Handbook of regression and modeling: applications for the clinical and pharmaceutical industries
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, Fla. [u.a.]
Chapman & Hall/CRC
2007
|
| Schriftenreihe: | Chapman & Hall/CRC biostatistics series
18 |
| Schlagworte: | |
| Online-Zugang: | Table of contents only Publisher description Inhaltsverzeichnis Klappentext |
| Beschreibung: | 503 S. zahlr. graph. Darst. |
| ISBN: | 9781574446104 157444610X |
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| 100 | 1 | |a Paulson, Daryl S. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Handbook of regression and modeling |b applications for the clinical and pharmaceutical industries |c Daryl S. Paulson |
| 264 | 1 | |a Boca Raton, Fla. [u.a.] |b Chapman & Hall/CRC |c 2007 | |
| 300 | |a 503 S. |b zahlr. graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Chapman & Hall/CRC biostatistics series |v 18 | |
| 650 | 4 | |a Analyse de régression | |
| 650 | 4 | |a Médecine - Recherche - Méthodes statistiques | |
| 650 | 4 | |a Médicaments - Recherche - Méthodes statistiques | |
| 650 | 4 | |a Études cliniques - Méthodes statistiques | |
| 650 | 4 | |a Medizin | |
| 650 | 4 | |a Medicine |x Research |x Statistical methods |v Handbooks, manuals, etc | |
| 650 | 4 | |a Regression analysis |v Handbooks, manuals, etc | |
| 650 | 4 | |a Drugs |x Research |x Statistical methods |v Handbooks, manuals, etc | |
| 650 | 4 | |a Clinical trials |x Statistical methods |v Handbooks, manuals, etc | |
| 650 | 4 | |a Clinical Medicine | |
| 650 | 4 | |a Regression Analysis | |
| 650 | 4 | |a Biometry |x methods | |
| 650 | 4 | |a Drug Industry | |
| 650 | 4 | |a Models, Statistical | |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-016244270 | ||
Datensatz im Suchindex
| _version_ | 1804137269173420032 |
|---|---|
| adam_text | Table
of Contents
Chapter
1
Basic Statistical Concepts
.......................... 1
Meaning of Standard Deviation
............................... 2
Hypothesis Testing
........................................ 3
Upper-Tail Test
......................................... 4
Lower-Tail Test
......................................... 4
Two-Tail Test
.......................................... 4
Confidence Intervals
....................................... 9
Applied Research and Statistics
............................... 9
Experimental Validity
..................................... 10
Empirical Research
....................................... 13
Biases
................................................. 14
Openness
............................................. 14
Discernment
.......................................... 15
Understanding
(Verstehen)................................ 15
Experimental Process
...................................... 16
Other Difficulties in Research
............................... 20
Experimental Error
....................................... 20
Confusing Correlation with Causation
......................... 20
Complex Study Design
.................................... 21
Basic Tools in Experimental Design
........................... 22
Statistical Method Selection: Overview
........................ 23
Chapter
2
Simple Linear Regression
......................... 25
General Principles of Regression Analysis
...................... 26
Regression and Causality
................................. 26
Meaning of Regression Parameters
............................ 27
Data for Regression Analysis
................................ 29
Regression Parameter Calculation
............................ 29
Properties of the Least-Squares Estimation
...................... 31
Diagnostics
............................................. 34
Estimation of the Error Term
................................ 38
Regression Inferences
..................................... 39
Computer Output
......................................... 43
Confidence Interval for
ß .................................. 43
Inferences with
ß0........................................ 44
Power of the Tests for
ß0
and
ßx............................. 47
Estimating
y
via Confidence Intervals
.......................... 49
Confidence Interval of
y
................................... 50
Prediction of a Specific Observation
........................... 53
Confidence Interval for the Entire Regression Model
.............. 54
ANOVA and Regression
................................... 57
Linear Model Evaluation of Fit of the Model
.................... 62
Reduced Error Model
..................................... 65
Exploratory Data Analysis and Regression
...................... 71
Pattern A
............................................. 72
Pattern
В
............................................. 72
Pattern
С
............................................. 72
Pattern
D
............................................. 72
Data That Cannot Be Linearized by
Reexpression
............... 73
Exploratory Data Analysis to Determine the Linearity of a
Regression Line without Using the Fc Test for Lack of Fit
........ 73
Correlation Coefficient
.................................... 76
Correlation Coefficient Hypothesis Testing
...................... 79
Confidence Interval for the Correlation Coefficient
................ 81
Prediction of a Specific
x
Value from
a y
Value
.................. 83
Predicting an Average
x
.................................... 85
D
Value Computation
..................................... 86
Simultaneous Mean Inferences of
ßo
and
ß ..................... 87
Simultaneous Multiple Mean Estimates of
