24 Nov 2007Data Management and Exploratory Data Analysis 1 Yongyuth Chaiyapong Ph.D. (Mathematical Statistics) Department of Statistics Faculty of Science.

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Presentation transcript:

24 Nov 2007Data Management and Exploratory Data Analysis 1 Yongyuth Chaiyapong Ph.D. (Mathematical Statistics) Department of Statistics Faculty of Science Chiang Mai University

24 Nov 2007Data Management and Exploratory Data Analysis 2 Data Management Data: Characteristics or Attributes of Interested That are Observed From Population or Sampled units Manage: to Handle, Direct, Govern, or Control in Action or Use

24 Nov 2007Data Management and Exploratory Data Analysis 3 Accomplishments Post Graduate Study and Research Fact or Conclusion Immediate and Long Term Utilization Reliability of Conclusion Error Cost Efficiency

24 Nov 2007Data Management and Exploratory Data Analysis 4 Research and Statistics Deductive and Inductive Methods Statistics Science Science of Data Inference about Population (s) of Interest Conclusions

24 Nov 2007Data Management and Exploratory Data Analysis 5 Statistical Inference Inductive Method Error and Reliability Quality of Research Outcomes

24 Nov 2007Data Management and Exploratory Data Analysis 6 Error Source Error Error Measurement Error Control

24 Nov 2007Data Management and Exploratory Data Analysis 7 Type of Error Sampling Error Non-sampling Error

24 Nov 2007Data Management and Exploratory Data Analysis 8 Sources of Sampling Error –Sample Size –Population Size –Variance of Population

24 Nov 2007Data Management and Exploratory Data Analysis 9 Statistical Methodology Point Estimation Interval Estimation Hypotheses Testing Statistical Modeling

24 Nov 2007Data Management and Exploratory Data Analysis 10 Measurement of Sampling Error –Variance –Type I and Type II Errors –Coverage Probability

24 Nov 2007Data Management and Exploratory Data Analysis 11 Sampling Error Control –Research Design –Statistical Methodology –Design of Data Collection –Data Analysis

24 Nov 2007Data Management and Exploratory Data Analysis 12 Non-sampling Error Data Collection Concepts and Definitions Data Entry and Verification Editing and Updating Data Processing

24 Nov 2007Data Management and Exploratory Data Analysis 13 Measurement of Non-sampling Error Difficult to Detect No Measurement No Statistical Theory

24 Nov 2007Data Management and Exploratory Data Analysis 14 Non-sampling Error Control Quality Assurance Quality Control

24 Nov 2007Data Management and Exploratory Data Analysis 15 Biomedical Research Cycle New Ideas Protocol & Funding Design Study Approval & Preparation Activate Study Findings Conduct Study for patient care & policy Clinical Care for clinical research Utilize Results for basic research Source: Ida Sim, UCSF trial simulators trial costing protocol authoring data mgmt plan IRB approval CRF design data acquisition & management GCP compliance data processing & analysis reporting

24 Nov 2007Data Management and Exploratory Data Analysis 16 Quality Assurance Describes the Systems and Processes Established to Ensure that the Data are Collected in Compliance with the Standard Requirement

24 Nov 2007Data Management and Exploratory Data Analysis 17 Quality Control (QC) The Operational Techniques and Activities Undertaken to Verify that the Requirements for Quality of the Research have been Fulfilled No errors, Inconsistencies, or Omissions

24 Nov 2007Data Management and Exploratory Data Analysis 18 Research Data Management Principles What is Research Data Management? a process that begins with conception and design of a research project, continues through data capture and analysis to publication, data archiving and data sharing with the broader scientific community. Data Design Data Acquisition and Quality Control Data Access and Archiving Data Analysis and Interpretation Data Manipulation and Quality Assurance Project Initiation Publication

24 Nov 2007Data Management and Exploratory Data Analysis 19 Functions of Data Management in Biomedical Research (Clinical Trial) Results Data Management Staff: Data Cleanup Data QC Data Entry Database Design Time Study Initiation Site Development Protocol Development Analysis Research Data Management Clinic Clinic/Site Staff: Data Closure Data Correction Data Collection GCP Compliance

24 Nov 2007Data Management and Exploratory Data Analysis 20 Research Data Management Principles Data Acquisition Data Storage Database Validation, Programming and Standards Data Entry and Data Processing Safety Data Management and Reporting Measuring Data Quality Assuring Data Quality Database Closure

24 Nov 2007Data Management and Exploratory Data Analysis 21 Data Management Data Design Data Acquisition Data Entry Data Quality

24 Nov 2007Data Management and Exploratory Data Analysis 22 Source Document & CRF (2 O Data Source) Hospital data collection form Medical records Laboratory results CRF(s)

24 Nov 2007Data Management and Exploratory Data Analysis 23 Standard Data Management Flow Data Entry Data Validation Database Lock Data Analysis & Report Data Collection Paper CRF Data Clarification Paper DCF Data Validation Database Lock Data Analysis & Report (eSubmission) Data Collection eCRF Internet Web Server Internet Data Clarification eDCF

24 Nov 2007Data Management and Exploratory Data Analysis 24 Data Validation Standards in Data Validation: · Making sure that the raw data were accurately entered into a computer-readable file. · Checking that character variables contain only valid values. · Checking that numeric values are within predetermined ranges. · Checking for and eliminating duplicate data entries. · Checking if there are missing values for variables where complete data are necessary. · Checking for uniqueness of certain values, such as subject ID's. · Checking for invalid date values and invalid date sequences. · Verifying that complex multi- file [or cross panel] rules have been followed. For example, if an adverse event of type X occurs, other data such as concomitant medications or procedures might be expected.

24 Nov 2007Data Management and Exploratory Data Analysis 25 Common Types of Data Problem/Error Data type Size / Out-of-range values Missing data Coding error Spelling error /Illegibility Inconsistency Errors in conduct of protocol