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In recent years the number of innovative medicinal products and devices submitted and approved by regulatory bodies has declined dramatically. The medical product development process is no longer able to keep pace with increasing technologies, science and innovations and the goal is to develop new scientific and technical tools and to make product development processes more efficient and effective. Statistical Methods in Healthcare focuses on the application of statistical methodologies to evaluate promising ...

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Statistical Methods in Healthcare

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In recent years the number of innovative medicinal products and devices submitted and approved by regulatory bodies has declined dramatically. The medical product development process is no longer able to keep pace with increasing technologies, science and innovations and the goal is to develop new scientific and technical tools and to make product development processes more efficient and effective. Statistical Methods in Healthcare focuses on the application of statistical methodologies to evaluate promising alternatives and to optimize the performance and demonstrate the effectiveness of those that warrant pursuit is critical to success. Statistical methods used in planning, delivering and monitoring health care, as well as selected statistical aspects of the development and/or production of pharmaceuticals and medical devices are also addressed.

With a focus on finding solutions to these challenges, this book:

  • Provides a comprehensive, in-depth treatment of statistical methods in healthcare, along with a reference source for practitioners and specialists in health care and drug development.
  • Offers a broad coverage of standards and established methods through leading edge techniques.
  • Uses an integrated, case-study based approach, with focus on applications.
  • Looks at the use of analytical and monitoring schemes to evaluate therapeutic performance.
  • Features the application of modern quality management systems to clinical practice, and to pharmaceutical development and production processes.
  • Addresses the use of modern Statistical methods such as Adaptive Design, Seamless Design, Data Mining, Bayesian networks and Bootstrapping that can be applied to support the challenging new vision.

Practitioners in healthcare-related professions, ranging from clinical trials to care delivery to medical device design, as well as statistical researchers in the field, will benefit from this book.

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Product Details

  • ISBN-13: 9781119942047
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 7/24/2012
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 1
  • Pages: 520
  • File size: 5 MB

