Epidemiology: study design and data analysis
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton [u.a.]
Chapman & Hall/CRC
2014
|
| Ausgabe: | 3. ed. |
| Schriftenreihe: | Chapman & Hall/CRC texts in statistical science series
[64] |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXII, 832 S. graph. Darst. |
| ISBN: | 9781439839706 |
Internformat
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| 245 | 1 | 0 | |a Epidemiology |b study design and data analysis |c Mark Woodward |
| 250 | |a 3. ed. | ||
| 264 | 1 | |a Boca Raton [u.a.] |b Chapman & Hall/CRC |c 2014 | |
| 300 | |a XXII, 832 S. |b graph. Darst. | ||
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| 490 | 1 | |a Chapman & Hall/CRC texts in statistical science series |v [64] | |
| 650 | 7 | |a Epidemiologie |2 gtt | |
| 650 | 7 | |a Epidemiologie |2 swd | |
| 650 | 4 | |a Epidemiología - Métodos estadísticos | |
| 650 | 7 | |a Statistische Analyse |2 swd | |
| 650 | 7 | |a Statistische methoden |2 gtt | |
| 650 | 4 | |a Épidémiologie - Méthodes statistiques | |
| 650 | 4 | |a Data Interpretation, Statistical | |
| 650 | 4 | |a Epidemiologic Methods | |
| 650 | 4 | |a Epidemiology |x Statistical methods | |
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Datensatz im Suchindex
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|---|---|
| adam_text | Table of Contents
1 Fundamental issues 1
1.1 What is epidemiology? 1
1.2 Case studies: the work of Doll and Hill 2
1.3 Populations and samples 6
1.3.1 Populations 6
1.3.2 Samples 7
1.4 Measuring disease 7
1.4.1 Incidence and prevalence 9
1.5 Measuring the risk factor 10
1.6 Causality 11
1.6.1 Association 11
1.6.2 Problems with establishing causality 13
1.6.3 Principles of causality 14
1.7 Studies using routine data 14
1.7.1 Ecological data 15
1.7.2 National sources of data on disease 10
1.7.3 National sources of data on risk factors 17
1.7.4 International data 17
1.8 Study design 1
1.8.1 Intervention studies 1 ^
1.8.2 Observational studies ^
1.9 Data analysis 20
Exercises 21
2 Basic analytical procedures 23
2.1 Introduction 23
2.1.1 Inferential procedures 23
2.2 Case study
2.2.1 The Scottish Heart Health Study 24
2.3 Types of variables ^
2.3.1 Qualitative variables 26
2.3.2 Quantitative variable s 26
2.3.3 The hierarchy of type 26
2.4 Tables and charts
2.4.1 Tables in reports 29
2.4.2 Diagrams in reports
2.5 Inferential techniques for categorical variables 33
2.5.1 Contingency tables ^
EPIDEMIOLOGY: STUDY DESIGN AND DATA ANALYSIS, 3RD EDITION
2.5.2 Binary variables: proportions and percentages 36
2.5.3 Comparing two proportions or percentages 40
2.6 Descriptive techniques for quantitative variables 41
2.6.1 The five-number summary 43
2.6.2 Quantiles 46
2.6.3 The two-number summary 48
2.6.4 Other summary statistics of spread 50
2.6.5 Assessing symmetry 50
2.6.6 Investigating shape 53
2.7 Inferences about means 57
2.7.1 Checking normality 58
2.7.2 Inferences for a single mean 60
2.7.3 Comparing two means 61
2.7.4 Paired data 64
2.8 Inferential techniques for non-normal data 66
2.8.1 Transformations 66
2.8.2 Nonparametric tests 69
2.8.3 Confidence intervals for medians 72
2.9 Measuring agreement 72
2.9.1 Quantitative variables 72
2.9.2 Categorical variables 74
2.9.3 Ordered categorical variables 77
2.9.4 Internal consistency 78
2.10 Assessing diagnostic tests 79
2.10.1 Accounting for sensitivity and specificity 81
