scholarly journals The analysis of very small samples of repeated measurements I: An adjusted sandwich estimator

2010 ◽  
Vol 29 (27) ◽  
pp. 2825-2837 ◽  
Author(s):  
Simon S. Skene ◽  
Michael G. Kenward
Keyword(s):  
Repeated Measurements ◽  
Small Samples ◽  
Sandwich Estimator

scholarly journals Dyslexia treatment studies: A systematic review and suggestions on testing treatment efficacy with small effects and small samples

2021 ◽  
Author(s):  
Enrico Toffalini ◽  
David Giofrè ◽  
Massimiliano Pastore ◽  
Barbara Carretti ◽  
Federica Fraccadori ◽  
...  
Keyword(s):  
Systematic Review ◽  
Effect Size ◽  
Treatment Efficacy ◽  
Multiple Testing ◽  
Poor Response ◽  
Population Level ◽  
Learning Disorders ◽  
Repeated Measurements ◽  
Response To Treatment ◽  
Small Samples

AbstractPoor response to treatment is a defining characteristic of reading disorder. In the present systematic review and meta-analysis, we found that the overall average effect size for treatment efficacy was modest, with a mean standardized difference of 0.38. Small true effects, combined with the difficulty to recruit large samples, seriously challenge researchers planning to test treatment efficacy in dyslexia and potentially in other learning disorders. Nonetheless, most published studies claim effectiveness, generally based on liberal use of multiple testing. This inflates the risk that most statistically significant results are associated with overestimated effect sizes. To enhance power, we propose the strategic use of repeated measurements with mixed-effects modelling. This novel approach would enable us to estimate both individual parameters and population-level effects more reliably. We suggest assessing a reading outcome not once, but three times, at pre-treatment and three times at post-treatment. Such design would require only modest additional efforts compared to current practices. Based on this, we performed ad hoc a priori design analyses via simulation studies. Results showed that using the novel design may allow one to reach adequate power even with low sample sizes of 30–40 participants (i.e., 15–20 participants per group) for a typical effect size of d = 0.38. Nonetheless, more conservative assumptions are warranted for various reasons, including a high risk of publication bias in the extant literature. Our considerations can be extended to intervention studies of other types of neurodevelopmental disorders.

scholarly journals The analysis of very small samples of repeated measurements II: A modified Box correction

2010 ◽  
Vol 29 (27) ◽  
pp. 2838-2856 ◽  
Author(s):  
Simon S. Skene ◽  
Michael G. Kenward
Keyword(s):  
Repeated Measurements ◽  
Small Samples

scholarly journals Assessment of a Modified Sandwich Estimator for Generalized Estimating Equations with Application to Opioid Poisoning in MIMIC-IV ICU Patients

Stats ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 650-664
Author(s):  
Paul Rogers ◽  
Julie Stoner
Keyword(s):  
Repeated Measures ◽  
Generalized Estimating Equations ◽  
Rare Event ◽  
Estimating Equations ◽  
Small Sample ◽  
Repeated Measurements ◽  
Regression Coefficients ◽  
Binary Outcomes ◽  
Sandwich Estimator ◽  
Generalized Estimating

Longitudinal data is encountered frequently in many healthcare research areas to include the critical care environment. Repeated measures from the same subject are expected to correlate with each other. Models with binary outcomes are commonly used in this setting. Regression models for correlated binary outcomes are frequently fit using generalized estimating equations (GEE). The Liang and Zeger sandwich estimator is often used in GEE to produce unbiased standard error estimation for regression coefficients in large sample settings, even when the covariance structure is misspecified. The sandwich estimator performs optimally in balanced designs when the number of participants is large with few repeated measurements. The sandwich estimator’s asymptotic properties do not hold in small sample and rare-event settings. Under these conditions, the sandwich estimator underestimates the variances and is biased downwards. Here, the performance of a modified sandwich estimator is compared to the traditional Liang-Zeger estimator and alternative forms proposed by authors Morel, Pan, and Mancl-DeRouen. Each estimator’s performance was assessed with 95% coverage probabilities for the regression coefficients using simulated data under various combinations of sample sizes and outcome prevalence values with independence and autoregressive correlation structures. This research was motivated by investigations involving rare-event outcomes in intensive care unit settings.

scholarly journals Article numbers as a leading indicator of publication time

2021 ◽  
Vol 31 (1) ◽  
pp. e41065
Author(s):  
Jimmie Leppink
Keyword(s):  
Correlation Function ◽  
Cross Correlation ◽  
Repeated Measurements ◽  
Cross Correlation Function ◽  
Small Samples ◽  
Outcome Variable ◽  
Leading Indicator ◽  
Single Subjects ◽  
Statistical Program ◽  
Publication Time

Aims: in health professions education (HPE), the use of statistics is commonly associated with somewhat larger samples, whereas smaller samples or single subjects (i.e., N = 1) are usually labelled as needing some kind of ‘qualitative’ approach. However, statistical methods can be very useful in small samples and for individual subjects as well, especially where we have time series of repeated measurements of the same outcome variable(s) of interest. The aim of this article is twofold: to demonstrate an example of a cross-correlation function for single subjects in a HPE context and to suggest a few settings in HPE where this cross-correlation function can be of use.Method: the example uses data from a recent Open Access publication on among others article numbers and publication time in a number of major HPE journals to examine the relation between the number of articles published and median publication time over time in the zero-cost Open-Source statistical program R version 4.0.5.Results: as to be expected, the number of articles published appears somewhat of a leading indicator of publication time: both number of articles in year ‘y’ and number of articles in year ‘y minus 1’ correlate > 0.6 with median publication time in year ‘y’, while correlations of other time differences (e.g., number of articles in year ‘y minus 2’ and median publication time in year ‘y’, or median publication time in year ‘y’ and number of articles in year ‘y plus 1’) are substantially smaller.Conclusion: in line with recent literature, this article demonstrates that the cross-correlation function can be used in the context of small samples and single subjects. While the example focusses on article numbers and publication times, it can equally be applied in for example studying relations between knowledge, skills and attitude in individuals, or relations between behaviors of individuals working in pairs or small groups.

