Epidemiology and medical statistics:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Amsterdam [u.a.]
North-Holland
2008
|
| Ausgabe: | 1. ed. |
| Schriftenreihe: | Handbook of statistics
27 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVIII, 852 S. graph. Darst. |
| ISBN: | 9780444528018 |
Internformat
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| 245 | 1 | 0 | |a Epidemiology and medical statistics |c ed. by C. R. Rao ... |
| 250 | |a 1. ed. | ||
| 264 | 1 | |a Amsterdam [u.a.] |b North-Holland |c 2008 | |
| 300 | |a XVIII, 852 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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| 490 | 1 | |a Handbook of statistics |v 27 | |
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| 830 | 0 | |a Handbook of statistics |v 27 |w (DE-604)BV000002510 |9 27 | |
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Datensatz im Suchindex
| _version_ | 1804137248528007168 |
|---|---|
| adam_text | Table
of
contents
Preface
xiii
Contributors
χν
Ch.
1.
Statistical Methods and Challenges in Epidemiology and
Biomedical
Research
1
Ross L. Prentice
1.
Introduction
1
2.
Characterizing the study cohort
3
3.
Observational study methods and challenges
6
4.
Randomized controlled trials
12
5.
Intermediate, surrogate, and auxiliary outcomes
17
6.
Multiple testing issues and high-dimensional biomarkers
18
7.
Further discussion and the Women s Health Initiative example
21
References
22
Ch.
2.
Statistical Inference for Causal Effects, With Emphasis on
Applications in Epidemiology and Medical Statistics
28
Donald B. Rubin
1.
Causal inference primitives
28
2.
The assignment mechanism
36
3.
Assignment-based modes of causal inference
41
4.
Posterior predictive causal inference
47
5.
Complications
55
References
58
Ch.
3.
Epidemiologie
Study Designs
64
Kenneth J. Rothman, Sander Greenland and Timothy L. Lash
1.
Introduction
64
2.
Experimental studies
65
3.
Nonexperimental studies
73
4.
Cohort studies
73
5.
Case-control studies
84
6.
Variants
of the case-control design
97
7.
Conclusion
104
References
104
Ch.
4.
Statistical Methods for Assessing
Biomarkers
and Analyzing
Biomarker Data
109
Stephen W. Looney and Joseph L.
Hagan
1.
Introduction
109
2.
Statistical methods for assessing
biomarkers
110
3.
Statistical methods for analyzing biomarker data
126
4.
Concluding remarks
143
References
144
Ch.
5.
Linear and Non-Linear Regression Methods in Epidemiology and
Biostatistics
148
Eric Vittinghoff, Charles E. McCulloch, David V. Glidden and Stephen
C. Shiboski
1.
Introduction
148
2.
Linear models
151
3.
Non-linear models
167
4.
Special topics
176
References
182
Ch.
6.
Logistic Regression
187
Edward L.
Spitznagel
Jr.
1.
Introduction
187
2.
Estimation of a simple logistic regression model
188
3.
Two measures of model fit
191
4.
Multiple logistic regression
192
5.
Testing for interaction
194
6.
Testing goodness of fit: Two measures for lack of fit
195
7.
Exact logistic regression
196
8.
Ordinal logistic regression
201
9.
Multinomial logistic regression
204
10.
Probit
regression
206
11.
Logistic regression in case-control studies
207
References
209
Ch.
7.
Count Response Regression Models
210
Joseph M.
Hübe
and William H. Greene
1.
Introduction
210
2.
The
Poisson
regression model
212
3.
Heterogeneity and overdispersion
224
4.
Important extensions of the models for counts
230
5.
Software
247
6.
Summary and conclusions
250
References
251
Ch.
8.
Mixed Models
253
Matthew J.
Gurka
and Lloyd J. Edwards
1.
Introduction
253
2.
Estimation for the linear mixed model
259
3.
Inference for the mixed model
261
4.
Selecting the best mixed model
264
5.
Diagnostics for the mixed model
268
6.
Outliers
270
7.
Missing data
270
8.
Power and sample size
272
9.
Generalized linear mixed models
273
10.
Nonlinear mixed models
274
11.
Mixed models for survival data
275
12.
Software
276
13.