y
..................... 89
Special Problems in Simple Linear Regression
................... 91
Piecewise Regression
.................................... 91
Comparison of Multiple Simple Linear
Regression Functions
.................................... 93
Evaluating Two Slopes (bla and bih) for
Equivalence in Slope Values
.............................. 95
Evaluating the Two
y
Intercepts
(/Зо)
for Equivalence
............. 101
Multiple Regression
...................................... 105
More Difficult to Understand
............................. 105
Cost-Benefit Ratio Low
................................. 105
Poorly Thought-Out Study
............................... 106
Conclusion
............................................ 106
Chapter
3
Special Problems in Simple Linear Regression: Serial
Correlation and Curve Fitting
..................... 107
Autocorrelation or Serial Correlation
......................... 107
Durbin-Watson Test for Serial Correlation
................... 109
Two-Tail Durbin-Watson Test Procedure
.................... 119
Simplified Durbin-Watson Test
........................... 119
Alternate Runs Test in Time Series
......................... 120
Measures to Remedy Serial Correlation Problems
.............. 123
Transformation
Procedure
(When Adding More Predictor
x¡
Values Is Not an Option)
............................ 124
Cochrane-Orcutt Procedure
.............................. 126
Lag
1
or First Difference Procedure
........................ 133
Curve Fitting with Serial Correlation
....................... 136
Remedy
............................................. 144
Residual Analysis
y¡
—
y¡ = e¡
............................... 147
Standardized Residuals
.................................. 151
Chapter
4
Multiple Linear Regression
....................... 153
Regression Coefficients
................................... 153
Multiple Regression Assumptions
............................ 154
General Regression Procedures
............................. 155
Application
............................................ 156
Hypothesis Testing for Multiple Regression
.................. 159
Overall Test
........................................ 159
Partial F-Test
....................................... 161
Alternative to SSR
................................... 165
The
ŕ-Test
for the Determination of the
/3,
Contribution
........ 166
Multiple Partial F-Tests
............................... 168
Forward Selection: Predictor Variables Added into the Model
..... 173
Backward Elimination: Predictors Removed from the Model
...... 182
Discussion
............................................. 192
Y
Estimate Point and Interval: Mean
........................ 192
Confidence Interval Estimation of the
β,
-s
....................
197
Predicting One or Several New Observations
................. 200
New Mean Vector Prediction
............................. 202
Predicting
t
New Observations
............................ 202
Entire Regression Surface Confidence Region
................. 203
Chapter
5
Correlation Analysis in Multiple Regression
.......... 205
Procedure for Testing Partial Correlation Coefficients
............. 209
R2 Used to Determine How Many x,- Variables
to Include in the Model
................................. 211
Chapter
6
Some Important Issues in Multiple Linear Regression
... 213
CoIIinearity and Multiple Collinearity
........................ 213
Measuring Multiple Collinearity
............................. 214
Eigen
(λ)
Analysis
....................................... 217
Condition Index
....................................... 219
Condition Number
..................................... 221
Variance Proportion
.................................... 221
Statistical Methods to Offset Serious Collinearity
................ 222
Rescaling
the Data for Regression
......................... 222
Ridge Regression
...................................... 222
Ridge Regression Procedure
.............................. 224
Conclusion
.......,.................................... 240
Chapter
7
Polynomial Regression
.......................... 241
Other Points to Consider
.................................. 242
Lack of Fit
............................................ 257
Splines (Piecewise Polynomial Regression)
.................... 261
Spline Example Diagnostic
................................ 266
Linear Splines
.......................................... 269
Chapter
8
Special Topics in Multiple Regression
.............. 277
Interaction between the
jc¡
Predictor Variables
................... 277
Confounding
........................................... 280
Unequal Error Variances
.................................. 281
Residual Plots
.......................................... 283
Modified Levene Test for Constant Variance
................... 285
Procedure
............................................. 286
Breusch-Pagan Test: Error Constancy
........................ 293
For Multiple
x¡
Variables
.................................. 296
Variance Stabilization Procedures
........................... 299
Weighted Least Squares
................................... 300
Estimation of the Weights
................................. 302
Residuals and Outliers, Revisited
............................ 307
Standardized Residuals
.................................. 309
Studentized Residuals
.................................. 309
Jackknife Residual
..................................... 310
To Determine Outliers
.................................. 311
Outlier Identification Strategy