Table of Contents



Editors in Chief


Part One Statistics in Development of Pharmaceutical Products

1 Statistical Aspects in ICH, FDA and EMA Guidelines

Allan Sampson and Ron S. Kenett


1.1 Introduction

1.2 ICH Guidelines Overview

1.3 ICH Guidelines for Determining Efficacy

1.4 ICH Quality Guidelines

1.5 Other Guidelines

1.6 Statistical Challenges in Drug Products Development and Manufacturing

1.7 Summary


2 Statistical Methods in Clinical Trials

Telba Irony, Caiyan Li and Phyllis Silverman


2.1 Introduction

2.1.1 Claims

2.1.2 Endpoints

2.1.3 Types of Study Designs and Controls

2.2 Hypothesis Testing, Significance Levels, p-values, Power and Sample Size

2.2.1 Hypothesis Testing

2.2.2 Statistical Errors, Significance Levels and p-values

2.2.3 Confidence Intervals

2.2.4 Statistical Power and Sample Size

2.3 Bias, Randomization and Blinding/Masking

2.3.1 Bias

2.3.2 Randomization

2.3.3 Blinding or Masking

2.4 Covariate Adjustment and Simpson’s Paradox

2.4.1 Simpson’s Paradox

2.4.2 Statistical Methods for Covariate Adjustment

2.5 Meta-analysis, Pooling and Interaction

2.5.1 Meta-analysis

2.5.2 Pooling and Interaction

2.6 Missing Data, Intent-to-treat and Other Analyses Cohorts

2.6.1 Missing Data

2.6.2 Intent-to-treat (ITT) and Other Analysis Cohorts

2.7 Multiplicity, Subgroup and Interim Analyses

2.7.1 Multiplicity

2.7.2 Subgroup Analyses

2.7.3 Interim Analyses

2.8 Survival Analyses

2.8.1 Estimating Survival Functions

2.8.2 Comparison of Survival Functions

2.9 Propensity Score

2.10 Bayesian Versus Frequentist Approaches to Clinical Trials

2.11 Adaptive Designs

2.11.1 Sequential Designs

2.12 Drugs Versus Devices


Further Reading

3 Pharmacometrics in Drug Development

Serge Guzy and Robert Bauer


3.1 Introduction

3.1.1 Pharmacometrics Definition

3.1.2 Dose-response Relationship

3.1.3 FDA Perspective of Pharmacometrics

3.1.4 When Should We Perform Pharmacometric Analysis?

3.1.5 Pharmacometric Software Tools

3.1.6 Organization of the Chapter

3.2 Pharmacometric Components

3.2.1 Pharmacokinetics (PK)

3.2.2 Pharmacodynamics (PD)

3.2.3 Disease Progression

3.2.4 Simulation of Clinical Trials

3.3 Pharmacokinetic/Pharmacodynamic Analysis

3.3.1 Compartmental Methods

3.4 Translating Dynamic Processes Into a Mathematical Framework

3.5 Nonlinear Mixed-effect Modeling

3.6 Model Formulation and Derivation of the Log-likelihood

3.7 Review of the Most Important Pharmacometric Software Characteristics

3.7.1 NONMEM

3.7.2 PDx-MC-PEM


3.7.4 WinBUGS

3.7.5 S-ADAPT

3.8 Maximum Likelihood Method of Population Analysis

3.9 Case Study: Population PK/PD Analysis in Multiple Sclerosis Patients

3.9.1 Study Design

3.9.2 Model Building

3.9.3 The PK Model

3.9.4 Platelet Modeling

3.9.5 T1 Lesions Model

3.10 Mathematical Description of the Dynamic Processes Characterizing the PK/Safety/Efficacy System

3.10.1 Optimization Procedure and Phase 2b Simulation Procedures

3.10.2 Clinical Simulation Results and Discussion

3.10.3 Calculation of the Cumulative Number of T1 Lesions and the Percentage MRI Improvement

3.10.4 Estimation of the Percentage of Patients to Reach Platelet Counts Below a Certain Threshold Value

3.10.5 Tentative Proposal for the Phase 2b Trial Design

3.11 Summary

3.11 References

4 Interactive Clinical Trial Design

Zvia Agur


4.1 Introduction

4.2 Development of the Virtual Patient Concept

4.2.1 The Basic Virtual Patient Model

4.3 Use of the Virtual Patient Concept to Predict Improved Drug Schedules

4.3.1 Modeling Vascular Tumor Growth

4.3.2 Synthetic Human Population (SHP)

4.4 The Interactive Clinical Trial Design (ICTD) Algorithm

4.4.1 Preclinical Phase: Constructing the PK/PD Module

4.4.2 Phase I: Finalizing and Validating the PK/PD Module

4.4.3 Interim Stage Between Phase I and Phase II: Intensive Simulations of Short-term Treatments

4.4.4 Phase II and Phase III: Focusing the Clinical Trials

4.4.5 Interactive Clinical Trial Design Method as Compared to Adaptive Clinical Trial Design Methods

4.5 Summary



5 Stage-wise Clinical Trial Experiments in Phases I, II and III

Shelemyahu Zacks


5.1 Introduction

5.2 Phase I Clinical Trials

