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Books on the topic 'Longitudinal clusters'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Laird, Nan. Analysis of Longitudinal and Cluster-Correlated Data. Institute of Mathematical Statistics and American Statistical Association, 2004. http://dx.doi.org/10.1214/cbms/1462106075.

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2

A, Croon Marcel, and Hagenaars Jacques A, eds. Marginal models: For dependent, clustered, and longitudinal categorical data. Springer, 2009.

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3

1961-, Heo Moonseoung, Zhang Song 1976-, and Kim, Mimi (Mimi Y.), eds. Sample size calculations for clustered and longitudinal outcomes in clinical research. CRC Press, Taylor & Francis, 2015.

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4

Sutradhar, Brajendra C. Dynamic mixed models for familial longitudinal data. Springer, 2011.

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5

Analysis of longitudinal and cluster-correlated data. Institute of Mathematical Statistics, 2004.

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6

Miksza, Peter, and Kenneth Elpus. Multilevel Models. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199391905.003.0012.

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This chapter introduces a statistical approach for analyzing nested data structures that both accounts for the dependence of observations due to hierarchical arrangements and allows for testing hypotheses at multiple levels. The most common application of multilevel models is for analyses of objects (e.g., people) nested within groups or clusters of some sort. Multilevel models can also be applied to longitudinal data analyses such that the “levels” do not refer to objects nested within groups but instead refer to multiple measurements (e.g., measures made at different occasions/time points) n

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7

Sutradhar, Brajendra C. Dynamic Mixed Models for Familial Longitudinal Data. Springer, 2013.

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8

Bergsma, Wicher, Jacques A. Hagenaars, and Marcel A. Croon. Marginal Models: For Dependent, Clustered, and Longitudinal Categorical Data. Springer, 2010.

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9

Zhang, Song, Chul Ahn, and Moonseong Heo. Sample Size Calculations for Clustered and Longitudinal Outcomes in Clinical Research. Taylor & Francis Group, 2020.

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10

Zhang, Song, Chul Ahn, and Moonseoung Heo. Sample Size Calculations for Clustered and Longitudinal Outcomes in Clinical Research. Taylor & Francis Group, 2014.

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11

Zhang, Song, Chul Ahn, and Moonseoung Heo. Sample Size Calculations for Clustered and Longitudinal Outcomes in Clinical Research. Taylor & Francis Group, 2014.

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12

Dynamic Mixed Models for Familial Longitudinal Data. Springer, 2011.

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13

Sutradhar, Brajendra C. Dynamic Mixed Models for Familial Longitudinal Data. Springer, 2011.

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14

Statistical Methods in Psychiatry and Related Fields: Longitudinal, Clustered, and Other Repeated Measures Data. Taylor & Francis Group, 2017.

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15

Gueorguieva, Ralitza. Statistical Methods in Psychiatry and Related Fields: Longitudinal, Clustered, and Other Repeated Measures Data. Taylor & Francis Group, 2017.

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16

Gueorguieva, Ralitza. Statistical Methods in Psychiatry and Related Fields: Longitudinal, Clustered, and Other Repeated Measures Data. Taylor & Francis Group, 2017.

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17

Gueorguieva, Ralitza. Statistical Methods in Psychiatry and Related Fields: Longitudinal, Clustered, and Other Repeated Measures Data. Taylor & Francis Group, 2017.

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18

Gueorguieva, Ralitza. Statistical Methods in Psychiatry and Related Fields: Longitudinal, Clustered, and Other Repeated Measures Data. Taylor & Francis Group, 2017.

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19

Hans, Steiner, Daniels Whitney, Kelly Michael, and Stadler Christina. Epidemiology of Disruptive Behavior Disorders. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190265458.003.0003.

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This chapter summarizes the available epidemiological evidence supporting the current diagnoses grouped in the disruptive behavior disorder (DBD) cluster. It seems DBDs are common disorders, but although researchers have made great strides in capturing the prevalence of these disorders in normal and clinical populations, and although there are some very solid longitudinal findings, there are still many unknowns that need to be corrected. Most of the difficulties encountered in epidemiology are a function of the ongoing changes in the descriptive diagnostic criteria and the lack of a truly stat

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