Muutke küpsiste eelistusi

E-raamat: JAMA Guide to Statistics and Methods

4.75/5 (4 hinnangut Goodreads-ist)
,
  • Formaat: 528 pages
  • Ilmumisaeg: 29-Nov-2019
  • Kirjastus: McGraw-Hill Education
  • Keel: eng
  • ISBN-13: 9781260455335
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 68,33 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
JAMA Guide to Statistics and MethodsSuurem pilt
  • Formaat: 528 pages
  • Ilmumisaeg: 29-Nov-2019
  • Kirjastus: McGraw-Hill Education
  • Keel: eng
  • ISBN-13: 9781260455335
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.



The world-renowned experts at JAMA® explain statistical analysis and the methods used in medical research 


Written in the language and style appropriate for clinicians and researchers, this new JAMA Guide to Statistics and Methods provides explanations and expert discussion of the statistical analytic approaches and methods used in the medical research reported in articles appearing in JAMA and the JAMA Network journals. 

This addition to the JAMAevidence® series is particularly timely and necessary because today’s physicians and other health care professionals must pursue lifelong learning to keep up with the ever-expanding universe of new medical science and evidence-based clinical information. Readers and users of research articles must have a firm grasp of the myriad new statistical, analytic, and methodologic approaches used in contemporary medical studies. To provide concrete examples, the explanations in the book link to research articles that incorporate the specific statistical test or methodological approach being discussed.


Organizational Structure of This Book xxiv
Interventional Studies
Trial Strategy and Design
Enrollment, Allocation of Treatment, and Ethics
Measurement Outcome and Analysis and Interpretation of Results
Application of Results
Observational Studies
Study Strategy and Design
Assessment of Risk Factors and Exposures
Measurement Outcome and Analysis and Interpretation of Results
Application of Results
Practical Guide to Data Sets
Foreword xxvii
Preface xxxi
Editors and Contributors xxxiii
INTERVENTIONAL STUDIES: Trial Strategy and Design
Noninferiority Trials: Is a New Treatment Almost as Effective as Another?
1(8)
Use of the Method
Why Are Noninferiority Trials Conducted?
What Are the Limitations of Noninferiority Trials?
Why Was a Noninferiority Trial Conducted in This Case?
How Should the Results Be Interpreted?
Caveats to Consider When Looking at a Noninferiority Trial
Dose-Finding Trials: Optimizing Phase 2 Data in the Drug Development Process
9(8)
Use of the Method
Why Are Dose-Response Models Used?
What Are the Limitations of Dose-Response Modeling?
Why Did the Authors Use Dose-Response Modeling in This Particular Study?
How Should the Dose-Response Findings Be Interpreted in This Particular Study?
Caveats to Consider When Looking at Results Based on a Dose-Response Model
Pragmatic Trials: Practical Answers to "Real World" Questions
17(8)
Use of the Method
Why Are Pragmatic Trials Conducted?
Description of the Method
What Are the Limitations of Pragmatic Trials?
Why Was a Pragmatic Trial Conducted in This Case?
How Should the Results Be Interpreted?
Cluster Randomized Trials: Evaluating Treatments Applied to Groups
25(8)
Use of the Method
Why Is Cluster Randomization Used?
What Are Limitations of Cluster Randomization?
Why Did the Authors Use Cluster Randomization in This Particular Study?
How Should Cluster Randomization Findings Be Interpreted in This Particular Study?
Caveats to Consider When Looking at a Cluster Randomized Trial
The Stepped-Wedge Clinical Trial: Evaluation by Rolling Deployment
33(8)
Use of the Method
Why Is a Stepped-Wedge Clinical Trial Design Used?
Description of the Stepped-Wedge Clinical Trial Design
Limitations of the Stepped-Wedge Design
How Was the Stepped-Wedge Design Used?
How Should a Stepped-Wedge Clinical Trial Be Interpreted?
Sample Size Calculation for a Hypothesis Test
41(8)
Use of the Method
Why Is Power Analysis Used?