Conclusions
276
References
277
Ch.
9.
Survival Analysis
281
John P. Klein and
Mei
-Ле
Zhang
1.
Introduction
281
2.
Univariate analysis
282
3.
Hypothesis testing
288
4.
Regression models
295
5.
Regression models for competing risks
310
References
317
Ch.
10.
A Review of Statistical Analyses for Competing Risks
321
Melvin L. Moeschberger, Kevin P. Tor doff and Nidhi Kochar
1.
Introduction
321
2.
Approaches to the statistical analysis of competing risks
324
3.
Example
327
4.
Conclusion
339
References
340
Ch.
11.
Cluster Analysis
342
William D. Shannon
1.
Introduction
342
2.
Proximity measures
344
3.
Hierarchical clustering
350
4.
Partitioning
355
5.
Ordination (scaling)
358
6.
How many clusters?
361
7.
Applications in medicine
364
8.
Conclusion
364
References
365
Ch.
12.
Factor Analysis and Related Methods
367
Carol M. Woods and Michael C. Edwards
1.
Introduction
367
2.
Exploratory factor analysis (EFA)
368
3.
Principle components analysis (PCA)
375
4.
Confirmatory factor analysis
(CFA)
375
5.
FA with non-normal continuous variables
379
6.
FA with categorical variables
380
7.
Sample size in FA
382
8.
Examples of EFA and
CFA
383
9.
Additional resources
389
Appendix A
391
Appendix
В
391
References
391
Ch.
13.
Structural Equation Modeling
395
Kentaro Hayashi, Peter M. Bentler and Ke-Hai Yuan
1.
Models and identification
395
2.
Estimation and evaluation
399
3.
Extensions of
SEM
410
4.
Some practical issues
415
References
418
Ch.
14.
Statistical Modeling in
Biomedical
Research: Longitudinal
Data Analysis
429
Chengjie Xiong, Kejun Zhu,
Kai
Yu and J. Philip Miller
1.
Introduction
429
2.
Analysis of longitudinal data
431
3.
Design issues of a longitudinal study
456
References
460
Ch.
15.
Design and Analysis of Cross-Over Trials
464
Michael G.
Kenward
and Byron Jones
1.
Introduction
464
2.
The two-period two-treatment cross-over trial
467
3.
Higher-order designs
476
4.
Analysis with non-normal data
482
5.
Other application areas
485
6.
Computer software
488
References
489
Ch.
16.
Sequential and Group Sequential Designs in Clinical Trials:
Guidelines for Practitioners
491
Madhu Mazumdar and Heejung Bang
1.
Introduction
492
2.
Historical background of sequential procedures
493
3.
Group sequential procedures for randomized trials
494
4.
Steps for GSD design and analysis
507
5.
Discussion
508
References
509
Ch.
17.
Early Phase Clinical Trials: Phases I and II
513
Feng
Gao,
Kathryn
Trinkaus
and J. Philip Miller
1. Introduction
513
2.
Phase I designs
514
3.
Phase II designs
526
4.
Summary
539
References
541
Ch.
18.
Definitive Phase III and Phase IV Clinical Trials
546
Barry
R
Davis and Sarah Baraniuk
1.
Introduction
546
2.
Questions
548
3.
Randomization
550
4.
Recruitment
551
5.
Adherence/sample size/power
552
6.
Data analysis
554
7.
Data quality and control/data management
558
8.
Data monitoring
558
9.
Phase IV trials
563
10.
Dissemination
-
trial reporting and beyond
564
11.
Conclusions
565
References
565
Ch.
19.
Incomplete Data in Epidemiology and Medical Statistics
569
Susanne Rässler,
Donald
В.
Rubin and Elizabeth R.
Zeil
1.
Introduction
569
2.
Missing-data mechanisms and ignorability
571
3.
Simple approaches to handling missing data
573
4.
Single imputation
574
5.
Multiple imputation
578
6.
Direct analysis using model-based procedures
581
7.
Examples
584
8.
Literature review for epidemiology and medical studies
586
9.
Summary and discussion
587
Appendix A
588
Appendix
В
592
References
598
Ch.
20. Meta-Analysis 602
Edward L. Spitznagel
Jr.
1.