............................ 311
Leverage Value Diagnostics
.............................. 311
Cook s Distance
...................................... 313
Leverages and Cook s Distance
........................... 323
Leverage and Influence
................................... 325
Leverage: Hat Matrix (x Values)
.......................... 325
Influence: Cook s Distance
............................... 333
Outlying Response Variable Observations, y,
................. 335
Studentized Deleted Residuals
.............................. 336
Influence: Beta Influence
.................................. 339
Summary
............................................. 340
Chapter
9
Indicator (Dummy) Variable Regression
............. 341
Inguinal Site,
IPA
Product, Immediate
........................ 345
Inguinal Site,
IPA
± CHG Product, Immediate
................. 346
Inguinal Site,
IPA
Product,
24
h
............................. 346
Inguinal Site,
IPA
± CHG
Product,
24
h
...................... 346
Comparing Two Regression Functions
........................ 353
Comparing the j-Intercepts
................................ 356
Test of b s or Slopes: Parallelism
............................ 361
Parallel Slope Test Using Indicator Variables
................... 364
Intercept Test Using an Indicator Variable Model
................ 367
Parallel Slope Test Using a Single
Regression Model
..................................... 370
IPA
Product
........................................... 372
IPA + CHG Product
...................................... 372
Test for Coincidence Using a Single Regression Model
............ 373
Larger Variable Models
................................. 375
More Complex Testing
................................. 376
Global Test for Coincidence
............................... 379
Global Parallelism
..................................... 383
Global Intercept Test
..................................... 385
Confidence Intervals for
/3,-
Values
........................... 386
Piecewise Linear Regression
............................... 387
More Complex Piecewise Regression Analysis
.................. 391
Discontinuous Piecewise Regression
......................... 401
Chapter
10
Model Building and Model Selection
.............. 409
Predictor Variables
...................................... 409
Measurement Collection
.................................. 410
Selection of the
x¡
Predictor Variables
........................ 410
Adequacy of the Model Fit
................................ 411
Stepwise Regression
..................................... 414
Forward Selection
........................................ 416
Backward Elimination
.................................... 417
Best Subset Procedures
................................... 419
R¡
and SSEi
............................................ 420
Adj
R¡
and MSEi
........................................ 420
Mallow s Ck Criteria
..................................... 421
Other Points
........................................... 421
Chapter
11
Analysis of Covariance
......................... 423
Single-Factor Covariance Model
............................ 424
Some Further Considerations
............................... 426
Requirements of ANCOVA
................................ 428
ANCOVA Routine
...................................... 429
Regression Routine Example
............................. 434
Treatment Effects
..................................... 437
Single Interval Estimate
................................... 440
Scheffe Procedure
—
Multiple Contrasts
....................... 440
Bonferroni Method
...................................... 442
Adjusted Average Response
................................ 442
Conclusion
............................................ 443
Appendix 1
............................................ 445
Tables A through
О
...................................... 445
Appendix II
........................................... 481
Matrix Algebra Applied to Regression
........................ 481
Matrix Operations
....................................... 483
Addition
............................................ 484
Subtraction
.......................................... 484
Multiplication
........................................ 485
Inverse of Matrix
...................................... 488
References
............................................ 497
Index
................................................ 499
Carefully designed for use by clinical and pharmaceutical researchers and
scientists, Handbook of Regression Analysis and Modeling explores statistical
methods that have been adapted into biological applications for the quickly
evolving field of biostatistics. The author clearly delineates a six-step method
for hypothesis testing using data that mimic real life. Relying heavily on computer
software, he includes exploratory data analysis to evaluate the fit of the model
to the actual data.
The book presents a well-defined procedure for adding or subtracting independent
variables to the model variable and covers how to apply statistical forecasting
methods to the serially correlated data characteristically found in clinical and
pharmaceutical settings. The standalone chapters allow you to pick and choose
which chapter to read first and hone in on the information that fits your immediate
needs. Each example is presented in computer software format. The author uses
MiniTab in the book but supplies instructions that are easily adapted for
SAS
and SPSSX, making the book applicable to individual situations.
Although written with the assumption that the reader has knowledge of basic
and matrix algebra, the book supplies a short course on matrix algebra in the
appendix for those who need it. Covering more than just statistical theory, the
book provides advanced methods that you can put to immediate use.
Features
•
Offers essential information for applying biostatistics to clinical and
pharmaceutical research
•
Presents exploratory data analysis to evaluate the fit of the model to the
actual data
•
Presents data sets in their entirety instead of relying on data sets supplied
on a software CD
•
Provides a well-defined procedure for adding or subtracting independent
variables to the model variables with methods that rely on computer software
•
Addresses practical issues such as cost, feasibility, model adequacy-testing,
and forecasting
•
Covers regression analysis and modeling for assay development/validation,
stability design and analysis, toxicology studies, and dose ranging and
response
|
| adam_txt |
Table
of Contents
Chapter
1
Basic Statistical Concepts
. 1
Meaning of Standard Deviation
. 2
Hypothesis Testing
. 3
Upper-Tail Test
. 4
Lower-Tail Test
. 4
Two-Tail Test
. 4
Confidence Intervals
. 9
Applied Research and Statistics
. 9
Experimental Validity
. 10
Empirical Research
. 13
Biases
. 14
Openness
. 14
Discernment
. 15
Understanding
(Verstehen). 15
Experimental Process
. 16
Other Difficulties in Research
. 20
Experimental Error
. 20
Confusing Correlation with Causation
. 20
Complex Study Design
. 21
Basic Tools in Experimental Design
. 22
Statistical Method Selection: Overview
. 23
Chapter
2
Simple Linear Regression
. 25
General Principles of Regression Analysis
. 26
Regression and Causality
. 26
Meaning of Regression Parameters
. 27
Data for Regression Analysis
. 29
Regression Parameter Calculation
. 29
Properties of the Least-Squares Estimation
. 31
Diagnostics
. 34
Estimation of the Error Term
. 38
Regression Inferences
. 39
Computer Output
. 43
Confidence Interval for
ß\. 43
Inferences with
ß0. 44
Power of the Tests for
ß0
and
ßx. 47
Estimating
y
via Confidence Intervals
. 49
Confidence Interval of
y
. 50
Prediction of a Specific Observation
. 53
Confidence Interval for the Entire Regression Model
. 54
ANOVA and Regression
. 57
Linear Model Evaluation of Fit of the Model
. 62
Reduced Error Model
. 65
Exploratory Data Analysis and Regression
. 71
Pattern A
. 72
Pattern
В
. 72
Pattern
С
. 72
Pattern
D
. 72
Data That Cannot Be Linearized by
Reexpression
. 73
Exploratory Data Analysis to Determine the Linearity of a
Regression Line without Using the Fc Test for Lack of Fit
. 73
Correlation Coefficient
. 76
Correlation Coefficient Hypothesis Testing
. 79
Confidence Interval for the Correlation Coefficient
. 81
Prediction of a Specific
x
Value from
a y
Value
. 83
Predicting an Average
x
. 85
D
Value Computation
. 86
Simultaneous Mean Inferences of
ßo
and
ß\. 87
Simultaneous Multiple Mean Estimates of
y
. 89
Special Problems in Simple Linear Regression
. 91
Piecewise Regression
. 91
Comparison of Multiple Simple Linear
Regression Functions
. 93
Evaluating Two Slopes (bla and bih) for
Equivalence in Slope Values
. 95
Evaluating the Two
y
Intercepts
(/Зо)
for Equivalence
. 101
Multiple Regression
. 105
More Difficult to Understand
. 105
Cost-Benefit Ratio Low
. 105
Poorly Thought-Out Study
. 106
Conclusion
. 106
Chapter
3
Special Problems in Simple Linear Regression: Serial
Correlation and Curve Fitting
. 107
Autocorrelation or Serial Correlation
. 107
Durbin-Watson Test for Serial Correlation
. 109
Two-Tail Durbin-Watson Test Procedure
. 119
Simplified Durbin-Watson Test
. 119
Alternate Runs Test in Time Series
. 120
Measures to Remedy Serial Correlation Problems
. 123
Transformation
Procedure
(When Adding More Predictor
x¡
Values Is Not an Option)
. 124
Cochrane-Orcutt Procedure
. 126
Lag
1
or First Difference Procedure
. 133
Curve Fitting with Serial Correlation
. 136
Remedy
. 144
Residual Analysis
y¡
—
y¡ = e¡
. 147
Standardized Residuals
. 151
Chapter
4
Multiple Linear Regression
. 153
Regression Coefficients
. 153
Multiple Regression Assumptions
. 154
General Regression Procedures
. 155
Application
. 156
Hypothesis Testing for Multiple Regression
. 159
Overall Test
. 159
Partial F-Test
. 161
Alternative to SSR
. 165
The
ŕ-Test
for the Determination of the
/3,
Contribution
. 166
Multiple Partial F-Tests
. 168
Forward Selection: Predictor Variables Added into the Model
. 173
Backward Elimination: Predictors Removed from the Model
. 182
Discussion
. 192
Y
Estimate Point and Interval: Mean
. 192
Confidence Interval Estimation of the
β,
-s
.