What Are the Limitations of Power Analysis?
Why Did the Authors Use Power Analysis in This Particular Study?
How Should This Method's Findings Be Interpreted in This Particular Study?
Caveats to Consider When Looking at Results Based on Power Analysis
Minimal Clinically Important Difference: Defining What Really Matters to Patients
49(8)
Use of the Method
Why Is the MCID Used?
What Are the Limitations of MCID Derivation Methods?
Why Did the Authors Use MCID in This Particular Study?
How Should MCID Findings Be Interpreted in This Particular Study?
Caveats to Consider When Looking at Results Based on MClDs
Enrollment, Allocation of Treatment, and Ethics
Randomization in Clinical Trials: Permuted Blocks and Stratification
57(8)
Explanation of the Concept
What Are Permuted Blocks and Stratified Randomization?
Why Are Permuted Blocks and Stratified Randomization Important?
Limitations of Permuted Block Randomization and Stratified Randomization
How Were These Approaches to Randomization Used?
How Does the Approach to Randomization Affect the Trial's Interpretation?
Equipoise in Research: Integrating Ethics and Science in Human Research
65(8)
What Is Equipoise?
Why Is Equipoise Important?
What Are the Limitations of Equipoise?
How Is Equipoise Applied in This Case?
How Does Equipoise Influence the Interpretation of the Study?
Measurement Outcomes and Analysis and Interpretation of Results
Time-to-Event Analysis
73(8)
Use of the Method
Why Is Time-to-Event Analysis Used?
What Are the Limitations of the Proportional Hazards Model?
How Should Time-to-Event Findings Be Interpreted in This Particular Study?
Caveats to Consider When Looking at Results from a Time-to-Event Analysis
The "Utility" in Composite Outcome Measures: Measuring What Is Important to Patients
81(8)
Why Are Composite End Points Used in Clinical Studies?
Limitations of Composite End Points
How Were Composite End Points Used in This Study?
How Does the Use of a Composite End Point Affect the Interpretation of This Study?
Missing Data: How to Best Account for What Is Not Known
89(8)
Use of the Method
Why Are These Methods Used?
What Are the Limitations of These Methods?
Why Did the Authors Use This Method in This Particular Study?
How Should This Method's Findings Be Interpreted in This Particular Study?
Caveats to Consider When Looking at the Results in This Study Based on This Method
The Intention-to-Treat Principle: How to Assess the True Effect of Choosing a Medical Treatment
97(8)
Use of the Method
Why Is ITT Analysis Used?
What Are the Limitations of ITT Analysis?
Why Did the Authors Use ITT Analysis in This Particular Study?
Caveats to Consider When Looking at Results Based on ITT Analysis
Analyzing Repeated Measurements Using Mixed Models
105(8)
Use of the Method
Why Are Mixed Models Used for Repeated Measures Data?
What Are the Limitations of Mixed Models?
Why Did the Authors Use Mixed Models in This Particular Study?
Caveats to Consider When Looking at Results From Mixed Models
Logistic Regression: Relating Patient Characteristics to Outcomes
113(8)
Use of the Method
Why Is Logistic Regression Used?
Description of the Method
What Are the Limitations of Logistic Regression?
Why Did the Authors Use Logistic Regression in This Study?
How Should the Results of Logistic Regression
Be Interpreted in This Particular Study?
Caveats to Consider When Assessing the Results of a Logistic Regression Analysis
Logistic Regression Diagnostics: Understanding How Well a Model Predicts Outcomes
121(8)
Use of the Method
Why Are Logistic Regression Model Diagnostic Used?
Description of the Method
What Are the Limitations of Logistic Regression Diagnostic?
Why Did the Authors Use Logistic Regression Diagnostics in
This Particular Study?
How Should the Results of Logistic Regression Diagnostics Be Interpreted in This Particular Study?
Caveats to Consider When Assessing the Results of Logistic Regression Diagnostics
Number Needed to Treat: Conveying the Likelihood of a Therapeutic Effect
129(8)
Explanation of the Concept
What Is the NNT?