Introduction
602
2.
History
603
3.
The Cochran-Mantel-Haenszel test
604
4.
Glass s proposal for meta-analysis
606
5.
Random effects models
607
6.
The forest plot
609
7.
Publication bias
610
8.
The Cochrane Collaboration
614
References
614
Ch.
21.
The Multiple Comparison Issue in Health Care Research
616
Lemuel
A. Moyé
1.
Introduction
616
2.
Concerns for significance testing
617
3.
Appropriate use of significance testing
618
4.
Definition of multiple comparisons
619
5.
Rational for multiple comparisons
620
6.
Multiple comparisons and analysis triage
621
7.
Significance testing and multiple comparisons
623
8.
Family wise error rate
625
9.
The Bonferroni inequality
626
10.
Alternative approaches
629
11.
Dependent testing
631
12.
Multiple comparisons and combined endpoints
635
13.
Multiple comparisons and subgroup analyses
641
14.
Data dredging
651
References
651
Ch.
22.
Power: Establishing the Optimum Sample Size
656
Richard
A. Zeller
and Yan Yan
1.
Introduction
656
2.
Illustrating power
658
3.
Comparing simulation and software approaches to power
663
4.
Using power to decrease sample size
672
5.
Discussion
677
References
677
Ch.
23.
Statistical Learning in Medical Data Analysis
679
Grace Wahba
1.
Introduction
679
2.
Risk factor estimation: penalized likelihood estimates
681
3.
Risk factor estimation: likelihood basis pursuit and the LASSO
690
4.
Classification: support
vector
machines
and related estimates
693
5.
Dissimilarity
data and kernel
estimates
700
6.
Tuning methods
704
7.
Regularization, empirical
Bayes,
Gaussian processes priors, and
reproducing kernels
707
References
708
Ch.
24.
Evidence Based Medicine and Medical Decision Making
712
Dan Mayer
1.
The definition and history of evidence based medicine
712
2.
Sources and levels of evidence
715
3.
The five stage process of EBM
717
4.
The hierarchy of evidence: study design and minimizing bias
718
5.
Assessing the significance or impact of study results: Statistical significance and
confidence intervals
721
6.
Meta-analysis and systematic reviews
722
7.
The value of clinical information and assessing the usefulness
of a diagnostic test
722
8.
Expected values decision making and the threshold approach
to diagnostic testing
726
9.
Summary
727
10.
Basic principles
727
References
728
Ch.
25.
Estimation of Marginal Regression Models with Multiple
Source Predictors
730
Heather J. Litman, Nicholas J.
Horton,
Bernardo Hernandez and
Nan M. Laird
1.
Introduction
730
2.
Review of the generalized estimating equations approach
732
3.
Maximum likelihood estimation
735
4.
Simulations
737
5.
Efficiency calculations
740
6.
Illustration
741
7.
Conclusion
743
References
745
Ch.
26.
Difference Equations with Public Health Applications
747
Asha Seth Kapadia and Lemuel
A. Moyé
1.
Introduction
747
2.
Generating functions
748
3.
Second-order nonhomogeneous equations and generating functions
750
4.
Example in rhythm disturbances
752
5.
Follow-up losses in clinical trials
758
6.
Applications in epidemiology
765
References
773
Ch.
27.
The Bayesian Approach to Experimental Data Analysis
775
Bruno Lecoutre
Preamble: and if you were a Bayesian without knowing it?
775
1.
Introduction
776
2.
Frequentist and Bayesian inference
778
3.
An illustrative example
783
4.
Other examples of inferences about proportions
795
5.
Concluding remarks and some further topics
803
References
808
Subject Index
813
Handbook of Statistics Contents of Previous Volumes
823
|
| adam_txt |
Table
of
contents
Preface
xiii
Contributors
χν
Ch.
1.
Statistical Methods and Challenges in Epidemiology and
Biomedical
Research
1
Ross L. Prentice
1.
Introduction
1
2.
Characterizing the study cohort
3
3.
Observational study methods and challenges
6
4.
Randomized controlled trials
12
5.
Intermediate, surrogate, and auxiliary outcomes
17
6.
Multiple testing issues and high-dimensional biomarkers
18
7.
Further discussion and the Women's Health Initiative example
21
References
22
Ch.
