Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide

Front Cover
Cambridge University Press, Mar 27, 2003 - Medical - 301 pages
2 Reviews
In this book the most important techniques available for longitudinal data analysis are discussed. This discussion includes simple techniques such as the paired t-test and summary statistics, but also more sophisticated techniques such as generalised estimating equations and random coefficient analysis. A distinction is made between longitudinal analysis with continuous, dichotomous, and categorical outcome variables. It should be stressed that the emphasis of the discussion lies on the interpretation of the different techniques and on the comparison of the results of different techniques. Furthermore, special chapters will deal with the analysis of two measurements, experimental studies and the problem of missing data in longitudinal studies. Finally, an extensive overview of (and a comparison between) different software packages is provided. It is important to realise that this book is a practical guide and especially suitable for non-statisticians.
  

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Google preview only to p 34
Intro
Study Design 7
Continous outcome variables15
Continous outcome variables 55
GEE 62
Random Coeff Analysis 77
Dichotomous Outcome 120
Long with 2 outcome 167
Missing Data
Tracking
Software
p.3 data set has 4 predictors x1 continuous time indepen, x2 cont time dep, x3 dichot time-dep, x4 dichot time-ind
p.6 long and broad data structures
most clinical trials cannot distinguish between age (DOB-Measurement) , period (measurement time) & birth (cohort-birth year) effects
Ex period effect of hot summer on age v activity
Ex cohort effect on height v age
(p.9)
 

Contents

Introduction
1
12 General approach
2
14 Example
3
15 Software
5
Study design
7
22 Observational longitudinal studies
9
222 Other confounding effects
13
223 Example
14
7754 Example
153
72 Count outcome variables
156
721 Example
157
7212 GEE analysis
158
7213 Random coefficient analysis
163
722 Comparison between GEE analysis and random coefficient analysis
165
Longitudinal studies with two measurements the definition and analysis of change
167
821 A numerical example
171

23 Experimental longitudinal studies
15
Continuous outcome variables
18
311 Example
20
32 Nonparametric equivalent of the paired ttest
21
321 Example
22
33 More than two measurements
23
a numerical example
26
332 The shape of the relationship between an outcome variable and time
29
333 A numerical example
30
334 Example
32
34 The univariate or the multivariate approach?
37
35 Comparing groups
38
a numerical example
39
352 Example
41
36 Comments
45
37 Posthoc procedures
46
371 Example
47
38 Different contrasts
48
381 Example
49
39 Nonparametric equivalent of MANOVA for repeated measurements
52
391 Example
53
Continuous outcome variables relationships with other variables
55
43 Example
57
44 Longitudinal methods
60
45 Generalized estimating equations
62
453 Interpretation of the regression coefficients derived from GEE analysis
66
454 Example
68
4542 Results of a GEE analysis
69
4543 Different correlation structures
72
4544 Unequally spaced time intervals
75
46 Random coefficient analysis
77
463 Example
80
4632 Unequally spaced time intervals
88
47 Comparison between GEE analysis and random coefficient analysis
91
471 Extensions of random coefficient analysis
92
4721 A numerical example
93
474 Comments
95
481 Example
98
Other possibilities for modelling longitudinal data
102
522 Modelling of changes
105
523 Autoregressive model
107
524 Overview
108
5252 Data structure for alternative models
109
5254 Random coefficient analysis
112
53 Comments
114
54 Another example
118
Dichotomous outcome variables
120
612 More than two measurements
122
614 Example
123
6143 Comparing groups
126
62 Relationships with other variables
128
623 Sophisticated methods
129
624 Example
131
6242 Random coefficient analysis
137
625 Comparison between GEE analysis and random coefficient analysis
140
626 Alternative models
143
627 Comments
144
Categorical and count outcome variables
145
712 More than two measurements
146
713 Comparing groups
147
715 Relationships with other variables
151
7153 Sophisticated methods
152
822 Example
173
83 Dichotomous and categorical outcome variables
175
84 Comments
177
85 Sophisticated analyses
178
Analysis of experimental studies
179
92 Example with a continuous outcome variable
181
922 Simple analysis
182
923 Summary statistics
184
924 MANOVA for repeated measurements
185
9241 MANOVA for repeated measurements corrected for the baseline value
186
925 Sophisticated analysis
188
93 Example with a dichotomous outcome variable
195
933 Sophisticated analysis
196
94 Comments
200
Missing data in longitudinal studies
202
102 Ignorable or informative missing data?
204
103 Example
205
1032 Analysis of determinants for missing data
206
104 Analysis performed on datasets with missing data
207
1041 Example
208
105 Comments
212
106 Imputation methods
213
10613 Multiple imputation method
214
1062 Dichotomous and categorical outcome variables
216
70632 Dichotomous outcome variables
219
1064 Comments
221
107 Alternative approaches
223
Tracking
225
113 Dichotomous and categorical outcome variables
230
114 Example
234
1141 Two measurements
235
1142 More than two measurements
237
115 Comments
238
1152 Risk factors for chronic diseases
239
116 Conclusions
240
Software for longitudinal data analysis
241
1222 SAS
243
1223 SPLUS
244
1224 Overview
246
123 GEE analysis with dichotomous outcome variables
247
1232 SAS
248
1233 SPLUS
249
1234 Overview
250
1242 SAS
251
1243 SPLUS
255
1244 SPSS
257
1245 MLwiN
259
1246 Overview
262
125 Random coefficient analysis with dichotomous outcome variables
263
1252 STATA
264
1253 SAS
265
1254 MLwiN
269
1255 Overview
270
126 Categorical and count outcome variables
271
127 Alternative approach using covariance structures
272
1271 Example
274
Sample size calculations
280
132 Example
283
References
286
Index
295
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About the author (2003)

Dr Jos W. R. Twisk is Senior Researcher and Lecturer in the Department of Clinical Epidemiology and Biostatistics and the Institute for Research in Extramural Studies, Vrije Universitiet, Medical Centre, Amsterdam.

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