Exercises 85
Assessing risk factors 89
3.1 Risk and relative risk 89
3.2 Odds and odds ratio 92
3.3 Relative risk or odds ratio? 94
3.4 Prevalence studies 97
3.5 Testing association 98
3.5.1 Equivalent tests 99
3.5.2 One-sided tests 100
3.5.3 Continuity corrections 101
3.5.4 Fisher’s exact test 102
3.5.5 Limitations of tests 104
3.6 Risk factors measured at several levels 105
3.6.1 Continuous risk factors 107
3.6.2 A test for linear trend 108
3.6.3 A test for nonlinearity 111
3.7 Attributable risk 111
3.8 Rate and relative rate 116
3.8.1 The general epidemiological rate 119
3.9 Measures of difference 119
3.10 EPITAB commands in Stata 120
Exercises 121
TABLE OF CONTENTS
xi
Confounding and interaction 125
4.1 Introduction 125
4.2 The concept of confounding 126
4.3 Identification of confounders 129
4.3.1 A strategy for selection 130
4.4 Assessing confounding 131
4.4.1 Using estimation 131
4.4.2 Using hypothesis tests 132
4.4.3 Dealing with several confounding variables 133
4.5 Standardisation 134
4.5.1 Direct standardisation of event rates 135
4.5.2 Indirect standardisation of event rates 138
4.5.3 Standardisation of risks 141
4.6 Mantel- -Haenszel methods 143
4.6.1 The Mantel-Haenszel relative risk 146
4.6.2 The Cochran-Mantel-Haenszel test 147
4.6.3 Further comments 148
4.7 The concept of interaction 149
4.8 Testing for interaction 151
4.8.1 Using the relative risk 151
4.8.2 Using the odds ratio 156
4.8.3 Using the risk difference 158
4.8.4 Which type of interaction to use? 159
4.8.5 Which interactions to test? 159
4.9 Dealing with interaction 160
4.10 EPITAB commands in Stata 161
Exercises 161
Cohort studies 165
5.1 Design considerations 165
5.1.1 Advantages 165
5.1.2 Disadvantages 165
5.1.3 Alternative designs with economic advantages 167
5.1.4 Studies with a single baseline sample 168
5.2 Analytical considerations 169
5.2.1 Concurrent follow-up 169
5.2.2 Moving baseline dates 170
5.2.3 Varying follow-up durations 170
5.2.4 Withdrawals 172
5.3 Cohort life tables 173
5.3.1 Allowing for sampling variation 175
5.3.2 Allowing for censoring 176
5.3.3 Comparison of two life tables 177
5.3.4 Limitations 180
5.4 Kaplan-Meier estimation 181
5.4.1 An empirical comparison 182
5.5 Comparison of two sets of survival probabilities 184
5.5.1 Mantel-Haenszel methods 184
EPIDEMIOLOGY: STUDY DESIGN AND DATA ANALYSIS, 3RD EDITION
xii
5.5.2 The log-rank test 186
5.5.3 Weighted log-rank tests 188
5.5.4 Allowing for confounding variables 190
5.5.5 Comparing three or more groups 190
5.6 Competing risk 190
5.7 The person-years method 193
5.7.1 Age-specific rates 194
5.7.2 Summarisation of rates 196
5.7.3 Comparison of two SERs 197
5.7.4 Mantel-Haenszel methods 199
5.7.5 Further comments 202
5.8 Period-cohort analysis 203
5.8.1 Period-specific rates 204
Exercises 206
Case-control studies 211
6.1 Basic design concepts 211
6.1.1 Advantages 211
6.1.2 Disadvantages 212
6.2 Basic methods of analysis 214
6.2.1 Dichotomous exposure 214
6.2.2 Polytomous exposure 217
6.2.3 Confounding and interaction 218
6.2.4 Attributable risk 218
6.3 Selection of cases 220
6.3.1 Definition 220
6.3.2 Inclusion and exclusion criteria 220
6.3.3 Incident or prevalent? 221
6.3.4 Source 221
6.3.5 Consideration of bias 221
6.4 Selection of controls 222
6.4.1 General principles 222
6.4.2 Hospital controls 224
6.4.3 Community controls 226
6.4.4 Other sources 227