2.
Statistical Inference for Causal Effects, With Emphasis on
Applications in Epidemiology and Medical Statistics
28
Donald B. Rubin
1.
Causal inference primitives
28
2.
The assignment mechanism
36
3.
Assignment-based modes of causal inference
41
4.
Posterior predictive causal inference
47
5.
Complications
55
References
58
Ch.
3.
Epidemiologie
Study Designs
64
Kenneth J. Rothman, Sander Greenland and Timothy L. Lash
1.
Introduction
64
2.
Experimental studies
65
3.
Nonexperimental studies
73
4.
Cohort studies
73
5.
Case-control studies
84
6.
Variants
of the case-control design
97
7.
Conclusion
104
References
104
Ch.
4.
Statistical Methods for Assessing
Biomarkers
and Analyzing
Biomarker Data
109
Stephen W. Looney and Joseph L.
Hagan
1.
Introduction
109
2.
Statistical methods for assessing
biomarkers
110
3.
Statistical methods for analyzing biomarker data
126
4.
Concluding remarks
143
References
144
Ch.
5.
Linear and Non-Linear Regression Methods in Epidemiology and
Biostatistics
148
Eric Vittinghoff, Charles E. McCulloch, David V. Glidden and Stephen
C. Shiboski
1.
Introduction
148
2.
Linear models
151
3.
Non-linear models
167
4.
Special topics
176
References
182
Ch.
6.
Logistic Regression
187
Edward L.
Spitznagel
Jr.
1.
Introduction
187
2.
Estimation of a simple logistic regression model
188
3.
Two measures of model fit
191
4.
Multiple logistic regression
192
5.
Testing for interaction
194
6.
Testing goodness of fit: Two measures for lack of fit
195
7.
Exact logistic regression
196
8.
Ordinal logistic regression
201
9.
Multinomial logistic regression
204
10.
Probit
regression
206
11.
Logistic regression in case-control studies
207
References
209
Ch.
7.
Count Response Regression Models
210
Joseph M.
Hübe
and William H. Greene
1.
Introduction
210
2.
The
Poisson
regression model
212
3.
Heterogeneity and overdispersion
224
4.
Important extensions of the models for counts
230
5.
Software
247
6.
Summary and conclusions
250
References
251
Ch.
8.
Mixed Models
253
Matthew J.
Gurka
and Lloyd J. Edwards
1.
Introduction
253
2.
Estimation for the linear mixed model
259
3.
Inference for the mixed model
261
4.
Selecting the best mixed model
264
5.
Diagnostics for the mixed model
268
6.
Outliers
270
7.
Missing data
270
8.
Power and sample size
272
9.
Generalized linear mixed models
273
10.
Nonlinear mixed models
274
11.
Mixed models for survival data
275
12.
Software
276
13.
Conclusions
276
References
277
Ch.
9.
Survival Analysis
281
John P. Klein and
Mei
-Ле
Zhang
1.
Introduction
281
2.
Univariate analysis
282
3.
Hypothesis testing
288
4.
Regression models
295
5.
Regression models for competing risks
310
References
317
Ch.
10.
A Review of Statistical Analyses for Competing Risks
321
Melvin L. Moeschberger, Kevin P. Tor doff and Nidhi Kochar
1.
Introduction
321
2.
Approaches to the statistical analysis of competing risks
324
3.
Example
327
4.
Conclusion
339
References
340
Ch.
11.
Cluster Analysis
342
William D. Shannon
1.
Introduction
342
2.
Proximity measures
344
3.
Hierarchical clustering
350
4.
Partitioning
355
5.
Ordination (scaling)
358
6.
How many clusters?
361
7.
Applications in medicine
364
8.
Conclusion
364
References
365
Ch.
12.
Factor Analysis and Related Methods
367
Carol M. Woods and Michael C. Edwards
1.
Introduction
367
2.
Exploratory factor analysis (EFA)
368
3.
Principle components analysis (PCA)
375
4.
Confirmatory factor analysis
(CFA)
375
5.
FA with non-normal continuous variables
379
6.
FA with categorical variables
380
7.
Sample size in FA
382
8.
Examples of EFA and
CFA
383
9.
