Challenges Facing the Programmer in Observational Research Laurence - - PowerPoint PPT Presentation

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Aug 10, 2023 285 likes •492 views

Challenges Facing the Programmer in Observational Research Laurence Carpenter, Amgen PhUSE, October 12 th 2011 Agenda Background Introduction Types of Observational Research (OR) Study Data Format and Collection The

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Challenges Facing the Programmer in Observational Research

Laurence Carpenter, Amgen PhUSE, October 12th 2011

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Agenda

Background
Introduction
Types of Observational Research (OR) Study
Data Format and Collection
The Changing Environment
Compliance to Standards
Conclusion

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Background

Developing new drugs is getting harder
OR becoming increasingly important
Evidence regarding

Ø Disease Ø Costs Ø Utilization patterns Ø Real world data (safety and effectiveness)

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Background (cont ..)

Demand from Regulatory Agencies

Ø Pharmacovigilance Ø Risk/Benefit

Demand from Reimbursement Authorities

Ø Health Technology Assessments Ø Comparative effectiveness

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Introduction

How are Statistical Programmers affected?

Ø Familiarity with phases I-IV Ø Different study designs Ø Challenges

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Types of Observational Study

Many Types
Reasons for Choice

Ø Data required Ø Practical aspects of data collection Ø Timelines

Schedule of assessments vs Routine clinical care
Prospective vs Retrospective

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Prospective Studies

Registries (Longitudinal Cohort Studies)

Enrolment End of Study

Study Visits & CRF completion

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Prospective Studies (cont ..)

Prospective Chart Reviews

Enrolment End of Study

Standard Clinical Care and Chart Abstraction

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Retrospective Studies

Retrospective Chart Reviews
Retrospective Database Analysis

Standard Clinical Care Chart Abstraction

Enrolment

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Data Format and Collection

(e)CRF data
Adjudication data
PRO data
Spreadsheet data
Adding value

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Data Quality

Completeness of data
Cleanliness of data
Rate of incoming data
Database analysis

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Coding Techniques

Efficient coding

Ø Generally desirable Ø Not usually critical in clinical trials Ø May become more important in OR

Techniques (in SAS) include

Ø Avoiding PROC SORT’s where possible Ø Using ‘WHERE’ instead of ‘IF’ Ø Creating Indexes Ø Testing programs using a subset of data

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The Changing Environment

Goals and Objectives
Evolution of ideas during study
Pre-specification / ad-hoc work
Programming team may need to work differently

Ø Planning vs Flexibility Ø Timelines and resourcing Ø Risks to quality

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Use of Output

Clinical Research

Ø Derived Datasets/TFLs usually final deliverables Ø Clinical Study Report, Submission

Observational Research

Ø Derived Datasets/TFLs may be part of a process Ø Costings Ø Economic Modelling Ø Publications / Posters

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Functional Interactions

Programmer

Statisticians Data Managers Clinical Medical Study Managers

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Functional Interactions (cont ..)