Showing papers on "Electronic data capture published in 2002"

A manager's guide to the design and conduct of clinical trials

Abstract: 1. Cut Costs and Increase Profits. No Excuse for the Wastage. Front-Loaded Solution. Downsizing. Think Transnational. A Final Word. 2. Guidelines. Start with Your Reports. The Wrong Way. Keep It in the Computer. Don't Push the River. KISS. Plug the Holes as They Arise. Pay for Results, Not Intentions. Plan, Do, Then Check. PART I PLAN. 3. Prescription for Success. Plan. A. Predesign Phase. B. Design the Trials. Do. C. Obtain Regulatory Agency Approval for the Trials. D. Form the Implementation Team. E. Line Up Your Panel of Physicians. F. Develop the Data Entry Software. G. Test the Software. H. Train. I. Recruit Patients. J. Set Up External Review Committees. K. Conduct the Trials. L. Develop Suite of Programs for Use in Data Analysis. M. Analyze and Interpret the Data. Check. N. Complete the Submission. 4. Staffing for Success. The People You Need. Design Team. Obtain Regulatory Approval for the Trials. Track Progress. Implementation Team. Develop Data Entry Software. Test the Software. Line Up Your Panel of Physicians. External Laboratories. Site Coordinators. External Review Committees. Recruit and Enroll Patients. Transnational Trials. Conduct the Trials. Programs for Data Analysis. Analyze and Interpret the Data. The People You Don't Need. For Further Information. 5. Design Decisions. Should the Study Be Performed? Should the Trials Be Transnational? Study Objectives. End Points. Secondary End Points. Should We Proceed with a Full-Scale Trial? Tertiary End Points. Baseline Data. Who Will Collect the Data? Quality Control. Study Population. Timing. Closure. Planned Closure. Unplanned Closure. Be Defensive. Review, Rewrite, Review Again. Checklist for Design. Budgets and Expenditures. For Further Information. 6. Trial Design. Baseline Measurements. Controlled Randomized Clinical Trials. Randomized Trials. Blocked Randomization. Stratified Randomization. Single- vs. Double-Blind Studies. Allocation Concealment. Exceptions to the Rule. Sample Size. Which Formula? Precision of Estimates. Bounding Type I and Type II Errors. Equivalence. Software. Subsamples. Loss Adjustment. Number of Treatment Sites. Alternate Designs. Taking Cost into Consideration. For Further Information. 7. Exception Handling. Patient Related. Missed Doses. Missed Appointments. Noncompliance. Adverse Reactions. Reporting Adverse Events. When Do You Crack the Code? Investigator Related. Lagging Recruitment. Protocol Deviations. Site-Specific Problems. Closure. Intent to Treat. Is Your Planning Complete? PART II DO. 8. Documentation. Guidelines. Common Technical Document. Reporting Adverse Events. Initial Submission to the Regulatory Agency. Sponsor Data. Justifying the Study. Objectives. Patient Selection. Treatment Plan. Outcome Measures and Evaluation. Procedures. Clinical Follow-Up. Adverse Events. Data Management, Monitoring, Quality Control. Statistical Analysis. Investigator Responsibilities. Ethical and Regulatory Considerations. Study Committees. Appendixes. Sample Informed Consent Form. Procedures Manuals. Physician's Procedures Manual. Laboratory Guidelines. Interim Reports. Enrollment Report. Data in Hand. Adverse Event Report. Annotated Abstract. Final Reports(s). Regulatory Agency Submissions. e-Subs. Journal Articles. For Further Information. 9. Recruiting and Retaining Patients and Physicians. Selecting Your Clinical Sites. Recruiting Physicians. Teaching Hospitals. Clinical Resource Centers. Look to Motivations. Physician Retention. Get the Trials in Motion. Patient Recruitment. Factors in Recruitment. Importance of Planning. Ethical Considerations. Mass Recruiting. Patient Retention. Ongoing Efforts. Run-In Period. Budgets and Expenditures. For Further Information. 10. Computer-Assisted Data Entry. Pre-Data Screen Development Checklist. Develop the Data Entry Software. Avoid Predefined Groupings in Responses. Screen Development. Radio Button. Pull-Down Menus. Type and Verify. When the Entries Are Completed. Audit Trail. Electronic Data Capture. Data Storage: CDISC Guidelines. Testing. Formal Testing. Stress Testing. Training. Reminder. Support. Budgets and Expenditures. For Further Information. 11. Data Management. Options. Flat Files. Hierarchical Databases. Network Database Model. Relational Database Model. Which Database Model? Object-Oriented Databases. Clients and Servers. One Size Does Not Fit All. Combining Multiple Databases. A Recipe for Disaster. Transferring Data. Quality Assurance and Security. Maintaining Patient Confidentiality. Access to Files. Maintaining an Audit Trail. Security. For Further Information. 12. Are You Ready? Pharmaceuticals/Devices. Software. Hardware. Documentation. Investigators. External Laboratories. Review Committees. Patients. Regulatory Agency. Test Phase. 13. Monitoring the Trials. Roles of the Monitors. Before the Trials Begin. Kick-Off Meetings. Duties During Trial. Site Visits. Between Visits. Other Duties. Maintaining Physician Interest in Lengthy Trials. 14. Managing the Trials. Recruitment. Supplies. Late and Incomplete Forms. Dropouts and Withdrawals. Protocol Violations. Adverse Events. Quality Control. Visualize the Data. Roles of the Committees. Termination and Extension. Extending the Trials. Budgets and Expenditures. For Further Information. 15. Data Analysis. Report Coverage. Understanding Data. Categories. Metric Data. Statistical Analysis. Categorical Data. Ordinal Data. Metric Data. An Example. Time-to-Event Data. Step By Step. The Study Population. Reporting Primary End Points. Exceptions. Adverse Events. Analytical Alternatives. When Statisticians Can't Agree. Testing for Equivalence. Simpson's Paradox. Estimating Precision. Bad Statistics. Using the Wrong Method. Deming Regression. Choosing the Most Favorable Statistic. Making Repeated Tests on the Same Data. Ad Hoc, Post Hoc Hypotheses. Interpretation. Documentation. For Further Information. A Practical Guide To Statistical Terminology. PART III CHECK. 16. Check. Closure. Patient Care. Data. Spreading the News. Postmarket Surveillance. Budget. Controlling Expenditures. Process Review Committee. Trial Review Committee. Investigatory Drug or Device. Interactions. Adverse Events. Collateral Studies. Future Studies. For Further Information. Appendix Software. Choices. All In One. Almost All In One. Project Management. Data Entry. Handheld Devices. Touch Screen. Speech Recognition. e-CRFs. Do It Yourself. Data Collection Via the Web. Preparing the Common Technical Document. Data Management. Data Entry and Data Management. Small-Scale Clinical Studies. Clinical Database Managers. Data Analysis. Utilities. Sample Size Determination. Screen Capture. Data Conversion. Author Index. Subject Index.

Validation of undergraduate clinical data by electronic capture (barcode).

Abstract: Assessment of clinical activity is common in dental schools. An audit project to confirm that dental undergraduate clinical activity recorded by electronic data capture is an accurate representation of the clinical case entry is reported. A printout of clinical activity for a period of a week was generated retrospectively and used to identify case notes. Activity recorded in the case notes was compared with the computer printout. All discrepancies were noted. A total of 125 patient files with 270 barcoded items of treatment were retrieved; 29 of 78 (37.1%) paediatric and 23 of 47 (48.9%) orthodontic cases had discrepancies between the case notes and the computer entry. However, some items recorded in the notes do not require barcoding and vice versa. When these were accounted for, only 19 items of treatment appeared in the notes that should have been barcoded, a 7% shortfall in recording of clinical activity. The barcode system is an accurate and reliable way of recording undergraduate clinical activity.

Controlling the Increased Complexity of Electronic Clinical Data Collection and Management Technology by Data Modeling and System Engineering

Abstract: Economic realities are forcing pharmaceutical research and development to become more productive. A lot of hope is put on new information technologies, including electronic data capture. However, adding new technologies to existing legacy systems bears the risk of creating informational chaos. This can be avoided by system engineering and data modeling, which focus on the technology independent essence of a system. Computer-aided system engineering tools translate logical models into real databases. As the aeronautical industry proves, rigorous computer-assisted system engineering and modeling is the way to manage highly complex systems successfully. The pharmaceutical industry has yet to make serious contributions to the planning and designing of complex systems.

Electronic data capture and verification for electronic forms

Abstract: Apparatus for automatically building an electronic form for presentation to a user during a data capture process segregates the data capture intent behind the form from the presentation and execution of the form to a data capture user. In this way, the data capture process, including generation of the form and display of user input prompts, can be carried out on any computing platform independent of the system used to generate a data capture definition file that specifies the intent of the data capture requirements. The specification of data elements required during data capture, each having a type specification and a logical relationship relative to other data elements in a hierarchical structure are defined in a data capture definition file in a predetermined format. A data capture process executes the data capture definition file and automatically generates a plurality of visual displays for presentation to a user, each input screen comprising a plurality of user input areas corresponding to the data elements and physically positioned on the screen in a manner corresponding to the defined logical hierarchical structure.

Clinical bioinformatics database driven pharmaceutical system

Abstract: Computer-based technologies and methods of human clinical data capture and analysis for identifying and recruiting patients for pharmaceutical and diagnostic product testing. These methods include acquiring product data and clinical data and comparing product data to clinical data in real time in order to identify suitable patients for product testing. Methods also provide for the generation of an alert message identifying suitable patients, preferably through the use of artificial intelligence or neural network techniques. Methods also preferably include the use of wireless devices to collect the patient data with a graphical user interface suitable of displaying the alert message and receiving additional questions for use in querying the patients for collection of data, the encryption of clinical data during transmission and storage, and conversion of clinical data to a format consistent with data mining techniques.

Capturing and reporting electronic data

Abstract: This volume reviews current data collection systems, examines unique approaches to data collection and storage, and provides the latest information on regulatory issues on data capture, storage, and reporting.

Data and Data Management in Clinical Trials

Abstract: Data management is one of the most critical areas in any clinical research protocol. They are the most important product of clinical research; and the ability to store, manipulate, analyze, and retrieve data is critical to the research process. The best data management tools are a well-written protocol, which includes data collection forms, standard terminologies, carefully written definitions, and a manual of standard operating procedures. Designing and conducting clinical research protocols involve the planning and the making of intelligent, informed decisions from the start. In conclusion, there is no proper way to conduct a clinical research study, as it takes the imagination, the creativity, the initiative, and the diligence of the of entire research team to complete the project. However, there are appropriate approaches that incorporate the regulatory requirements with effective and efficient data management designs.

Standardizing Laboratory Data Interchange in Clinical Trials

Abstract: The Clinical Data Interchange Standards Consortium has developed a Laboratory Model for laboratory data that is generated during the conduct of clinical trials. The Laboratory Model is the first step in proposing standards for the interchange of clinical trial laboratory data. Standards will decrease the time and resources required by stakeholders in the pharmaceutical development process (pharmaceutical companies, biotechnology companies, contract research organizations and laboratories). Standardization will therefore contain costs as well as improve data quality.