5.2.1 Up-and-down Adaptive Designs in Search of the MTD

5.2.2 The Continuous Reassessment Method

5.2.3 Efficient Dose Escalation Scheme With Overdose Control (EWOC)

5.3 Adaptive Methods for Phase II Trials

5.3.1 Individual Dosing

5.3.2 Termination of Phase II

5.4 Adaptive Methods for Phase III

5.4.1 Randomization in Clinical Trials

5.4.2 Adaptive Randomization Procedures

5.4.3 Group Sequential Methods: Testing Hypotheses

5.5 Summary


6 Risk Management in Drug Manufacturing and Healthcare

Ron S. Kenett


6.1 Introduction to Risks in Healthcare and Trends in Reporting Systems

6.2 Reporting Adverse Events

6.3 Risk Management and Optimizing Decisions With Data

6.3.1 Introduction to Risk Management

6.3.2 Bayesian Methods in Risk Management

6.3.3 Basics of Financial Engineering and Risk Management

6.3.4 Black Swans and the Taleb Quadrants

6.4 Decision Support Systems for Managing Patient Healthcare Risks

6.5 The Hemodialysis Case Study

6.6 Risk-based Quality Audits of Drug Manufacturing Facilities

6.6.1 Background on Facility Quality Audits

6.6.2 Risk Dimensions of Facilities Manufacturing Drug Products

6.6.3 The Site Risk Assessment Structure

6.7 Summary


7 The Twenty-first Century Challenges in Drug Development

Yafit Stark


7.1 The FDA’s Critical Path Initiative

7.2 Lessons From 60 Years of Pharmaceutical Innovation

7.2.1 New-drug Performance Statistics

7.2.2 Currently There are Many Players, but Few Winners

7.2.3 Time to Approval – Standard New Molecular Entities

7.3 The Challenges of Drug Development

7.3.1 Clinical Trials

7.3.2 The Critical-path Goals

7.3.3 Three Dimensions of the Critical Path

7.3.4 A New-product Development Toolkit

7.3.5 Towards a Better Safety Toolkit

7.3.6 Tools for Demonstrating Medical Utility

7.4 A New Era in Clinical Development

7.4.1 Advancing New Technologies in Clinical Development

7.4.2 Advancing New Clinical Trial Designs

7.4.3 Advancing Innovative Trial Designs

7.4.4 Implementing Pharmacogenomics (PGx) During All Stages of Clinical Development

7.5 The QbD and Clinical Aspects

7.5.1 Possible QbD Clinical Approach

7.5.2 Defining Clinical Design Space

7.5.3 Clinical Deliverables to QbD

7.5.4 Quality by Design in Clinical Development


Part Two Statistics in Outcomes Analysis

8 The Issue of Bias in Combined Modelling and Monitoring of Health Outcomes

Olivia A. J. Grigg


8.1 Introduction

8.1.1 From the Industrial Setting to the Health Setting: Forms of Bias and the Flexibility of Control Charts

8.1.2 Specific Types of Control Chart

8.2 Example I: Re-estimating an Infection Rate Following a Signal

8.2.1 Results From a Shewhart and an EWMA Chart

8.2.2 Results From a CUSUM, and General Concerns About Bias

8.2.3 More About the EWMA as Both a Chart and an Estimator

8.3 Example II: Correcting Estimates of Length-of-stay Measures

to Protect Against Bias Caused by Data Entry Errors

8.3.1 The Multivariate EWMA Chart

8.3.2 A Risk Model for Length of Stay Given Patient Age and Weight 1

8.3.3 Risk Adjustment

8.3.4 Results From a Risk-adjusted Multivariate EWMA Chart

8.3.5 Correcting for Bias in Estimation Through Regression

8.4 Discussion


9 Disease Mapping

Annibale Biggeri and Dolores Catelan


9.1 Introduction

9.2 Epidemiological Design Issues

9.3 Disease Tracking

9.4 Spatial Data

9.5 Maps

9.6 Statistical Models

9.7 Hierarchical Models for Disease Mapping

9.7.1 How to Choose Priors in Disease Mapping?

9.7.2 More on the BYM Model and the Clustering Term

9.7.3 Model Checking

9.8 Multivariate Disease Mapping

9.9 Special Issues

9.9.1 Gravitational Models

9.9.2 Wombling

9.9.3 Some Specific Statistical Modeling Examples

9.9.4 Ecological Bias

9.9.5 Area Profiling

9.10 Summary


10 Process Indicators and Outcome Measures in the Treatment of Acute Myocardial Infarction Patients

Alessandra Guglielmi, Francesca Ieva, Anna Maria Paganoni and Fabrizio Ruggeri


10.1 Introduction

10.2 A Semiparametric Bayesian Generalized Linear Mixed Model

10.3 Hospitals’ Clustering

10.4 Applications to AMI Patients

10.5 Summary


11 Meta-analysis

Eva Negri


11.1 Introduction

11.2 Formulation of the Research Question and Definition of Inclusion/Exclusion Criteria

