TOC Assay Eval.

Krouwer Consulting

	Preface	ix
	Acknowledgements	xi
Chapter 1	Introduction	1
	The Business of Diagnostic Testing	1
	Quality Has Improved--Yet Physicians Rely More on Test Results	1
	Lifescan: One Manufacturer's Problem	1
	University of Washington Medical Center: A Laboratory Problem	2
	Analysis of the Two Problems	2
	Why Problems Occur	3
	Financial Incentives Don't Favor Allocating Resources to Quality	3
	Industry-supported Studies Favor Low-information-content Reports	4
	Corporate Culture	4
	Inertia	4
	How This Book Can Help	5
*Appendix*	The Role of Assay Error in Decisions to Approve Assays for Use	6
Chapter 2	The Diagnostic Assay Development Landscape and the Role of Consultants	7
	The Technical Environment	7
	The Commercial Environment	8
	The Regulatory and Medical Environment	8
	The Management Environment	8
	The Five Stages of Product Development	9
	The Consultant's Environment--How Consultants Get Their Solutions Implemented	10
	How Statisticians Are Often Perceived and What They Really Do	10
	When Management Resists--Techniques Used by Consultants to Implement Solutions	11
	It's About Control	12
	The Successful Consulting Cycle	13
	Technology Transfer--The Benefits of a Learning Organization	13
	Training as Part of a Learning Organization	14
Chapter 3	Stage I: Researching New Opportunities	17
	Why Scientists and Engineers Need to Understand Financial Models	17
	Using Decision-analysis-based Financial Models to Value Opportunities	18
	Decision-analysis Background and Terms	18
	Selecting Decision-analysis Software	19
	Creating the Decision-analysis Team	19
	Preparing an Influence Diagram	19
	Techniques to Solicit Unbiased Data	21
	Performing the Analysis--The Results	22
	The Base Case	22
	Sensitivity Analysis	22
	Distribution Analysis	23
	Techniques to Improve Decision-analysis Models	24
	The Use of Options	24
	Markov Analysis	25
	Methods to Evaluate the Probability of the Technical Success of Opportunities	27
	Different States of Knowledge Require Different Strategies	28
	Results Based on Decision Analysis	28
	Why Management Always Wants the Product Released Sooner	28
	Why Quality Ranks Low in Terms of Financial Rewards	30
	Portfolio Analysis	31
	A Caveat About Using Decision Analysis	31
Appendix	How Expected NPVs Are Calculated	31
Chapter 4	Stage II: Proving Feasibility	33
	Setting Performance Specifications Using Quantitative Methods	33
	The Importance of Adequate Performance Specifications	33
	Adequate and Less-than-adequate Performance Specifications	34
	Nonexistent Specifications	34
	Nonquantitative Specifications	35
	Unrealistic Specifications	35
	Incorrect Specifications	36
	Specifications Without an Associated Testing and Analysis Method	36
	Characteristics of an Adequate Performance Specification	37
	An Example of a Performance Specification for Blood Gas Analyzer Glucose Imprecision	37
	How Specifications Change Through the Development Process	37
	Different Origins of Performance Specifications	37
	Regulatory	38
	Medical Need	38
	Competitive	38
	How Specifications Are Used Differently by Manufacturers and Customers	39
	Specific Techniques Used to Set Performance Specifications	40
	Focus Groups and Surveys	40
	Conjoint Analysis	41
	Quality Function Deployment	42
	Different Approaches to Demonstrating Feasibility	45
	Beware the Technical Administrator	45
Chapter 5	Stage III: Scheduled Development	49
	Why Products Are Almost Always Late	49
	Using Design of Experiments Methods to Build Robust Assays	52
	Why Many Scientists Do not Use Design of Experiments (DOE) Methods	52
	Cause-and-effect Diagrams and Process Flow Charts	53
	Factorial and Response-surface Methods	54
	Experiment-planning Checklist	56
	Writing Reports That Convert Data into Information	57
	The Need for Written Reports	57
	Tips for Converting Data into Information	57
	A Suggested Report Format	58
	Symptoms for Problem Reports	59
	Using Reliability Growth Management to Build Reliable Systems	59
	An Overview of Reliability Growth Management	60
	When "Testing in Quality" Is More Efficient Than "Designing in Quality"	61
	A Model of Instrument System Service Calls	61
	Redundancy and Reliability Goals	62
	FRACAS	62
	Data Analysis	63
	Corrective Action	63
	Measuring Progress	64
	Results Achieved with Reliability Growth Management	66
Chapter 6	Stage IV: Validation	69
	Why Many Published Validation Methods Fall Short in Assessing Assay Quality	69
	Validation Methods Within Companies	69
	Error Modeling Using Simulation	69
	A Block Diagram or Flow Chart of the System	70
	A Cause-and-effect Diagram of Error	70
	How the Error Model Works	71
	The Simulation Software	72
	Total Analytical Error	72
	Multifactor Protocols	76
	Introduction	76
	Multifactor Protocol History	76
	Understanding Multifactor Protocols	76
	Use and Interpretation of Multifactor Protocols	79
	Examples	79
	Implementation	82
	Additional Special Studies	82
	Diagnostic Accuracy	82
	The Detection Limit	83
	Specific Interference Studies	83
	Direct vs. Indirect Methods of Estimation	83
	Estimation of Outliers	83
	Outlier Goals	84
	Estimation of Outlier Rates	85
	External (Customer) Validation Methods	88
	Why Manufacturer Trials Held at Customer Sites Often Fail to Detect Problems	88
Chapter 7	Stage V: Commercialization	91
	How Claims Differ from Internal Specifications	91
	The Typical Data Claim	91
	The Guaranteed Performance Claim	92
	Obtaining and Analyzing Remote Data	93
	Fault Detection	93
	Data Collection	93
	Data Analysis	94
	The Mean Cumulative Repair Function	94
	Using Process Capability to Compare Performance Across Assays	96
	The Difference Between Quality Control and Process Capability	97
	An Example Set of Assays Compared	97
	Interpretation	98
	Using Complaints to Improve Assays	101
	Index	103