NEMC Data Validation, Usability & Defensibility. Kesavalu M. Bagawandoss Ph.D., J.D. August 6, 2013

Transcription

1 NEMC 2013 Data Validation, Usability & Defensibility Kesavalu M. Bagawandoss Ph.D., J.D. August 6, 2013

2 Data Validation Data Validation consists of an analyte and sample specific process of evaluating compliance of the laboratory data with methods, procedures or contract requirements. Guidance document for Data Validation is from the USEPA CLP Program 2

3 Data Validation What is required for validation? Level 4 Data package is a mandatory deliverable EDD depending on format of validation software SOP s, QAP s and Methods Specific contracts or SOW s provided to the laboratory or required by client 3

4 Data Validation Two Criteria must be measured: 1. Does the data meet the specific technical and QC criteria established 2. Usability of the data not meeting the technical and QC criteria established If the data does not meet criteria how does one handle the data. 4

5 Data Validation Data Validation requires extensive laboratory systems knowledge and the ability to apply consistent professional judgment during the process. In other words the same data package cannot be validated differently on a different day by the same person. 5

6 Data Validation How to select Data Validators Knowledge, Technical Background Experience (history of prior validation and challenges)(check references) Will the validator stand the legal challenge Can the validator defend the data on your behalf History on legal challenges and background of company 3 rd party data validation vs. internal validation 6

7 Data Validation When do you need validation? Laboratory quality is unknown Laboratory has a history of errors Data is used for litigation Data is used for Risk Assessment (agency requirements need to be adhered to) Project plan requires full validation Routine Monitoring???(under the microscope?) Agency spilt samples 7

8 Data Validation Tools available for data validation Commercial software Databases developed by validators Guidance documents from public sources (validation guidelines established by EPA CLP and others) Most tools are semi-automatic due to professional judgment assessments Establishing standard codes- explanations for each situation of qualification (data qualifier codes) 8

9 Discussions Data Validation Look for trends in Lab performance Formats and requirements of data validators Ethics in data validation and relationships Judgment of Data validators vs. defense of data QA/QC issues and how to handle data Consistency in Data Validation 9

10 Data Validation Discussions (contd.) Under what circumstances do you as a data validator choose to accept data where there were problems with the QA/QC, method selection or sample integrity. Analyst/validators discretion, all data deficiencies are not created equal, where do you draw the lines. 10

11 Data Usability Data usability is an executive summary report of the data based on the validation performed with comments and comparison of site specific analytes and plausibility TCEQ provides guidance for TX as to how the data usability report should be prepared DV report + Narrative = Data Usability Summary 11

12 Data Usability Based on the data usability summary, risk assessment can be performed Data validation coupled with the data usability provides a strong platform for risk assessment with ruggedness Further, the defensibility of data is enhanced due to the rigorous scrutiny of the data from inception to conclusion of the project and beyond 12

13 Data Defensibility Data defensibility starts with the initiation of sampling in the field Prior to sampling a solid sampling and analysis plan must be prepared and approved All parties must be involved with the conception of the project Parties must include both the stake holders and all other players including data validators 13

14 Data Defensibility The standard for defensibility is based on the Daubert case, wherein the US Supreme Court outlines a list of factors The Code of Evidence also spells out expert qualifications as well as individual State rules Any method could be defended as long as they can confirm to the Daubert factors outlined by the Court Daubert v. Merrel Dow Pharmaceuticals, Inc., 509 U.S. 579,

15 Data Defensibility The Daubert factors are: (1) whether the expert's theory has been tested; (2) whether the theory has been subject to peer review and publication; (3) the known or potential rate of error of a technique or theory when applied; (4) the existence and maintenance of standards and controls; and (5) the degree to which the technique or theory has been generally accepted in the scientific community. See Moore v. Ashland Chem. Inc., 151 F.3d 269, 275 (5th Cir.1998) (citing Daubert, 509 U.S. at , 113 S.Ct. at (1993)). 15

16 Data Defensibility In general an expert identified must have knowledge of the data as well as the project requirements and the expertise required for the analytical methods utilized The ideal person would be someone with experience in the subject matter and has validated the data and understands the intricacies of the data subject to cross examination providing the trier of facts the evidence to gain the truth in the matter 16

17 Data Defensibility Just because a person has been qualified as an expert in another case does not mean that he can be qualified as an expert in the case at hand The expert is still subject to challenges before trial to be qualified as an expert Louisiana Law allows for the opponent to challenge the qualifications of an expert 30 days prior to trial before the trial judge in any case. 17

18 References fg.pdf emo-%20sf%20data%20validation%20guidance.pdf Daubert v. Merrel Dow Pharmaceuticals, Inc., 509 U.S. 579, TCEQ data usability guidelines TCEQ publication RG- 366/TRRP-13, May