11.3 Identification of Relevant Studies

11.4 Statistical Analysis

11.5 Extraction of Study-specific Information

11.6 Outcome Measures

11.6.1 Binary Outcome Measures

11.6.2 Continuous Outcome Measures

11.7 Estimation of the Pooled Effect

11.7.1 Fixed-effect Models

11.7.2 Random-effects Models

11.7.3 Random-effects vs. Fixed-effects Models

11.8 Exploring Heterogeneity

11.9 Other Statistical Issues

11.10 Forest Plots

11.11 Publication and Other Biases

11.12 Interpretation of Results and Report Writing

11.13 Summary


Part Three Statistical Process Control in Healthcare

12 The Use of Control Charts in Healthcare

William H. Woodall, Benjamin M. Adams and James C. Benneyan


12.1 Introduction

12.2 Selection of a Control Chart

12.2.1 Basic Shewhart-type Charts

12.2.2 Use of CUSUM and EWMA Charts

12.2.3 Risk-adjusted Monitoring

12.3 Implementation Issues

12.3.1 Overall Process Improvement System

12.3.2 Sampling Issues

12.3.3 Violations of Assumptions

12.3.4 Measures of Control Chart Performance

12.4 Certification and Governmental Oversight Applications

12.5 Comparing the Performance of Healthcare Providers

12.6 Summary



13 Common Challenges and Pitfalls Using SPC in Healthcare

Victoria Jordan and James C. Benneyan


13.1 Introduction

13.2 Assuring Control Chart Performance

13.3 Cultural Challenges

13.3.1 Philosophical and Statistical Literacy

13.3.2 Acceptable Quality Levels

13.4 Implementation Challenges

13.4.1 Data Availability and Accuracy

13.4.2 Rational Subgroups

13.4.3 Specification Threshold Approaches

13.4.4 Establishing Versus Maintaining Stability

13.5 Technical Challenges

13.5.1 Common Errors

13.5.2 Subgroup Size Selection

13.5.3 Over-use of Supplementary Rules

13.5.4 g Charts

13.5.5 Misuse of Individuals Charts

13.5.6 Distributional Assumptions

13.6 Summary


14 Six Sigma in Healthcare

Shirley Y. Coleman


14.1 Introduction

14.2 Six Sigma Background

14.3 Development of Six Sigma in Healthcare

14.4 The Phases and Tools of Six Sigma

14.5 DMAIC Overview

14.5.1 Define

14.5.2 Measure

14.5.3 Analyse

14.5.4 Improve

14.5.5 Control

14.5.6 Transfer

14.6 Operational Issues of Six Sigma

14.6.1 Personnel

14.6.2 Project Selection

14.6.3 Training

14.6.4 Kaizen Workshops

14.6.5 Organisation of Training

14.7 The Way Forward for Six Sigma in Healthcare

14.7.1 Variations

14.7.2 Six Sigma and the Complementary Methodology

of Lean Six Sigma

14.7.3 Implementation Issues

14.7.4 Implications of Six Sigma for Statisticians

14.8 Summary


15 Statistical Process Control in Clinical Medicine

Per Winkel and Nien Fan Zhang


15.1 Introduction

15.2 Methods

15.2.1 Control Charts

15.2.2 Measuring the Quality of a Process

15.2.3 Logistic Regression

15.2.4 Autocorrelation of Process Measurements

15.2.5 Simulation

15.3 Clinical Applications

15.3.1 Measures and Indicators of Quality of Healthcare

15.3.2 Applications of Control Charts

15.4 A Cautionary Note on the Risk-adjustment of Observational Data

15.5 Summary

Appendix A

15.A.1 The EWMA Chart

15.A.2 Logistic Regression

15.A.3 Autocovariance and Autocorrelation



Part Four Applications to Healthcare Policy and Implementation

16 Modeling Kidney Allocation: A Data-driven Optimization Approach

Inbal Yahav


16.1 Introduction

16.1.1 Literature Review

16.2 Problem Description

16.2.1 Notation

16.2.2 Choosing Objectives

16.3 Proposed Real-time Dynamic Allocation Policy

16.3.1 Stochastic Optimization Formulation

16.3.2 Knowledge-based Real-time Allocation Policy

16.4 Analytical Framework

16.4.1 Data

16.4.2 Model Estimation

16.5 Model Deployment

16.5.1 Stochastic Optimization Analysis

16.5.2 Knowledge-based Real-time Policy

16.6 Summary



17 Statistical Issues in Vaccine Safety Evaluation

Patrick Musonda


17.1 Background

17.2 Motivation

17.3 The Self-controlled Case Series Model

17.4 Advantages and Limitations

17.5 Why Use the Self-controlled Case Series Method

17.6 Other Case-only Methods

17.7 Where the Self-controlled Case Series Method Has Been Used

17.8 Other Issues That were Explored in Improving the SCCM

17.9 Summary of the Chapter


18 Statistical Methods for Healthcare Economic Evaluation