Why Is the NNT Important?
Limitations and Alternatives to the NNT
How Was the Concept of NNT Applied in This Particular Study?
How Should the NNT Be Interpreted in the Study by Zhao et al?
Multiple Comparison Procedures
137(8)
Use of the Method
Why Are Multiple Comparison Procedures Used?
What Are the Limitations of Multiple Comparison Procedures?
Why Did the Authors Use Multiple Comparison Procedures in This Particular Study?
How Should This Method's Findings Be Interpreted in This Particular Study?
Caveats to Consider When Looking at Multiple Comparison Procedures
To Adjust or Not
Confirmatory vs Exploratory
FWER vs FDR
Definition of Family
Gatekeeping Strategies for Avoiding False-Positive Results in Clinical Trials With Many Comparisons
145(8)
Use of the Method
Why Is Serial Gatekeeping Used?
Description of the Method
What Are the Limitations of Gatekeeping Strategies?
How Was Gatekeeping Used in This Case?
How Should the Results Be Interpreted?
Multiple Imputation: A Flexible Tool for Handling Missing Data
153(8)
Use of the Method
Why Is Multiple Imputation Used?
What Are the Limitations of Multiple Imputation?
Why Did the Authors Use Multiple Imputation in This Particular Study?
How Should Multiple Imputation Findings Be Interpreted in This Particular Study?
Caveats to Consider When Looking at Results Based on Multiple Imputation
Interpretation of Clinical Trials That Stopped Early
161(8)
Use of the Method
Why Is Early Stopping Used?
What Are the Limitations of Early Stopping?
Why Did the Authors Use Early Stopping in This Study?
How Should Early Stopping Be Interpreted in This Particular Study?
Caveats to Consider When Looking at a Trial That Stopped Early
Bayesian Analysis: Using Prior Information to Interpretthe Results of Clinical Trials
169(6)
Prior Information
What Is Prior Information?
Why Is Prior Information Important?
Limitations of Prior Information
How Was Prior Information Used?
How Should the Trial Results Be Interpreted in Light of the Prior Information?
Application of Results
Decision Curve Analysis
175(8)
Use of the Method
Why Is DCA Used?
What Are the Limitations of the DCA Method?
Why Did the Authors Use DCA in This Particular Study?
How Should DCA Findings Be Interpreted in This Particular Study?
Caveats to Consider When Looking at Results Based on DCA
Methods for Evaluating Changes in Health Care Policy---The Difference-in-Differences Approach
183(8)
Use of the Method
Why Was the Difference-in-Differences Method Used?
What Are the Limitations of the Difference-in-Differences Method?
Why Did the Authors Use the Difference-in-Differences Method?
How Should the Findings Be Interpreted?
Caveats to Consider When Assessing the Results of a Difference-in-Differences Analysis
OBSERVATIONAL STUDIES Study Strategy and Design
Case-Control Studies: Using "Real-world" Evidence to Assess Association
191(8)
Explanation of the Method
What Are Case-Control and Nested Case-Control Studies?
Why Are Case-Control Studies Used?
Limitations of Case-Control Studies
How Was the Method Applied in This Case?
How Does the Case-Control Design Affect the Interpretation of the Study?
Meta-analyses Can Be Credible and Useful: A New Standard
199(8)
Overview
The Existing Evidence
Improvements
Conclusions
Mendelian Randomization
207(8)
Use of the Method
Why Is Mendelian Randomization Used?
What Are the Limitations of Mendelian Randomization?
How Did the Authors Use Mendelian Randomization?
Caveats to Consider When Evaluating Mendelian Randomization Studies
Using the E-Value to Assess the Potential Effect of Unmeasured Confounding in Observational Studies
215(8)
Why ls the E-Value Used?
What Are the Limitations of the E-Value?
Why Did the Authors Use the E-Value in This Particular Study?
How Should the E-Value Findings Be Interpreted in This Particular Study?
Caveats to Consider When Looking at Results Based on the E-Value
Assessment of Risk Factors and Exposures
Confounding by Indication in Clinical Research
223(8)
Addressing Confounding in Clinical Research
Use of Methods to Control Confounding
What Are the Limitations of Methods to Control for Confounding?