197
Predicting One or Several New Observations
. 200
New Mean Vector Prediction
. 202
Predicting
t
New Observations
. 202
Entire Regression Surface Confidence Region
. 203
Chapter
5
Correlation Analysis in Multiple Regression
. 205
Procedure for Testing Partial Correlation Coefficients
. 209
R2 Used to Determine How Many x,- Variables
to Include in the Model
. 211
Chapter
6
Some Important Issues in Multiple Linear Regression
. 213
CoIIinearity and Multiple Collinearity
. 213
Measuring Multiple Collinearity
. 214
Eigen
(λ)
Analysis
. 217
Condition Index
. 219
Condition Number
. 221
Variance Proportion
. 221
Statistical Methods to Offset Serious Collinearity
. 222
Rescaling
the Data for Regression
. 222
Ridge Regression
. 222
Ridge Regression Procedure
. 224
Conclusion
.,. 240
Chapter
7
Polynomial Regression
. 241
Other Points to Consider
. 242
Lack of Fit
. 257
Splines (Piecewise Polynomial Regression)
. 261
Spline Example Diagnostic
. 266
Linear Splines
. 269
Chapter
8
Special Topics in Multiple Regression
. 277
Interaction between the
jc¡
Predictor Variables
. 277
Confounding
. 280
Unequal Error Variances
. 281
Residual Plots
. 283
Modified Levene Test for Constant Variance
. 285
Procedure
. 286
Breusch-Pagan Test: Error Constancy
. 293
For Multiple
x¡
Variables
. 296
Variance Stabilization Procedures
. 299
Weighted Least Squares
. 300
Estimation of the Weights
. 302
Residuals and Outliers, Revisited
. 307
Standardized Residuals
. 309
Studentized Residuals
. 309
Jackknife Residual
. 310
To Determine Outliers
. 311
Outlier Identification Strategy
. 311
Leverage Value Diagnostics
. 311
Cook's Distance
. 313
Leverages and Cook's Distance
. 323
Leverage and Influence
. 325
Leverage: Hat Matrix (x Values)
. 325
Influence: Cook's Distance
. 333
Outlying Response Variable Observations, y,
. 335
Studentized Deleted Residuals
. 336
Influence: Beta Influence
. 339
Summary
. 340
Chapter
9
Indicator (Dummy) Variable Regression
. 341
Inguinal Site,
IPA
Product, Immediate
. 345
Inguinal Site,
IPA
± CHG Product, Immediate
. 346
Inguinal Site,
IPA
Product,
24
h
. 346
Inguinal Site,
IPA
± CHG
Product,
24
h
. 346
Comparing Two Regression Functions
. 353
Comparing the j-Intercepts
. 356
Test of b\s or Slopes: Parallelism
. 361
Parallel Slope Test Using Indicator Variables
. 364
Intercept Test Using an Indicator Variable Model
. 367
Parallel Slope Test Using a Single
Regression Model
. 370
IPA
Product
. 372
IPA + CHG Product
. 372
Test for Coincidence Using a Single Regression Model
. 373
Larger Variable Models
. 375
More Complex Testing
. 376
Global Test for Coincidence
. 379
Global Parallelism
. 383
Global Intercept Test
. 385
Confidence Intervals for
/3,-
Values
. 386
Piecewise Linear Regression
. 387
More Complex Piecewise Regression Analysis
. 391
Discontinuous Piecewise Regression
. 401
Chapter
10
Model Building and Model Selection
. 409
Predictor Variables
. 409
Measurement Collection
. 410
Selection of the
x¡
Predictor Variables
. 410
Adequacy of the Model Fit
. 411
Stepwise Regression
. 414
Forward Selection
. 416
Backward Elimination
. 417
Best Subset Procedures
. 419
R¡
and SSEi
. 420
Adj
R¡
and MSEi
. 420
Mallow's Ck Criteria
. 421
Other Points
. 421
Chapter
11
Analysis of Covariance
. 423
Single-Factor Covariance Model
. 424
Some Further Considerations
. 426
Requirements of ANCOVA
. 428
ANCOVA Routine
. 429
Regression Routine Example
. 434
Treatment Effects
. 437
Single Interval Estimate
. 440
Scheffe Procedure
—
Multiple Contrasts
. 440
Bonferroni Method
. 442
Adjusted Average Response
. 442
Conclusion
. 443
Appendix 1
. 445
Tables A through
О
. 445
Appendix II
. 481
Matrix Algebra Applied to Regression
. 481
Matrix Operations
. 483
Addition
. 484
Subtraction
. 484
Multiplication
. 485
Inverse of Matrix
. 488
References
. 497
Index
. 499
Carefully designed for use by clinical and pharmaceutical researchers and
scientists, Handbook of Regression Analysis and Modeling explores statistical
methods that have been adapted into biological applications for the quickly
evolving field of biostatistics. The author clearly delineates a six-step method