Caterina Conigliani, Andrea Manca and Andrea Tancredi


18.1 Introduction

18.2 Statistical Analysis of Cost-effectiveness

18.2.1 Incremental Cost-effectiveness Plane, Incremental Cost-effectiveness Ratio and Incremental Net Benefit

18.2.2 The Cost-effectiveness Acceptability Curve

18.3 Inference for Cost-effectiveness Data From Clinical Trials

18.3.1 Bayesian Parametric Modelling

18.3.2 Semiparametric Modelling and Nonparametric Statistical Methods

18.3.3 Transformation of the Data

18.4 Complex Decision Analysis Models

18.4.1 Markov Models

18.5 Further Extensions

18.5.1 Probabilistic Sensitivity Analysis and Value of Information Analysis

18.5.2 The Role of Bayesian Evidence Synthesis

18.6 Summary


19 Costing and Performance in Healthcare Management

Rosanna Tarricone and Aleksandra Torbica


19.1 Introduction

19.2 Theoretical Approaches to Costing Healthcare Services: Opportunity Cost and Shadow Price

19.3 Costing Healthcare Services

19.3.1 Measuring Full Costs of Healthcare Services

19.3.2 Definition of the Cost Object (Output)

19.3.3 Classification of Cost Components (Direct vs. Non-direct Costs)

19.3.4 Selection of Allocation Methods

19.3.5 Calculation of Full Costs

19.4 Costing for Decision Making: Tariff Setting in Healthcare

19.4.1 General Features of Cost-based Pricing and Tariff Setting

19.4.2 Cost-based Tariff Setting in Practice: Prospective Payments System for Hospital Services Reimbursement

19.5 Costing, Tariffs and Performance Evaluation

19.5.1 Definition of Final Cost Object

19.5.2 Classification and Evaluation of Cost Components

19.5.3 Selection of Allocative Methods and Allocative Basis

19.5.4 Calculation of the Full Costs

19.5.5 Results

19.6 Discussion

19.7 Summary


Part Five Applications to Healthcare Management

20 Statistical Issues in Healthcare Facilities Management

Daniel P. O’Neill and Anja Drescher


20.1 Introduction

20.2 Healthcare Facilities Management

20.2.1 Description

20.2.2 Relevant Data

20.3 Operating Expenses and the Cost Savings Opportunities Dilemma

20.4 The case for Baselining

20.5 Facilities Capital . . . is it Really Necessary?

20.5.1 Facilities Capital Management

20.5.2 A Census of Opportunities

20.5.3 Prioritization and Efficiency Factors

20.5.4 Project Management

20.6 Defining Clean, Orderly and in Good Repair

20.6.1 Customer Focus

20.6.2 Metrics and Methods

20.7 A Potential Objective Solution

20.8 Summary


21 Simulation for Improving Healthcare Service Management

Anne Shade


21.1 Introduction

21.2 Talk-through and Walk-through Simulations

21.3 Spreadsheet Modelling

21.4 System Dynamics

21.5 Discrete Event Simulation

21.6 Creating a Discrete Event Simulation

21.7 Data Difficulties

21.8 Complex or Simple?

21.9 Design of Experiments for Validation, and for Testing Robustness

21.10 Other Issues

21.11 Case Study No. 1: Simulation for Capacity Planning

21.12 Case Study No. 2: Screening for Vascular Disease

21.13 Case Study No. 3: Meeting Waiting Time Targets in Orthopaedic Care

21.14 Case Study No. 4: Bed Capacity Implications Model (BECIM)

21.15 Summary


22 Statistical Issues in Insurance/payor Processes

Melissa Popkoski


22.1 Introduction

22.2 Prescription Drug Claim Processing and Payment

22.2.1 General Process: High-level Outline

22.2.2 Prescription Drug Plan Part D Claims Payment Process

22.3 Case Study: Maximizing Part D Prescription Drug Claim


22.4 Looking Ahead

22.5 Summary


23 Quality of Electronic Medical Records

Dario Gregori and Paola Berchialla


23.1 Introduction

23.2 Quality of Electronic Data Collections

23.2.1 Administrative Databases

23.2.2 Health Surveys

23.2.3 Patient Medical Records

23.2.4 Clinical Trials

23.2.5 Clinical Epidemiology Studies

23.3 Data Quality Issues in Electronic Medical Records

23.4 Procedure to Enhance Data Quality

23.4.1 Clinical Vocabularies

23.4.2 Ontologies

23.4.3 Potential Technical Challenges for EMR Data Quality

23.4.4 Data Warehousing

23.5 Form Design and On-entry Procedures

23.5.1 Data Capture

23.5.2 Data Input

23.5.3 Error Prevention

23.5.4 Physician-entered Data

23.6 Quality of Data Evaluation

23.7 Summary



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