Additional resources
389
Appendix A
391
Appendix
В
391
References
391
Ch.
13.
Structural Equation Modeling
395
Kentaro Hayashi, Peter M. Bentler and Ke-Hai Yuan
1.
Models and identification
395
2.
Estimation and evaluation
399
3.
Extensions of
SEM
410
4.
Some practical issues
415
References
418
Ch.
14.
Statistical Modeling in
Biomedical
Research: Longitudinal
Data Analysis
429
Chengjie Xiong, Kejun Zhu,
Kai
Yu and J. Philip Miller
1.
Introduction
429
2.
Analysis of longitudinal data
431
3.
Design issues of a longitudinal study
456
References
460
Ch.
15.
Design and Analysis of Cross-Over Trials
464
Michael G.
Kenward
and Byron Jones
1.
Introduction
464
2.
The two-period two-treatment cross-over trial
467
3.
Higher-order designs
476
4.
Analysis with non-normal data
482
5.
Other application areas
485
6.
Computer software
488
References
489
Ch.
16.
Sequential and Group Sequential Designs in Clinical Trials:
Guidelines for Practitioners
491
Madhu Mazumdar and Heejung Bang
1.
Introduction
492
2.
Historical background of sequential procedures
493
3.
Group sequential procedures for randomized trials
494
4.
Steps for GSD design and analysis
507
5.
Discussion
508
References
509
Ch.
17.
Early Phase Clinical Trials: Phases I and II
513
Feng
Gao,
Kathryn
Trinkaus
and J. Philip Miller
1. Introduction
513
2.
Phase I designs
514
3.
Phase II designs
526
4.
Summary
539
References
541
Ch.
18.
Definitive Phase III and Phase IV Clinical Trials
546
Barry
R
Davis and Sarah Baraniuk
1.
Introduction
546
2.
Questions
548
3.
Randomization
550
4.
Recruitment
551
5.
Adherence/sample size/power
552
6.
Data analysis
554
7.
Data quality and control/data management
558
8.
Data monitoring
558
9.
Phase IV trials
563
10.
Dissemination
-
trial reporting and beyond
564
11.
Conclusions
565
References
565
Ch.
19.
Incomplete Data in Epidemiology and Medical Statistics
569
Susanne Rässler,
Donald
В.
Rubin and Elizabeth R.
Zeil
1.
Introduction
569
2.
Missing-data mechanisms and ignorability
571
3.
Simple approaches to handling missing data
573
4.
Single imputation
574
5.
Multiple imputation
578
6.
Direct analysis using model-based procedures
581
7.
Examples
584
8.
Literature review for epidemiology and medical studies
586
9.
Summary and discussion
587
Appendix A
588
Appendix
В
592
References
598
Ch.
20. Meta-Analysis 602
Edward L. Spitznagel
Jr.
1.
Introduction
602
2.
History
603
3.
The Cochran-Mantel-Haenszel test
604
4.
Glass's proposal for meta-analysis
606
5.
Random effects models
607
6.
The forest plot
609
7.
Publication bias
610
8.
The Cochrane Collaboration
614
References
614
Ch.
21.
The Multiple Comparison Issue in Health Care Research
616
Lemuel
A. Moyé
1.
Introduction
616
2.
Concerns for significance testing
617
3.
Appropriate use of significance testing
618
4.
Definition of multiple comparisons
619
5.
Rational for multiple comparisons
620
6.
Multiple comparisons and analysis triage
621
7.
Significance testing and multiple comparisons
623
8.
Family wise error rate
625
9.
The Bonferroni inequality
626
10.
Alternative approaches
629
11.
Dependent testing
631
12.
Multiple comparisons and combined endpoints
635
13.
Multiple comparisons and subgroup analyses
641
14.
Data dredging
651
References
651
Ch.
22.
Power: Establishing the Optimum Sample Size
656
Richard
A. Zeller
and Yan Yan
1.
Introduction
656
2.
Illustrating power
658
3.
Comparing simulation and software approaches to power
663
4.
Using power to decrease sample size
672
5.
Discussion
677
References
677
Ch.
23.
Statistical Learning in Medical Data Analysis
679
Grace Wahba
1.
Introduction
679
2.