6.4.5 How many? 228
6.5 Matching 229
6.5.1 Advantages 229
6.5.2 Disadvantages 230
6.5.3 One-to-many matching 231
6.5.4 Matching in other study designs 231
6.6 The analysis of matched studies 231
6.6.1 1 : 1 Matching 232
6.6.2 1 : c Matching 234
6.6.3 1 : Variable matching 240
6.6.4 Many : many matching 242
6.6.5 A modelling approach 245
TABLE OF CONTENTS
xiii
6.7 Nested case—control studies 245
6.7.1 Matched studies 247
6.7.2 Counter-matched studies 248
6.8 Case-cohort studies 248
6.9 Case-crossover studies 250
Exercises 251
7 Intervention studies 257
7.1 Introduction 257
7.1.1 Advantages 259
7.1.2 Disadvantages 259
7.2 Ethical considerations 259
7.2.1 The protocol 260
7.3 Avoidance of bias 261
7.3.1 Use of a control group 261
7.3.2 Blindness 262
7.3.3 Randomisation 263
7.3.4 Consent before randomisation 264
7.3.5 Analysis by intention-to-treat 265
7.4 Parallel group studies 265
7.4.1 Number needed to treat 268
7.4.2 Cluster randomised trials 270
7.4.3 Stepped wedge trials 270
7.4.4 Non-inferiority trials 271
7.5 Cross-over studies 273
7.5.1 Graphical analysis 275
7.5.2 Comparing means 277
7.5.3 Analysing preferences 282
7.5.4 Analysing binary data 283
7.6 Sequential studies 284
7.6.1 The Haybittle-Peto stopping rule 285
7.6.2 Adaptive designs 286
7.7 Allocation to treatment group 286
7.7.1 Global randomisation 286
7.7.2 Stratified randomization 288
7.7.3 Implementation 291
7.8 Trials as cohorts 291
Exercises 291
8 Sample size determination 295
8.1 Introduction 295
8.2 Power 296
8.2.1 Choice of alternative hypothesis 300
8.3 Testing a mean value 303
8.3.1 Common choices for power and significance level 305
8.3.2 Using a table of sample sizes 305
XIV
EPIDEMIOLOGY: STUDY DESIGN AND DATA ANALYSIS, 3RD EDITION
8.3.3 The minimum detectable difference 306
8.3.4 The assumption of known standard deviation 307
8.4 Testing a difference between means 307
8.4.1 Using a table of sample sizes 308
8.4.2 Power and minimum detectable difference 310
8.4.3 Optimum distribution of the sample 310
8.4.4 Paired data 311
8.5 Testing a proportion 311
8.5.1 Using a table of sample sizes 312
8.6 Testing a relative risk 313
8.6.1 Using a table of sample sizes 315
8.6.2 Power and minimum detectable relative risk 316
8.7 Case---control studies 317
8.7.1 Using a table of sample sizes 319
8.7.2 Power and minimum detectable relative risk 319
8.7.3 Comparison with cohort studies 321
8.7.4 Matched studies 321
8.8 Complex sampling designs 324
8.9 Concluding remarks 325
Exercises 326
Modelling quantitative outcome variables 331
9.1 Statistical models 331
9.2 One categorical explanatory variable 332
9.2.1 The hypotheses to be tested 332
9.2.2 Construction of the ANOVA table 333
9.2.3 How the ANOVA table is used 336
9.2.4 Estimation of group means 336
9.2.5 Comparison of group means 337
9.2.6 Fitted values 338
9.2.7 Using computer packages 341
9.3 One quantitative explanatory variable 344
9.3.1 Simple linear regression 344
9.3.2 Correlation 352
9.3.3 Nonlinear regression 355
9.4 Two categorical explanatory variables 358
9.4.1 Model specification 358
9.4.2 Model fitting 359
9.4.3 Balanced data 359
9.4.4 Unbalanced data 359
9.4.5 Fitted values 362
9.4.6 Least squares means 363
9.4.7 Interaction 364
9.5 Model building 365
9.6 General linear models 371
9.7 Several explanatory variables 377
9.7.1 Information criteria 381
9.7.2 Boosted regression 383
TABLE OF CONTENTS
xv
