Statistical reasoning in medicine: the intuitive P-value primer
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York
Springer
2006
|
| Ausgabe: | 2. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | XX, 301 S. Ill. |
| ISBN: | 0387329137 9780387329130 |
Internformat
MARC
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| 245 | 1 | 0 | |a Statistical reasoning in medicine |b the intuitive P-value primer |c Lemuel A. Moyé |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a New York |b Springer |c 2006 | |
| 300 | |a XX, 301 S. |b Ill. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 0 | 7 | |a Medizinische Statistik |0 (DE-588)4127563-9 |2 gnd |9 rswk-swf |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-020514481 | ||
Datensatz im Suchindex
| _version_ | 1804143192133599232 |
|---|---|
| adam_text | Contents
PREFACE
...........................................................................................
VII
Acknowledgments
........................................................................
їх
Contents
.........................................................................................
χι
Introduction
..............................................................................xvii
Prologue
..........................................................................................1
Europe s Emergence from the Middle Ages
.................................../
Absolutism
......................................................................................3
Refusing to be Counted
..................................................................4
No Need for Probability
.................................................................5
Intellectual Triumph: The Industrial Revolution
...........................6
Reasoning from a Sample
..............................................................7
Political Arithmetic
........................................................................8
The Role of Religion in Political Arithmetic
..................................8
Probability and the Return to Order
............................................10
Let Others Thrash It Out!
........................................................11
Early Experimental Design
..........................................................11
Agricultural Articulations
............................................................12
James Johnson
.....................................................................................13
Fisher,
Gösset,
and Modern Experimental Design
......................13
References
....................................................................................14
1.
The Basis of Statistical Reasoning in Medicine
.................17
1.1
What Is Statistical Reasoning?
...........................................17
1.1.2
Physicians and the Patient Perspective
.......................................18
1.1.2
Research and the Population Perspective
...................................19
1.1.3
The Need for Integration
............................................................20
1.1.4
A Trap
........................................................................................20
1.1.5
Clinical Versus Research Skills
..................................................21
1.2
Statistical Reasoning.
.............................................................22
1.2.1
The Great Compromise
..............................................................23
1.2.2
Example of Sampling Error
........................................................24
1.2.3
PRAISE I and II
.........................................................................26
1.2.4
Fleas
...........................................................................................28
1.3
Generalizations to Populations
..............................................29
1.3.1
Advantages of the Random Sample
............................................29
1.3.2
Limitations of the Random Sample
............................................30
1.3.3
Example: Salary Estimation
.......................................................31
1.3.4
Difficulty
1:
Sample Size and Missing Data
..............................32
1.3.5
Sample Vision
............................................................................33
References
....................................................................................55
Statistical Reasoning in Medicine: The Intuitive P-value Primer
2.
Search Versus Research
.........................................................37
2.1
Introduction
...........................................................................37
2.2
Catalina s Dilemma
...............................................................37
2.2.1.
Can
Catalina
Generalize?
..........................................................37
2.2.2
Do Logistical Issues Block Generalization?
...............................38
2.2.3
Disturbed Estimators
..................................................................38
2.3
Exploratory Analysis and Random Research
.........................40
2.4
Gender-Salary Problem Revisited
.........................................42
2.5
Exploratory Versus Confirmatory
..........................................44
2.5.1
Cloak of Confirmation
................................................................45
2.6
Exploration andMRFIT.
........................................................46
2.7
Exploration in the ELITE Trials
............................................47
2.8
Necessity of Exploratory Analyses
.........................................48
2.8.1
Product 2254RP
..........................................................................49
2.8.2
The Role of Discovery Versus Confirmation
.............................49
2.8.3
Tools of Exploration
...................................................................50
2.9
Prospective Plans and Calling Your Shot
.........................50
2.9.1
The US Carvedilol Program
.......................................................51
2.9.2
Let the Data Decide!
...................................................................54
2.10
Tight Protocols
....................................................................55
2.11
Design Manuscripts
.............................................................56
2.12
Concordant Versus Discordant Research
............................57
2.12.1
Severe Discordance: Mortality Corruption
...............................57
2.12.2
Severe Discordance: Medication Changes
...............................58
2.12.3
Discordance and NSABP
.........................................................59
2.13
Conclusions
..........................................................................59
References
....................................................................................60
3.
A Hypothesis-Testing Primer
..................................................63
5.7
Introduction
...........................................................................63
3.2
The Rubric of Hypothesis Testing
..........................................64
3.3
The Normal Distribution and Its Estimators
..........................65
3.4
Using the Normal Distribution
..............................................66
3.4.1
Simplifying Transformations
......................................................66
3.4.3
Symmetry
...................................................................................68
3.5
The Null Hypothesis: State of the Science
..............................70
3.6
Type II Error and Power.
.......................................................76
3.7
Balancing Alpha and Beta
.....................................................80
3.8
Reducing Alpha and Beta: The Sample Size
..........................81
3.9
Two-Sided Testing.
.................................................................83
3.10
Sampling Error Containment
...............................................86
3.11
Confidence Intervals
............................................................87
3.12
Hypothesis Testing in Intervention Studies
..........................89
3.13
Community Responsibility
...................................................89
4.
Mistaken Identity: P-values in Epidemiology
.....................91
4.1
Mistaken Identity.
...................................................................91
4.2
Detective Work
........................................... ...........................91
4.2.1
Association versus Causation
.....................................................92
4.3
Experimental Versus Observational Studies
..........................92
Introduction
xiii
4.3.1
The Role of Randomization
........................................................92
4.3.2
Observational Studies
.................................................................94
4.4
Determining Causation
..........................................................95
4.4.1
Causality Tenets
.........................................................................96
4.5
Clinical Significance Without
Ρ
-Values
.................................98
4.5.1
Thalidomide
...............................................................................98
4.5.2
The Radium Girls
.......................................................................99
4.6
Tools of the Epidemiologist
.................................................100
4.6.1
Case Reports and Case Series
...................................................100
4.6.2
Categories of Observational Studies
.................................104
4.6.2.1
Directionality: Forward or Backward?
..................................104
4.6.2.2
Retrospective Versus Prospective
..........................................105
4.6.3
Variable Adjustments
...............................................................107
4.7
F
enfluramines
......................................................................108
4.8
Design Considerations
.........................................................109
4.9
Solid Structures from Imperfect BricL·
................................110
4.10
Drawing Inferences in Epidemiology
................................
Ill
4.11
Study counting: The ceteris
paribus
fallacy
.......................112
4.12
Critiquing Experimental Designs
.......................................113
4.13
Conclusions
........................................................................113
References
..................................................................................114
5.
Shrine Worship
........................................................................117
5./
Introduction
.........................................................................117
5.2
The Nightmare
.....................................................................117
5.3
P-value Bashing
...................................................................118
5.4
Epidemiology and Biostatistics
............................................118
5.4.1
The Link Between Epidemiology and Statistics
.......................119
5.4.2.
Exposure-Disease Relationships
.............................................119
5.5
The Initial Schism
................................................................120
5.6
Appearance of Statistical Significance
................................122
5.6.1
Introducing the
0.05
Level
.......................................................122
5.6.2
Dangerous Nonsense
.............................................................124
5.7
The P-value Love Affair in Healthcare
................................126
5.8
Use and Abuse ofP-values
..................................................127
5.8.1
Confidence Intervals as False Substitution
...............................129
5.8.2
War Too Important to be Left to the Generals?
........................130
5.9
Proper Research Interpretation
...........................................131
References
..................................................................................133
6.
P-v
alues,
Power, and Efficacy
............................................137
6.1
Introduction
.........................................................................137
6.2
P-values and Strength of Evidence
......................................137
6.2.1
Example
...................................................................................138
6.3
Power
...................................................................................141
6.4
No Way Out?
........................................................................143
6.5
Sample Size Computations
...................................................144
6.5.1
The Anvil
.................................................................................144
6.6.
Non-statistical Considerations
............................................145
6.6.1
The LRC Sample Size
..............................................................146
xiv
Statistical Reasoning in Medicine: The Intuitive P-value Primer
6.7
The Good Enough for Them Approach
............................147
6.8
Efficacy Seduction
................................................................147
6.8.1
Efficacy and Sample Size
....................................,....................148
6.8.2.
Large P-values and Small Effect Sizes
....................................149
6.9
Number Needed To Treat
.....................................................150
6.10
Absolute versus Relative Risk
............................................151
6.11
Matching Statistical with Clinical Significance
.................153
6.12
Power for Smaller Efficacy Levels
.....................................155
6.13
Conclusions
........................................................................156
References
..................................................................................156
1.
Scientific Reasoning, P-values, and the Court
................157
7.1
Introduction
.........................................................................157
7.2
Blood Pressure and Deception: The Frye Test
....................158
7.3
Rule
402...............................................................................159
7.4
The Daubert Rulings
............................................................160
7.5
The Havner Ruling
...............................................................161
7.6
Relative Risk and the Supreme Court
..................................163
7.7
P-values, Confidence Intervals, and the Courts
...................164
7.8
Conclusions
..........................................................................165
References
..................................................................................165
8.