Risk factor estimation: penalized likelihood estimates
681
3.
Risk factor estimation: likelihood basis pursuit and the LASSO
690
4.
Classification: support
vector
machines
and related estimates
693
5.
Dissimilarity
data and kernel
estimates
700
6.
Tuning methods
704
7.
Regularization, empirical
Bayes,
Gaussian processes priors, and
reproducing kernels
707
References
708
Ch.
24.
Evidence Based Medicine and Medical Decision Making
712
Dan Mayer
1.
The definition and history of evidence based medicine
712
2.
Sources and levels of evidence
715
3.
The five stage process of EBM
717
4.
The hierarchy of evidence: study design and minimizing bias
718
5.
Assessing the significance or impact of study results: Statistical significance and
confidence intervals
721
6.
Meta-analysis and systematic reviews
722
7.
The value of clinical information and assessing the usefulness
of a diagnostic test
722
8.
Expected values decision making and the threshold approach
to diagnostic testing
726
9.
Summary
727
10.
Basic principles
727
References
728
Ch.
25.
Estimation of Marginal Regression Models with Multiple
Source Predictors
730
Heather J. Litman, Nicholas J.
Horton,
Bernardo Hernandez and
Nan M. Laird
1.
Introduction
730
2.
Review of the generalized estimating equations approach
732
3.
Maximum likelihood estimation
735
4.
Simulations
737
5.
Efficiency calculations
740
6.
Illustration
741
7.
Conclusion
743
References
745
Ch.
26.
Difference Equations with Public Health Applications
747
Asha Seth Kapadia and Lemuel
A. Moyé
1.
Introduction
747
2.
Generating functions
748
3.
Second-order nonhomogeneous equations and generating functions
750
4.
Example in rhythm disturbances
752
5.
Follow-up losses in clinical trials
758
6.
Applications in epidemiology
765
References
773
Ch.
27.
The Bayesian Approach to Experimental Data Analysis
775
Bruno Lecoutre
Preamble: and if you were a Bayesian without knowing it?
775
1.
Introduction
776
2.
Frequentist and Bayesian inference
778
3.
An illustrative example
783
4.
Other examples of inferences about proportions
795
5.
Concluding remarks and some further topics
803
References
808
Subject Index
813
Handbook of Statistics Contents of Previous Volumes
823 |
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| illustrated | Illustrated |
| index_date | 2024-07-02T19:15:11Z |
| indexdate | 2024-07-09T21:09:17Z |
| institution | BVB |
| isbn | 9780444528018 |
| language | English |
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| spelling | Epidemiology and medical statistics ed. by C. R. Rao ... 1. ed. Amsterdam [u.a.] North-Holland 2008 XVIII, 852 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Handbook of statistics 27 Medizinische Statistik (DE-588)4127563-9 gnd rswk-swf Epidemiologie (DE-588)4015016-1 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Epidemiologie (DE-588)4015016-1 s Medizinische Statistik (DE-588)4127563-9 s DE-188 Rao, Calyampudi Radhakrishna 1920-2023 Sonstige (DE-588)119285924 oth Handbook of statistics 27 (DE-604)BV000002510 27 Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016230244&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Epidemiology and medical statistics Handbook of statistics Medizinische Statistik (DE-588)4127563-9 gnd Epidemiologie (DE-588)4015016-1 gnd |
| subject_GND | (DE-588)4127563-9 (DE-588)4015016-1 (DE-588)4143413-4 |
| title | Epidemiology and medical statistics |
| title_auth | Epidemiology and medical statistics |
| title_exact_search | Epidemiology and medical statistics |
| title_exact_search_txtP | Epidemiology and medical statistics |
| title_full | Epidemiology and medical statistics ed. by C. R. Rao ... |
| title_fullStr | Epidemiology and medical statistics ed. by C. R. Rao ... |
| title_full_unstemmed | Epidemiology and medical statistics ed. by C. R. Rao ... |
| title_short | Epidemiology and medical statistics |
| title_sort | epidemiology and medical statistics |
| topic | Medizinische Statistik (DE-588)4127563-9 gnd Epidemiologie (DE-588)4015016-1 gnd |
| topic_facet | Medizinische Statistik Epidemiologie Aufsatzsammlung |
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