9.8 Model checking 383
9.9 Confounding 387
9.9.1 Adjustment using residuals 391
9.10 Splines 392
9.10.1 Choice of knots 395
9.10.2 Other types of splines 396
9.11 Panel data 398
9.12 Non-normal alternatives 402
Exercises 404
10 Modelling binary outcome data 409
10.1 Introduction 409
10.2 Problems with standard regression models 412
10.2.1 The r—x relationship may well not be linear 412
10.2.2 Predicted values of the risk may be outside the valid range 412
10.2.3 The error distribution is not normal 412
10.3 Logistic regression 413
10.4 Interpretation of logistic regression coefficients 415
10.4.1 Binary risk factors 415
10.4.2 Quantitative risk factors 417
10.4.3 Categorical risk factors 419
10.4.4 Ordinal risk factors 424
10.4.5 Floating absolute risks 425
10.5 Generic data 427
10.6 Multiple logistic regression models 428
10.7 Tests of hypotheses 432
10.7.1 Goodness of fit for grouped data 433
10.7.2 Goodness of fit for generic data 435
10.7.3 Effect of a risk factor 435
10.7.4 Information criteria 438
10.7.5 Tests for linearity and nonlinearity 440
10.7.6 Tests based upon estimates and their standard errors 443
10.7.7 Problems with missing values 444
10.8 Confounding 444
10.9 Interaction 44^
10.9.1 Between two categorical variables 44$
10.9.2 Between a quantitative and a categorical variable 449
10.9.3 Between two quantitative variables 4^2
10.10 Dealing with a quantitative explanatory variable 452
10.10.1 Linear form 4^3
10.10.2 Categorical form 4®^
10.10.3 Linear spline form 4^°
10.10.4 Generalisations 4^
10.11 Model checking 4^
10.11.1 Residuals 4^9
10.11.2 Influential observations 4^^
10.12 Measurement error 4®2
XVI
EPIDEMIOLOGY: STUDY DESIGN AND DATA ANALYSIS, 3RD EDITION
10.12.1 Regression to the mean 463
10.12.2 Correcting for regression dilution 465
10.13 Case—control studies 467
10.13.1 Unmatched studies 467
10.13.2 Matched studies 468
10.14 Outcomes with several levels 469
10.14.1 The proportional odds assumption 471
10.14.2 The proportional odds model 473
10.14.3 Multinomial regression 475
10.15 Longitudinal data 475
10.16 Binomial regression 476
10.16.1 Adjusted risks 479
10.16.2 Risk differences 483
10.16.3 Problems with binomial models 484
10.17 Propensity scoring 488
10.17.1 Pair-matched propensity scores 488
10.17.2 Stratified propensity scores 489
10.17.3 Weighting by the inverse propensity score 490
10.17.4 Adjusting for the propensity score 491
10.17.5 Deriving the propensity score 492
10.17.6 Propensity score outliers 493
10.17.7 Conduct of the matched design 493
10.17.8 Analysis of the matched design 494
10.17.9 Case studies 495
10.17.10 Interpretation of effects 498
10.17.11 Problems with estimating uncertainty 499
10.17.12 Propensity scores in practice 499
Exercises 501
11 Modelling follow-up data 507
11.1 Introduction 507
11.1.1 Models for survival data 50 7
11.2 Basic functions of survival time 507
11.2.1 The survival function 507
11.2.2 The hazard function 507
11.3 Estimating the hazard function 508
11.3.1 Kaplan-Meier estimation 508
11.3.2 Person-time estimation 510
11.3.3 Actuarial estimation 511
11.3.4 The cumulative hazard 512
11.4 Probability models 512
11.4.1 The probability density and cumulative distribution functions 512
11.4.2 Choosing a model 514
11.4.3 The exponential distribution 514
11.4.4 The Weibull distribution 517
11.4.5 Other probability models 520
11.5 Proportional hazards regression models 521
TABLE OF CONTENTS xvü
11.5.1 Comparing two groups 521
11.5.2 Comparing several groups 521
11.5.3 Modelling with a quantitative variable 523
11.5.4 Modelling with several variables 524
11.5.5 Left-censoring 525
11.6 The Cox proportional hazards model 526
11.6.1 Time-dependent covariates 535
11.6.2 Recurrent events 536
11.7 The Weibull proportional hazards model 536
11.8 Model checking 541
11.8.1 Log cumulative hazard plots 541
11.8.2 An objective test of proportional hazards for
the Cox model 545
11.8.3 An objective test of proportional hazards for the
Weibull model 545
11.8.4 Residuals and influence 546
11.8.5 Nonproportional hazards 546
11.9 Competing risk 546
11.9.1 Joint modeling of longitudinal and survival data 548
11.10 Poisson regression 549
11.10.1 Simple regression 550
11.10.2 Multiple regression 553
11.10.3 Comparison of standardised event ratios 555
11.10.4 Routine or registration data 556
11.10.5 Generic data 558
11.10.6 Model checking 559
11.11 Pooled logistic regression 559
Exercises 561
12 Meta-analysis 565
12.1 Reviewing evidence 565
12.1.1 The Cochrane Collaboration 567
12.2 Systematic review 567
12.2.1 Designing a systematic review 567
12.2.2 Study quality 571
12.3 A general approach to pooling 572
12.3.1 Inverse variance weighting 573
12.3.2 Fixed effect and random effects 573
12.3.3 Quantifying heterogeneity 574
12.3.4 Estimating the between-study variance 576
12.3.5 Calculating inverse variance weights 577
12.3.6 Calculating standard errors from confidence intervals 577
12.3.7 Case studies ^78
12.3.8 Pooling risk differences 582
12.3.9 Pooling differences in mean values 583
12.3.10 Other quantities ^83
12.3.11 Pooling mixed quantities
12.3.12 Dose-response meta-analysis 584
XV111
EPIDEMIOLOGY: STUDY DESIGN AND DATA ANALYSIS, 3RD EDITION
12.4 Investigating heterogeneity 584
12.4.1 Forest plots 585
12.4.2 Influence plots 586
12.4.3 Sensitivity analyses 588
12.4.4 Meta-regression 588
12.5 Pooling tabular data 591
12.5.1 Inverse variance weighting 591
12.5.2 Mantel-Haenszel methods 591
12.5.3 The Peto method 592
12.5.4 Dealing with zeros 592
12.5.5 Advantages and disadvantages of using tabular data 593
12.6 Individual participant data 593
12.7 Dealing with aspects of study quality 594
12.8 Publication bias 595
12.8.1 The funnel plot 596
12.8.2 Consequences of publication bias 597
12.8.3 Correcting for publication bias 597
12.8.4 Other causes of asymmetry in funnel plots 599
12.9 Advantages and limitations of meta-analysis 600
Exercises 600
13 Risk scores and clinical decision rules 605
13.1 Introduction 605
13.1.1 Individual and population level interventions 605
13.1.2 Scope of this chapter 607
13.2 Association and prognosis 608
13.2.1 The concept of discrimination 610
13.2.2 Risk factor thresholds 611
13.2.3 Risk thresholds 615
13.2.4 Odds ratios and discrimination 616
13.3 Risk scores from statistical models 618
13.3.1 Logistic regression 618
13.3.2 Multiple variable risk scores 620
13.3.3 Cox regression 621
13.3.4 Risk thresholds 623
13.3.5 Multiple thresholds 624
13.4 Quantifying discrimination 625
13.4.1 The area under the curve 626
13.4.2 Comparing AUCs 629
13.4.3 Survival data 631
13.4.4 The standardised mean effect size 632
13.4.5 Other measures of discrimination 637
13.5 Calibration 637
13.5.1 Overall calibration 638
13.5.2 Mean calibration 638
13.5.3 Grouped calibration 639
13.5.4 Calibration plots 641
TABLE OF CONTENTS xix
13.6 Recalibration 643
13.6.1 Recalibration of the mean 643
13.6.2 Recalibration of scores in a fixed cohort 643
13.6.3 Recalibration of parameters from a Cox model 646
13.6.4 Recalibration and discrimination 647
13.7 The accuracy of predictions 648
13.7.1 The Brier score 648
13.7.2 Comparison of Brier scores 650
13.8 Assessing an extraneous prognostic variable 651
13.9 Reclassification 652
13.9.1 The integrated discrimination improvement from
a fixed cohort 653
13.9.2 The net reclassification improvement from a fixed cohort 656
13.9.3 The integrated discrimination improvement from
a variable cohort 659
13.9.4 The net reclassification improvement from a
variable cohort 660
13.9.5 Software 662
13.10 Validation 662
13.11 Presentation of risk scores 663
13.11.1 Point scoring 664
13.12 Impact studies 674
Exercises 675
14 Computer-intensive methods 679
14.1 Rationale 679
14.2 The bootstrap 679
14.2.1 Bootstrap distributions 681
14.3 Bootstrap confidence intervals 684
14.3.1 Bootstrap normal intervals 685
14.3.2 Bootstrap percentile intervals 686
14.3.3 Bootstrap bias-corrected intervals 688
14.3.4 Bootstrap bias-corrected and accelerated intervals 690
14.3.5 Overview of the worked example 691
14.3.6 Choice of bootstrap interval 692
14.4 Practical issues when bootstrapping 692
14.4.1 Software 692
14.4.2 How many replications should be used? 693
14.4.3 Sensible strategies 696
14.5 Further examples of bootstrapping 696
14.5.1 Complex bootstrap samples 701
14.6 Bootstrap hypothesis testing 703
14.7 Limitations of bootstrapping 705
14.8 Permutation tests 706
14.8.1 Monte Carlo permutation tests 707
14.8.2 Limitations 709
14.9 Missing values 709
XX
EPIDEMIOLOGY: STUDY DESIGN AND DATA ANALYSIS, 3RD EDITION
14.9.1 Dealing with missing values 711
14.9.2 Types of missingness 713
14.9.3 Complete case analyses 714
14.10 Naive imputation methods 716
14.10.1 Mean imputation 716
14.10.2 Conditional mean and regression imputation 716
14.10.3 Hot deck imputation and predictive mean matching 718
14.10.4 Longitudinal data 719
14.11 Univariate multiple imputation 720
14.11.1 Multiple imputation by regression 720
14.11.2 The three-step process in MI 721
14.11.3 Imputer’s and analyst’s models 722
14.11.4 Rubin’s equations 723
14.11.5 Imputation diagnostics 728
14.11.6 Skewed continuous data 729
14.11.7 Other types of variables 731
14.11.8 How many imputations? 731
14.12 Multivariate multiple imputation 733
14.12.1 Monotone imputation 733
14.12.2 Data augmentation 734
14.12.3 Categorical variables 742
14.12.4 What to do when DA fails 742
14.12.5 Chained equations 743
14.12.6 Longitudinal data 747
14.13 When is it worth imputing? 747
Exercises 748
Appendix A Materials available on the website for this book 755
Appendix B Statistical tables 759
Appendix C Additional datasets for exercises 785
References 799
Index
821
|
| any_adam_object | 1 |
| author | Woodward, Mark |
| author_GND | (DE-588)128378735 |
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| dewey-full | 614.4/07/27 |
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| dewey-ones | 614 - Forensic medicine; incidence of disease |
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| dewey-sort | 3614.4 17 227 |
| dewey-tens | 610 - Medicine and health |
| discipline | Mathematik Medizin |
| edition | 3. ed. |
| format | Book |
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| id | DE-604.BV042283116 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T01:17:15Z |
| institution | BVB |
| isbn | 9781439839706 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027720445 |
| oclc_num | 915556146 |
| open_access_boolean | |
| owner | DE-83 DE-473 DE-BY-UBG |
| owner_facet | DE-83 DE-473 DE-BY-UBG |
| physical | XXII, 832 S. graph. Darst. |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Chapman & Hall/CRC |
| record_format | marc |
| series | Chapman & Hall/CRC texts in statistical science series |
| series2 | Chapman & Hall/CRC texts in statistical science series |
| spelling | Woodward, Mark Verfasser (DE-588)128378735 aut Epidemiology study design and data analysis Mark Woodward 3. ed. Boca Raton [u.a.] Chapman & Hall/CRC 2014 XXII, 832 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC texts in statistical science series [64] Epidemiologie gtt Epidemiologie swd Epidemiología - Métodos estadísticos Statistische Analyse swd Statistische methoden gtt Épidémiologie - Méthodes statistiques Data Interpretation, Statistical Epidemiologic Methods Epidemiology Statistical methods Medizinische Statistik (DE-588)4127563-9 gnd rswk-swf Epidemiologie (DE-588)4015016-1 gnd rswk-swf Epidemiologie (DE-588)4015016-1 s Medizinische Statistik (DE-588)4127563-9 s DE-604 Chapman & Hall/CRC texts in statistical science series [64] (DE-604)BV022819715 64 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027720445&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Woodward, Mark Epidemiology study design and data analysis Chapman & Hall/CRC texts in statistical science series Epidemiologie gtt Epidemiologie swd Epidemiología - Métodos estadísticos Statistische Analyse swd Statistische methoden gtt Épidémiologie - Méthodes statistiques Data Interpretation, Statistical Epidemiologic Methods Epidemiology Statistical methods Medizinische Statistik (DE-588)4127563-9 gnd Epidemiologie (DE-588)4015016-1 gnd |
| subject_GND | (DE-588)4127563-9 (DE-588)4015016-1 |
| title | Epidemiology study design and data analysis |
| title_auth | Epidemiology study design and data analysis |
| title_exact_search | Epidemiology study design and data analysis |
| title_full | Epidemiology study design and data analysis Mark Woodward |
| title_fullStr | Epidemiology study design and data analysis Mark Woodward |
| title_full_unstemmed | Epidemiology study design and data analysis Mark Woodward |
| title_short | Epidemiology |
| title_sort | epidemiology study design and data analysis |
| title_sub | study design and data analysis |
| topic | Epidemiologie gtt Epidemiologie swd Epidemiología - Métodos estadísticos Statistische Analyse swd Statistische methoden gtt Épidémiologie - Méthodes statistiques Data Interpretation, Statistical Epidemiologic Methods Epidemiology Statistical methods Medizinische Statistik (DE-588)4127563-9 gnd Epidemiologie (DE-588)4015016-1 gnd |
| topic_facet | Epidemiologie Epidemiología - Métodos estadísticos Statistische Analyse Statistische methoden Épidémiologie - Méthodes statistiques Data Interpretation, Statistical Epidemiologic Methods Epidemiology Statistical methods Medizinische Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027720445&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV022819715 |
| work_keys_str_mv | AT woodwardmark epidemiologystudydesignanddataanalysis |