How Should the Results Be Interpreted?
Caveats to Consider When Interpreting an Analysis Intended to Adjust for Confounding by Indication
Mediation Analysis
231(8)
Use of the Method
Why Is Mediation Analysis Used?
Description of Mediation Analysis
What Are the Limitations of Mediation Analysis?
Why Did the Authors Use Mediation Analysis?
Caveats to Consider When Assessing the Results of Mediation Analysis
Measurement Outcome and Analysis and Interpretation of Results
Odds Ratios---Current Best Practice and Use
239(8)
Why Report Odds Ratios From Logistic Regression?
What Are the Limitations of Odds Ratios?
How Did the Authors Use Odds Ratios?
How Should the Findings Be Interpreted?
What Caveats Should the Reader Consider?
Marginal Effects---Quantifying the Effect of Changes in Risk Factors in Logistic Regression Models
247(8)
Use of Marginal Effects
Why Are Marginal Effects Used?
What Are Marginal Effects?
What Are the Limitations of Marginal Effects?
How Should the Marginal Effects Be Interpreted in Cummings et al?
Adjusting for Covariates: A Source of False Findings in Published Research Studies
255(6)
Treatment Effects in Multicenter Randomized Clinical Trials
261(8)
Estimating Treatment Effects in Multicenter Clinical Trials
Why Are Differences Between Centers Considered
When Estimating Treatment Effects?
How Are Center Effects Incorporated into Estimates of Treatment Effects?
Limitations of Estimates of Treatment Effects from Multicenter Clinical Trials
How Were the Multicenter Data Analyzed in the Study by Dodick et al?
How Should the Results From This Study Be Interpreted?
The Propensity Score
269(8)
Use of the Method
Why Were Propensity Methods Used?
What Are the Limitations of Propensity Score Methods?
Why Did the Authors Use Propensity Methods?
How Should the Findings Be Interpreted?
What Caveats Should the Reader Consider When Assessing the Results of Propensity Analyses?
Using Free-Response Receiver Operating Characteristic Curves to Assess the Accuracy of Machine Diagnosis of Cancer
277(8)
Why Are FROC Curves Used?
How Are FROC Curves Constructed?
What Are the Limitations of FROC Curves?
How Should the FROC Curves Be Interpreted in This Study?
Caveats to Consider When Looking at FROC Curves
Random-Effects Meta-analysis: Summarizing Evidence With Caveats
285(8)
Why Is Random-Effects Meta-analysis Used?
Description of Random-Effects Meta-analysis
Why Did the Authors Use Random-Effects Meta-analysis?
What Are Limitations of a Random-Effects Meta-analysis?
Caveats to Consider When Assessing the Results of a Random-Effects Meta-analysis
How Should the Results of a Random-Effects Meta-analysis Be Interpreted in This Particular Study?
Bayesian Hierarchical Models
293(8)
Why Is a BHM Used?
What Are the Limitations of BHMs?
How Were BHMs Used in This Case?
How Should BHMs Be Interpreted?
Application of Results
Evaluating Discrimination of Risk Prediction Models: The C Statistic
301(8)
Use of the Method
Why Are C Statistics Used?
What Are the Limitations of the C Statistic?
Why Did the Authors Use C Statistics in Their Study?
How Should the Findings Be Interpreted?
Caveats to Consider When Using C Statistics to Assess Predictive Model Performance
Overview of Cost-effectiveness Analysis
309(8)
The Use of Cost-effectiveness Analysis
Description of Cost-effectiveness Analysis
Limitation in the Use of Cost-effectiveness Analysis
How Was the Cost-effectiveness Analysis Performed in This Study?
How Should the Cost-effectiveness Analysis Be Interpreted in This Study?
Choosing a Time Horizon in Cost and Cost-effectiveness Analyses
317(8)
The Use of Time Horizon in a Cost-effectiveness Analysis
Limitations Regarding Selection of Time Horizons
How Was Time Horizon Defined and Used in the Study?
