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การบริหารจัดการข้อมูลระบบเครือข่าย โดยใช้ ScanTRIAD PowerPoint PPT Presentation

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การบริหารจัดการข้อมูลระบบเครือข่าย โดยใช้ ScanTRIAD. รศ.ดร. บัณฑิต ถิ่นคำรพ , PhD. (Statistics) ผู้อำนวยการศูนย์บริหารจัดการข้อมูลและสนับสนุนด้านสถิติ ภาควิชาชีวสถิติและประชากรศาสตร์ คณะสาธารณสุขศาสตร์ มหาวิทยาลัยขอนแก่น. ความบังเอิญ (Random error). อคติ ( Bias หรือ Systematic error ).

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การบริหารจัดการข้อมูลระบบเครือข่าย โดยใช้ ScanTRIAD

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.. , PhD. (Statistics)

(Random error)

(Bias Systematic error )

Data Management

= +

[Selection Bias]

[Information Bias]

[Confounding Bias]


Selection Bias [All eligible subjects vs. Lost to follow-up and Non-response]

  • Cancel out in large study

  • Achieve precision by reducing measurement error

Data entry-> Data editing & cleaning -> Back-up storage

Systematic Approach -------> Statistical Coordinating Center


Information Bias [Validity & reliability of tools vs. Data management]

Confounding Bias [All related predictors vs. what were collected]

Systematic Approach

Data Management and Statistical Support

Availability of complete, clean data takes time, effort, and attention to details.

Clean data

Researcher + Statistician

Manual of Operation Plan for data analysis

Statistician + Data manager + Programmer




ICH =International Conference on Harmonisation of

Technical Requirements for Registration of

Pharmaceuticals for Human Use

GCP =Good Clinical Practice

European Union (EU), Japan, USA 1996

Australia; Canada; the Nordic countries; WHO; etc.


Quality assurance and/or control

Prevent problems

Detect problems

Correct problems

Quality assurance elements

  • Prevention

    • Well-written protocol, manual of operations

    • Collection limited to essential items, uncomplicated forms, criteria

    • Pre-test study forms and procedures

    • Investigators commitment to follow protocol

    • Training and certification of all staff

    • Data from central classification committees, specialized equipment (calibration), central laboratories or reading centers (internal replication; external duplication or standards)

    • Maintain study records; audit trails, archiving

Adopted from Shrikant I. Bangdiwala, Ph.D.

Quality assurance elements

  • Detection

    • Central monitoring of data on individual subjects:

      • data entry system checks

      • logical, consistency checks

      • extreme values

    • Site visits: standard check-list, records audit

    • Comprehensive performance-monitoring reports: study overall, by site, by staff

      • recruitment, follow-up, adherence, completion of procedures

      • errors

    • Statistical investigations of aggregate data: by site, by staff

      • identify unusual patterns

      • lack of variability

      • unusual relationships in the data

Adopted from Shrikant I. Bangdiwala, Ph.D.

Quality assurance elements

  • Correction

    • correct the errors and minimize the chance of future occurrences

    • procedures must be implemented early in the study

    • empower individuals, committees, centers to address problems

    • effect of systematic errors, bias, violations of protocol

    • address individual site or staff performance

    • redress misconduct or fraud

    • Document all actions

Adopted from Shrikant I. Bangdiwala, Ph.D.

RDM Processes

Data Entry Design Considerations

Design of data collection forms

Paper-based : , , ,

Electronic-based : CAPI, PDA, Web-based, Applications, Optical Scan, etc

Data collection methods

Self-administered, , ,


Type of projects

Single siteVSMulti-center

Cross-sectional or Longitudinal

Routine data collection


Small size projectVSMega studyVSCountry census

Real-time monitoring, Urgent, Allow sufficient time

RDM Processes

Data Entry Design

Portal of data entry

Distributed data entry

Centralized data entry

Design of data entry interface

DirectVSVia CRF

Key punchingVSMouse clickingVSOptical scan

Spread sheet styleVSWYSIWYG

Data entry, validation, and verification methods

SingleVSDouble data entry

Embedded validation at entryVSValidation externally

Verification tools : PaperVSPrintout, Paper VS Screen,

ScreenVSScreen, Two parts within a screen,

Data and images being integrated(SD and CRF can be integrated)

RDM Processes

  • Data cleaning

    • All variables or key variables?

    • How much computerized vs manual?

    • Consistency checks across variables, across forms, across time, across similar individuals

    • Frequency and timing given rate of accumulation and study needs

    • Audit trail & documentation ALL changes to original data specify what, when, why, by whom

RDM Processes

  • Audit trail & documentation ALL changes to original data specify what, when, why, by whom

  • WHY?

    • Monitor study integrity and quality assurance

      • CC does this separately by personnel, collectively for trends

    • Regulatory agencies wish to compare the information in original data collection forms with that in reports

      • Usually, sample 10% of subjects in database, 100% of data from sampled subjects, and often 100% of subjects for key variables

  • Tolerance of errors: < 25/10000 fields = 0.25%

Main Tasks

Data Management using



Back Office





Front Office

State of the art for quality data entry

Scan and activate OMR & ICR

Check for image error and fix if any

Export both images and data as a ZIP file

Unzip to database server

Login to the client computer

Send data to the server

Load data

Verify data

Save data

Login to the client computer

Check items with validation warnings

Check items based on EDA results

Check items with verification remarks

Data ready for researchers


Feed paper






DVD Backup




Downloading station



DVD Backup



Data Management System with Tools for

Optical Recognition, Verification, and Purification.



Data Verification Center

Data Verification Center

Example that data verification is needed

Example that data verification is needed

Example that data verification is needed

Example that data verification is needed

Example that data verification is needed

Example that data verification is needed

Example that data verification is needed

Example that data verification is needed


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