One-Sided Versus Two-Sided Testing
.................................167
8.1
Introduction
.........................................................................167
8.2
Attraction of One-Sided Testing
...........................................167
8.3
Belief Versus Knowledge in Healthcare
..............................167
8.4
Belief Systems and Research Design
...................................168
8.5
Statistical Versus Ethical Optimization
...............................169
8.6
Blinded by the Light : CAST.
............................................170
8.7
LRC Results
.........................................................................173
8.8
Sample Size Issues
...............................................................174
8.9
Hoping for the Best, Preparing for the Worst
......................
Π
5
8.10.
Symmetries versus Ethics
..................................................176
8.11
Conclusions
........................................................................179
References
..................................................................................180
9.
Multiple Testing and Combined
Endpoints........................181
9.1
Introduction
.........................................................................181
9.2
Definition of Multiple Analyses
............................................182
9.3
Efficiency Versus Parsimony
.......... ..................182
9.3.1
Efficiency
.................................................................................183
9.3.2 Epidemiologie
Strength
............................................................183
9.3.3
The Need To Explore
..................
ľZľZľľ ľlľ
................
I84
9.4
Hypothesis Testing in Multiple Analyses
.............................
1S4
9.4.1
Don t Ask, Don t Tell
..............................................................184
9.5
Familywise Error
................................................................186
9.7
The Bonferr
oni
Inequality
....................................................186
9.8
Is Tight Control of the FWER Necessary?
...........................188
9.9
Alternative Approaches
........................................................190
Introduction
xv
9.9.1
Sequentially Rejective Procedures
...........................................190
9.9.2
Resampling P-values
................................................................190
9.10
Analysis Triage
..................................................................191
9.10.1
Primary Versus Secondary Analyses
......................................192
9.10.2
Secondary Analyses
...............................................................192
9.10.3
Example of
Endpoint
Triaging
...............................................192
9.11
Combined
Endpoints..........................................................194
9.12
Why Use Combined
Endpoints...........................................194
9.12.1 Epidemiologie
Considerations
................................................195
9.12.2
Sample Size Concerns
............................................................195
9.12.3
Improved Resolving Power
....................................................195
9.13
Combined
Endpoint
Construction
......................................196
9.13.1
Property
1:
Coherence
............................................................196
9.13.2
Property
2:
Equivalence
.........................................................197
9.13.3
Therapy Homogeneity
............................................................198
9.14
Measuring Combined
Endpoints........................................199
9.14.1
Prospective
Identification
.......................................................199
9.14.2
Ascertaining
Endpoints..........................................................200
9.15
Conclusions
........................................................................201
References
..................................................................................202
10.
Subgroup Analyses
..............................................................205
10.1
Bona Fide
Gems or Fool s Gold.
.......................................205
10.2
What Are Subgroups?
........................................................205
10.3
The Amlopidine Controversy
.............................................206
10.4
Definitions
..........................................................................208
10.5
Interpretation Difficulties
..................................................208
10.5.1
Re-evaluating the Same Patients
............................................208
10.5.2.
Random Findings
..................................................................209
10.5.3
Clinical Trial-Mediated Subgroup Effects
..........................211
10.5.4
The Importance of Replication
...............................................215
10.6
Stratified Randomization
...................................................216
10.7
Proper Versus Improper Subgroups
..................................217
10.8
Intention-to-Treat Versus As Treated
........................218
10.9
Effect Domination Principle
..............................................219
10.10
Confirmatory Subgroup Analyses
....................................220
10.11
Assessment of Subgroup Effects
.......................................221
10.11.1
Effect Modification and Interactions
....................................221
10.11.2
Within-Stratum Effects
.........................................................222
10.12
Data Dredging
—
Caveat Emptor
...................................224
10.13
Conclusions
......................................................................224
References
..................................................................................225
11.
P-values and Regression Analyses
...................................229
11.1
The New Meaning of Regression
.......................................229
11.2
Assumptions in Regression Analysis
..................................230
11.3
Model Estimation
...............................................................231
11.3.1
Cross-Sectional Versus Longitudinal
.....................................232
11.4
Variance Partitioning
........................................................233
11.5
Enter Dichotomous Explainer Variables
...........................236
11.6
The Meaning of Adjustment
...........................................237
Statistical Reasoning in Medicine: The Intuitive P-value Primer
11.7
Super Fits
...........................................................................239
11.8
Pearls of Great Price
.........................................................241
11.9
Effect Modifiers and Alpha Allocation
...............................242
11.9.1
Effect Modification Models
...................................................242
11.9.2
The Difficulty of Effect Modification
....................................244
11.10
Conclusions
......................................................................247
12.
Bayesian Analysis: Posterior P- values
...........................249
12.1
An Arbitrary Process
.........................................................249
12.2
The Frequentists
................................................................250
12.2.1
The
Frequentisi
and
Long-Term
Accuracy
.............................250
12.2.2
Reverse Perspective
................................................................250
12.2.3
The Likelihood Principle
........................................................251
12.3
The Bayesian Philosophy
...................................................255
12.3.1
Bayes
and Heart Failure
.........................................................255
12.4
Feasibility of Prior Distributions
.......................................263
12.5
The Loss Function
..............................................................265
12.6
Example of Bayesian Estimation
........................................265
12.7
Bayesians and P-values
.....................................................266
12.8
Bayes
Testing: Asthma Prevalence
....................................267
12.9
Conclusions
........................................................................271
References
..................................................................................272
Conclusions:
...............................................................................273
Appendix A. Standard Normal Probabilities
........................279
Appendix B: Sample Size Primer
................................................283
B.I General Discussion of Sample Size
.....................................283
B.2 Derivation of Sample Size
...................................................285
B.2.1 Phase
1:
Under the Null Hypothesis
........................................286
B.2.2 Phase
2:
Under the Alternative Hypothesis
.............................286
B.2.3 Phase
3:
Consolidation
............................................................287
B.3 Example
...............................................................................288
B.4 Continuous Outcomes
..........................................................289
B.4.1 Phase I
:
The Null Hypothesis
................................................290
B.4.2 Phase II: The Alternative Hypothesis
......................................290
B.4.3 Phase III: Consolidation
..........................................................291,
B.4.4 Example
...................................................................................292
References
..................................................................................293
Appendix C: Daubert and Rule
702
Factors
..........................295
C.I The Daubert Factors
...........................................................295
C.2 The
702
Factors
..................................................................295
Index
.............................................................................................297
|
| any_adam_object | 1 |
| author | Moyé, Lemuel A. 1952- |
| author_GND | (DE-588)122260651 |
| author_facet | Moyé, Lemuel A. 1952- |
| author_role | aut |
| author_sort | Moyé, Lemuel A. 1952- |
| author_variant | l a m la lam |
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| bvnumber | BV036593829 |
| classification_rvk | XF 3500 |
| ctrlnum | (OCoLC)637726317 (DE-599)BVBBV036593829 |
| discipline | Medizin |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV036593829 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:43:45Z |
| institution | BVB |
| isbn | 0387329137 9780387329130 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020514481 |
| oclc_num | 637726317 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR |
| owner_facet | DE-355 DE-BY-UBR |
| physical | XX, 301 S. Ill. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Springer |
| record_format | marc |
| spelling | Moyé, Lemuel A. 1952- Verfasser (DE-588)122260651 aut Statistical reasoning in medicine the intuitive P-value primer Lemuel A. Moyé 2. ed. New York Springer 2006 XX, 301 S. Ill. txt rdacontent n rdamedia nc rdacarrier Medizinische Statistik (DE-588)4127563-9 gnd rswk-swf p-Wert (DE-588)4605821-7 gnd rswk-swf Medizinische Statistik (DE-588)4127563-9 s p-Wert (DE-588)4605821-7 s DE-604 text/html http://deposit.dnb.de/cgi-bin/dokserv?id=2788986&prov=M&dok_var=1&dok_ext=htm Inhaltstext Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020514481&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Moyé, Lemuel A. 1952- Statistical reasoning in medicine the intuitive P-value primer Medizinische Statistik (DE-588)4127563-9 gnd p-Wert (DE-588)4605821-7 gnd |
| subject_GND | (DE-588)4127563-9 (DE-588)4605821-7 |
| title | Statistical reasoning in medicine the intuitive P-value primer |
| title_auth | Statistical reasoning in medicine the intuitive P-value primer |
| title_exact_search | Statistical reasoning in medicine the intuitive P-value primer |
| title_full | Statistical reasoning in medicine the intuitive P-value primer Lemuel A. Moyé |
| title_fullStr | Statistical reasoning in medicine the intuitive P-value primer Lemuel A. Moyé |
| title_full_unstemmed | Statistical reasoning in medicine the intuitive P-value primer Lemuel A. Moyé |
| title_short | Statistical reasoning in medicine |
| title_sort | statistical reasoning in medicine the intuitive p value primer |
| title_sub | the intuitive P-value primer |
| topic | Medizinische Statistik (DE-588)4127563-9 gnd p-Wert (DE-588)4605821-7 gnd |
| topic_facet | Medizinische Statistik p-Wert |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=2788986&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020514481&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT moyelemuela statisticalreasoninginmedicinetheintuitivepvalueprimer |