How Does the Time Horizon Selected by Wittenborn et al Affect the Interpretation of the Study?
On Deep Learning for Medical Image Analysis
325(8)
Opening the Deep Learning Black Box
What Are the Limitations of Deep Learning Methods?
PRACTICAL GUIDE TO DATA SETS
A Checklist to Elevate the Science of Surgical Database Research
333(8)
Tips for Analyzing Large Data Sets From the JAMA Surgery Statistical Editors
341(8)
Study Population Considerations
Methodological and Sample Size Considerations
Data Elements and Presentation
Analytic and Statistical Considerations
Conclusions
Practical Guide to Surgical Data Sets: Healthcare Cost and Utilization Project National Inpatient Sample (NIS)
349(10)
Introduction to the Healthcare Cost and Utilization Project
Strengths of Administrative Data
Limitations of Administrative Data and the HCUP Databases
Administrative Data Limitations
NIS Limitations
Critical Methodologic Considerations
Unique Capabilities of HCUP
Practical Guide to Surgical Data Sets: Surveillance, Epidemiology, and End Results (SEER) Database
359(8)
Introduction
Data Considerations
Data Sources
Time Trend Data
Cancer Data
Treatment Data
Statistical Considerations
Conclusions
Practical Guide to Surgical Data Sets: Medicare Claims Data
367(8)
Introduction
Pros and Cons of Medicare Data
Potential Avenues of Research
Comparative Effectiveness Research
Health Policy Evaluation
Understanding Variation
Where to Find More Information
Practical Guide to Surgical Data Sets: Military Health System Tricare Encounter Data
375(8)
Introduction
Use of the Data
Salient and Unique Features of the Data Set
How Are Data Compiled?
What Are Common Outcomes That Can Be Studied?
What Are the Limitations With This Data Set?
Statistical Considerations
Where to Find More Information
Practical Guide to Surgical Data Sets: Veterans Affairs Surgical Quality Improvement Program (VASQIP)
383(8)
Advent of the Veterans Affairs Surgical Quality Improvement Program
Data Considerations
Patients
Procedure
Hospital
Outcomes
Utility and Unique Features of VASQIP
Statistical Considerations
Conclusions
Practical Guide to Surgical Data Sets: National Surgical Quality Improvement Program (NSQIP) and Pediatric NSQIP
391(8)
Introduction
Data Elements and Considerations
Access and Logistics
Variables and Outcomes
Statistical Methodology
Limitations
Conclusions
Practical Guide to Surgical Data Sets: Metabolic and Bariatric Surgery Accreditation and Quality Program (MBSAQIP)
399(8)
Introduction
Data Considerations for the MBSAQIP Participant Use File
Deidentification of Patients, Facilities, and Clinicians
MBSAQIP PUF Content
Outcomes
Statistical Considerations
MBSAQIP PUF Advantages and Limitations
Conclusions
Practical Guide to Surgical Data Sets: National Cancer Database (NCDB)
407(8)
Introduction
Data Element Considerations
Hospital Variables
Tumor Characteristics
Treatment Variables
Outcomes
Analytic and Statistical Considerations
Conclusions
Practical Guide to Surgical Data Sets: National Trauma Data Bank (NTDB)
415(8)
Introduction
Data Compilation and Structure
Methods
Limitations
Recommended Reading
Conclusions
Practical Guide to Surgical Data Sets: Society for Vascular Surgery Vascular Quality Initiative (SVSVQI)
423(8)
Features of the Data Set
Statistical Considerations
Conclusions
Practical Guide to Surgical Data Sets: Society of Thoracic Surgeons (STS) National Database
431(8)
Introduction
Data Element Considerations
Adult Cardiac Surgery Database (ACSD)
Congenital Heart Surgery Database
General Thoracic Surgery Database
Data Source
Outcomes and Other Key Measures
Accessing Data
Statistical Considerations
Limitations
Conclusions
Glossary 439(28)
Index 467
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97812604553352e.html
Märksõnad: