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Analysing Seasonal Health Data [Hardback]

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  • Formāts: Hardback, 164 pages, height x width: 235x155 mm, weight: 950 g, 112 Illustrations, black and white; XIII, 164 p. 112 illus., 1 Hardback
  • Sērija : Statistics for Biology and Health
  • Izdošanas datums: 26-Feb-2010
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642107478
  • ISBN-13: 9783642107474
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Analysing Seasonal Health DataPalielināt
  • Formāts: Hardback, 164 pages, height x width: 235x155 mm, weight: 950 g, 112 Illustrations, black and white; XIII, 164 p. 112 illus., 1 Hardback
  • Sērija : Statistics for Biology and Health
  • Izdošanas datums: 26-Feb-2010
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642107478
  • ISBN-13: 9783642107474
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783642107474.html
Keywords:

Seasonal patterns have been found in a remarkable range of health conditions, including birth defects, respiratory infections and cardiovascular disease. Accurately estimating the size and timing of seasonal peaks in disease incidence is an aid to understanding the causes and possibly to developing interventions. With global warming increasing the intensity of seasonal weather patterns around the world, a review of the methods for estimating seasonal effects on health is timely.

This is the first book on statistical methods for seasonal data written for a health audience. It describes methods for a range of outcomes (including continuous, count and binomial data) and demonstrates appropriate techniques for summarising and modelling these data. It has a practical focus and uses interesting examples to motivate and illustrate the methods. The statistical procedures and example data sets are available in an R package called ‘season’.



This book explains statistical methods for finding and estimating seasonal patterns. It describes methods for a range of outcomes (including continuous, count and binomial data) and demonstrates appropriate techniques for summarizing and modeling these data.

Recenzijas

From the reviews:

This book is aimed at both non-statistical researchers and statisticians, and it is presented as the first book on statistical methods for seasonal data for a health audience. this is a useful book on an important subject and I would recommend it to anybody interested in the analysis of seasonal data. (Mario Cortina Borja, Significance, June, 2011)

The authors are to be commended on a useful and clear introduction to seasonal health data analysis. The text will be helpful to statisticians, particularly in combination with the associated R package season, which will encourage them to test their own preferred methods in context and assist in teaching seasonal modelling. (Malcolm Hudson, Australian & New Zealand Journal of Statistics, Vol. 53 (3), 2011)

1 Introduction 1
1.1 Example Data Sets
1
1.1.1 Cardiovascular Disease Deaths
1
1.1.2 Schizophrenia
2
1.1.3 Influenza
4
1.1.4 Exercise
5
1.1.5 Stillbirths
6
1.1.6 Footballers
7
1.2 Time Series Methods
8
1.2.1 Autocovariance and Autocorrelation
9
1.3 Fourier Series
14
1.3.1 Cosine and Sine Functions
14
1.3.2 Fourier Series
18
1.3.3 Periodogram
19
1.3.4 Cumulative Periodogram
23
1.4 Regression Methods
25
1.4.1 Scatter Plot
26
1.4.2 Linear Regression
27
1.4.3 Residual Checking
29
1.4.4 Influential Observations
33
1.4.5 Generalized Linear Model
35
1.4.6 Offsets
38
1.4.7 Akaike Information Criterion
39
1.4.8 Non-linear Regression Using Splines
40
1.5 Box Plots
42
1.6 Bayesian Statistics
44
1.6.1 Markov Chain Monte Carlo Estimation
45
1.6.2 Deviance Information Criterion
46
2 Introduction to Seasonality 49
2.1 What is a Season?
49
2.1.1 Seasonality and Health
50
2.2 Descriptive Seasonal Statistics and Plots
53
2.2.1 Adjusting Monthly Counts
53
2.2.2 Data Reduction
55
2.2.3 Circular Plot
61
2.2.4 Smooth Plot of Season
63
2.3 Modelling Monthly Data
65
2.3.1 Month as a Fixed Effect
66
2.3.2 Month as a Random Effect
69
2.3.3 Month as a Correlated Random Effect
69
3 Cosinor 75
3.1 Examples
76
3.1.1 Cardiovascular Disease Deaths
76
3.1.2 Exercise
78
3.1.3 Stillbirths
80
3.2 Tests of Seasonality
80
3.2.1 Chi-Squared Test of Seasonality
83
3.2.2 Sample Size Using the Cosinor Test
85
3.3 Sawtooth Season
86
3.3.1 Examples
87
4 Decomposing Time Series 93
4.1 Stationary Cosinor
96
4.1.1 Examples
97
4.2 Season, Trend, Loess
98
4.2.1 Examples
101
4.3 Non-stationary Cosinor
104
4.3.1 Parameter Estimation
106
4.3.2 Examples
109
4.4 Modelling the Amplitude and Phase
111
4.4.1 Parameter Estimation
114
4.4.2 Examples
116
4.5 Month as a Random Effect
118
4.5.1 Examples
119
4.6 Comparing the Decomposition Methods
121
4.7 Exposures
122
4.7.1 Comparing Trends with Trends and Seasons with Seasons
123
4.7.2 Exposure–Risk Relationships
124
5 Controlling for Season 129
5.1 Case–Crossover
129
5.1.1 Matching Using Day of the Week
132
5.1.2 Case–Crossover Examples
133
5.1.3 Changing Stratum Length
135
5.1.4 Matching Using a Continuous Confounder
135
5.1.5 Non-linear Associations
136
5.2 Generalized Additive Model
138
5.2.1 Definition of a GAM
138
5.2.2 Non-linear Confounders
140
5.3 A Spiked Seasonal Pattern
142
5.3.1 Modelling a Spiked Seasonal Pattern
143
5.4 Adjusting for Seasonal Independent Variables
146
5.4.1 Effect on Estimates of Long-term Risk
147
5.5 Biases Caused by Ignoring Season
149
6 Clustered Seasonal Data 151
6.1 Seasonal Heterogeneity
151
6.2 Longitudinal Models
153
6.2.1 Example
154
6.3 Spatial Models
155
6.3.1 Example
156
References 159
Index 163
Adrian Barnett is a senior research fellow at Queensland University of Technology, Australia. Annette Dobson is a Professor of Biostatistics at The University of Queensland, Australia. Both are experienced medical statisticians with a commitment to statistical education and have previously collaborated in research in the methodological developments and applications of biostatistics, especially to time series data. Among other projects, they worked together on revising the well-known textbook "An Introduction to Generalized Linear Models," third edition, Chapman Hall/CRC, 2008. In their new book they share their knowledge of statistical methods for examining seasonal patterns in health.