ◽  
Small Sample ◽  
Growth Curve Models ◽  
Achievement Data ◽  
Sample Data ◽  
Complex Models

Bayesian methods for analyzing longitudinal data in social and behavioral research are recommended for their ability to incorporate prior information in estimating simple and complex models. We first summarize the basics of Bayesian methods before presenting an empirical example in which we fit a latent basis growth curve model to achievement data from the National Longitudinal Survey of Youth. This step-by-step example illustrates how to analyze data using both noninformative and informative priors. The results show that in addition to being an alternative to the maximum likelihood estimation (MLE) method, Bayesian methods also have unique strengths, such as the systematic incorporation of prior information from previous studies. These methods are more plausible ways to analyze small sample data compared with the MLE method.

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