Applying the Anderson-Darling Test to Suicide Clusters

Crisis ◽  
2013 ◽  
Vol 34 (6) ◽  
pp. 434-437 ◽  
Author(s):  
Donald W. MacKenzie
Keyword(s):  
Poisson Process ◽  
Goodness Of Fit ◽  
Small Samples ◽  
Massachusetts Institute Of Technology ◽  
Homogeneous Poisson Process ◽  
Cornell University ◽  
The Difference ◽  
Suicide Clusters ◽  
Institute Of Technology ◽  
Anderson Darling

Background: Suicide clusters at Cornell University and the Massachusetts Institute of Technology (MIT) prompted popular and expert speculation of suicide contagion. However, some clustering is to be expected in any random process. Aim: This work tested whether suicide clusters at these two universities differed significantly from those expected under a homogeneous Poisson process, in which suicides occur randomly and independently of one another. Method: Suicide dates were collected for MIT and Cornell for 1990–2012. The Anderson-Darling statistic was used to test the goodness-of-fit of the intervals between suicides to distribution expected under the Poisson process. Results: Suicides at MIT were consistent with the homogeneous Poisson process, while those at Cornell showed clustering inconsistent with such a process (p = .05). Conclusions: The Anderson-Darling test provides a statistically powerful means to identify suicide clustering in small samples. Practitioners can use this method to test for clustering in relevant communities. The difference in clustering behavior between the two institutions suggests that more institutions should be studied to determine the prevalence of suicide clustering in universities and its causes.

The German Version of the Satisfaction With Life Scale (SWLS)

2011 ◽  
Vol 27 (2) ◽  
pp. 127-132 ◽  
Author(s):  
Heide Glaesmer ◽  
Gesine Grande ◽  
Elmar Braehler ◽  
Marcus Roth
Keyword(s):  
Convergent Validity ◽  
Satisfaction With Life ◽  
Age Groups ◽  
Population Based ◽  
Factorial Invariance ◽  
Satisfaction With Life Scale ◽  
Small Samples ◽  
Cross Sectional ◽  
Confirmatory Factor Analyses ◽  
Life Scale

The Satisfaction with Life Scale (SWLS) is the most commonly used measure for life satisfaction. Although there are numerous studies confirming factorial validity, most studies on dimensionality are based on small samples. A controversial debate continues on the factorial invariance across different subgroups. The present study aimed to test psychometric properties, factorial structure, factorial invariance across age and gender, and to deliver population-based norms for the German general population from a large cross-sectional sample of 2519 subjects. Confirmatory factor analyses supported that the scale is one-factorial, even though indications of inhomogeneity of the scale have been detected. Both findings show invariance across the seven age groups and both genders. As indicators of the convergent validity, a positive correlation with social support and negative correlation with depressiveness was shown. Population-based norms are provided to support the application in the context of individual diagnostics.

Modeling Intraindividual Variability in Three-Level Multilevel Models

Methodology ◽  
2018 ◽  
Vol 14 (3) ◽  
pp. 95-108 ◽  
Author(s):  
Steffen Nestler ◽  
Katharina Geukes ◽  
Mitja D. Back
Keyword(s):  
Multilevel Model ◽  
Multilevel Models ◽  
Mixed Effects ◽  
Repeated Measurements ◽  
Scale Model ◽  
Suggested Approach ◽  
Psychological Study ◽  
Level Data ◽  
Ecological Momentary ◽  
Momentary Assessment

Abstract. The mixed-effects location scale model is an extension of a multilevel model for longitudinal data. It allows covariates to affect both the within-subject variance and the between-subject variance (i.e., the intercept variance) beyond their influence on the means. Typically, the model is applied to two-level data (e.g., the repeated measurements of persons), although researchers are often faced with three-level data (e.g., the repeated measurements of persons within specific situations). Here, we describe an extension of the two-level mixed-effects location scale model to such three-level data. Furthermore, we show how the suggested model can be estimated with Bayesian software, and we present the results of a small simulation study that was conducted to investigate the statistical properties of the suggested approach. Finally, we illustrate the approach by presenting an example from a psychological study that employed ecological momentary assessment.

Assessing Person Fit With the Information Matrix Test

Methodology ◽  
2015 ◽  
Vol 11 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Jochen Ranger ◽  
Jörg-Tobias Kuhn
Keyword(s):  
Simulation Study ◽  
Type I Error ◽  
Information Matrix ◽  
Small Samples ◽  
Type I ◽  
Person Fit ◽  
Power Of The Test ◽  
Order Expansion ◽  
Trait Stability ◽  
Information Matrix Test

In this manuscript, a new approach to the analysis of person fit is presented that is based on the information matrix test of White (1982) . This test can be interpreted as a test of trait stability during the measurement situation. The test follows approximately a χ2-distribution. In small samples, the approximation can be improved by a higher-order expansion. The performance of the test is explored in a simulation study. This simulation study suggests that the test adheres to the nominal Type-I error rate well, although it tends to be conservative in very short scales. The power of the test is compared to the power of four alternative tests of person fit. This comparison corroborates that the power of the information matrix test is similar to the power of the alternative tests. Advantages and areas of application of the information matrix test are discussed.

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