for hypothesis testing using data that mimic real life. Relying heavily on computer
software, he includes exploratory data analysis to evaluate the fit of the model
to the actual data.
The book presents a well-defined procedure for adding or subtracting independent
variables to the model variable and covers how to apply statistical forecasting
methods to the serially correlated data characteristically found in clinical and
pharmaceutical settings. The standalone chapters allow you to pick and choose
which chapter to read first and hone in on the information that fits your immediate
needs. Each example is presented in computer software format. The author uses
MiniTab in the book but supplies instructions that are easily adapted for
SAS
and SPSSX, making the book applicable to individual situations.
Although written with the assumption that the reader has knowledge of basic
and matrix algebra, the book supplies a short course on matrix algebra in the
appendix for those who need it. Covering more than just statistical theory, the
book provides advanced methods that you can put to immediate use.
Features
•
Offers essential information for applying biostatistics to clinical and
pharmaceutical research
•
Presents exploratory data analysis to evaluate the fit of the model to the
actual data
•
Presents data sets in their entirety instead of relying on data sets supplied
on a software CD
•
Provides a well-defined procedure for adding or subtracting independent
variables to the model variables with methods that rely on computer software
•
Addresses practical issues such as cost, feasibility, model adequacy-testing,
and forecasting
•
Covers regression analysis and modeling for assay development/validation,
stability design and analysis, toxicology studies, and dose ranging and
response |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Paulson, Daryl S. |
| author_facet | Paulson, Daryl S. |
| author_role | aut |
| author_sort | Paulson, Daryl S. |
| author_variant | d s p ds dsp |
| building | Verbundindex |
| bvnumber | BV023040718 |
| callnumber-first | R - Medicine |
| callnumber-label | R853 |
| callnumber-raw | R853.S7 |
| callnumber-search | R853.S7 |
| callnumber-sort | R 3853 S7 |
| callnumber-subject | R - General Medicine |
| classification_rvk | WC 7000 |
| ctrlnum | (OCoLC)71328376 (DE-599)BVBBV023040718 |
| dewey-full | 610.72/7 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 610 - Medicine and health |
| dewey-raw | 610.72/7 |
| dewey-search | 610.72/7 |
| dewey-sort | 3610.72 17 |
| dewey-tens | 610 - Medicine and health |
| discipline | Biologie Medizin |
| discipline_str_mv | Biologie Medizin |
| format | Book |
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| id | DE-604.BV023040718 |
| illustrated | Illustrated |
| index_date | 2024-07-02T19:20:33Z |
| indexdate | 2024-07-09T21:09:36Z |
| institution | BVB |
| isbn | 9781574446104 157444610X |
| language | English |
| lccn | 2006030225 |
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| publisher | Chapman & Hall/CRC |
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| series | Chapman & Hall/CRC biostatistics series |
| series2 | Chapman & Hall/CRC biostatistics series |
| spelling | Paulson, Daryl S. Verfasser aut Handbook of regression and modeling applications for the clinical and pharmaceutical industries Daryl S. Paulson Boca Raton, Fla. [u.a.] Chapman & Hall/CRC 2007 503 S. zahlr. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC biostatistics series 18 Analyse de régression Médecine - Recherche - Méthodes statistiques Médicaments - Recherche - Méthodes statistiques Études cliniques - Méthodes statistiques Medizin Medicine Research Statistical methods Handbooks, manuals, etc Regression analysis Handbooks, manuals, etc Drugs Research Statistical methods Handbooks, manuals, etc Clinical trials Statistical methods Handbooks, manuals, etc Clinical Medicine Regression Analysis Biometry methods Drug Industry Models, Statistical Modellierung (DE-588)4170297-9 gnd rswk-swf Medizin (DE-588)4038243-6 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Pharmazie (DE-588)4045705-9 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 s Medizin (DE-588)4038243-6 s DE-604 Pharmazie (DE-588)4045705-9 s Modellierung (DE-588)4170297-9 s b DE-604 Chapman & Hall/CRC biostatistics series 18 (DE-604)BV023097394 18 http://www.loc.gov/catdir/toc/ecip0620/2006030225.html Table of contents only http://www.loc.gov/catdir/enhancements/fy0701/2006030225-d.html Publisher description Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016244270&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016244270&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Paulson, Daryl S. Handbook of regression and modeling applications for the clinical and pharmaceutical industries Chapman & Hall/CRC biostatistics series Analyse de régression Médecine - Recherche - Méthodes statistiques Médicaments - Recherche - Méthodes statistiques Études cliniques - Méthodes statistiques Medizin Medicine Research Statistical methods Handbooks, manuals, etc Regression analysis Handbooks, manuals, etc Drugs Research Statistical methods Handbooks, manuals, etc Clinical trials Statistical methods Handbooks, manuals, etc Clinical Medicine Regression Analysis Biometry methods Drug Industry Models, Statistical Modellierung (DE-588)4170297-9 gnd Medizin (DE-588)4038243-6 gnd Regressionsanalyse (DE-588)4129903-6 gnd Pharmazie (DE-588)4045705-9 gnd |
| subject_GND | (DE-588)4170297-9 (DE-588)4038243-6 (DE-588)4129903-6 (DE-588)4045705-9 |
| title | Handbook of regression and modeling applications for the clinical and pharmaceutical industries |
| title_auth | Handbook of regression and modeling applications for the clinical and pharmaceutical industries |
| title_exact_search | Handbook of regression and modeling applications for the clinical and pharmaceutical industries |
| title_exact_search_txtP | Handbook of regression and modeling applications for the clinical and pharmaceutical industries |
| title_full | Handbook of regression and modeling applications for the clinical and pharmaceutical industries Daryl S. Paulson |
| title_fullStr | Handbook of regression and modeling applications for the clinical and pharmaceutical industries Daryl S. Paulson |
| title_full_unstemmed | Handbook of regression and modeling applications for the clinical and pharmaceutical industries Daryl S. Paulson |
| title_short | Handbook of regression and modeling |
| title_sort | handbook of regression and modeling applications for the clinical and pharmaceutical industries |
| title_sub | applications for the clinical and pharmaceutical industries |
| topic | Analyse de régression Médecine - Recherche - Méthodes statistiques Médicaments - Recherche - Méthodes statistiques Études cliniques - Méthodes statistiques Medizin Medicine Research Statistical methods Handbooks, manuals, etc Regression analysis Handbooks, manuals, etc Drugs Research Statistical methods Handbooks, manuals, etc Clinical trials Statistical methods Handbooks, manuals, etc Clinical Medicine Regression Analysis Biometry methods Drug Industry Models, Statistical Modellierung (DE-588)4170297-9 gnd Medizin (DE-588)4038243-6 gnd Regressionsanalyse (DE-588)4129903-6 gnd Pharmazie (DE-588)4045705-9 gnd |
| topic_facet | Analyse de régression Médecine - Recherche - Méthodes statistiques Médicaments - Recherche - Méthodes statistiques Études cliniques - Méthodes statistiques Medizin Medicine Research Statistical methods Handbooks, manuals, etc Regression analysis Handbooks, manuals, etc Drugs Research Statistical methods Handbooks, manuals, etc Clinical trials Statistical methods Handbooks, manuals, etc Clinical Medicine Regression Analysis Biometry methods Drug Industry Models, Statistical Modellierung Regressionsanalyse Pharmazie |
| url | http://www.loc.gov/catdir/toc/ecip0620/2006030225.html http://www.loc.gov/catdir/enhancements/fy0701/2006030225-d.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016244270&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016244270&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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