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Clinical Programming Team

Clinical Programming Team

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Why do a 3rd of Submissions fail the Technical Rejection Criteria?

Clinical Programming Team

At the July 25th PhUSE Webinar Wednesday, Ethan Chen (Director, Division of Data Management Services and Solutions Office of Business Informatics, FDA CDER) gave us a glimpse of the FDA’s view on Technical Rejection Criteria for Study Data. Those that were at the US Connect Conference earlier in the year may have seen this already.

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Topics: Define.xml, ADaM Datasets, CDISC Standards, SDTM, SAS Programming, FDA, CDISC, SDTM Domains, CDISC SDTM, CDISC Guidelines

First Steps in Laboratory Dataset in SAS

Clinical Programming Team

The laboratory dataset is one of the core safety datasets and, at first glance, it could appear intimidating, with multiple tests and visits per patient. This article will illustrate checks that are worth applying at the very beginning of programming work – these could be in addition to the standardized process of domain validation.

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Topics: Laboratory Datasets, Safety Dataset, Statistical Programming, SAS Programming, SAS, Clinical Programming, CDISC Standards, CDISC SDTM, SAS Macros, SAS Datasets, SDTM, Case Report Form (CRF), SDTM Domains, CDISC Guidelines, CDISC

The Problems (and solutions) of Real World Data in the Pharma Industry

Clinical Programming Team

Our job as Real World Data programmers is to put the whole patient data, usually spread across multiple tables, into one coherent history. This might lead to some surprising discoveries such as: patients with records before birth/after death, patients changing sexes multiple times, and finding non-valid codes. Such discrepancies are usually pretty easy to classify as data issues and are easy to handle. On the other hand we might encounter real world data events that cannot be as easily classified and need special approaches such as; patient receiving multiple Rx’s for the same drug on one day, or receiving a new Rx’s before the previous runs out, patients visiting the Doctor’s office / Emergency Room during their hospital stays, patients changing wards/level of care multiple times during one hospital visit, and finding valid but non-billable codes in insurance claims data. In this post we share some commonly encountered problems (and our solutions) related to Real World Data analysis.

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Topics: Real World Data, Clinical Programming, Observational longitudinal databases, Outcomes Research

The Logic of a Clinical Research Organization Programmer

Clinical Programming Team

As a statistical programmer at a leading data focused Clinical Research Organization (CRO), we are requested to become involved with many programming activities on a daily basis, centred around dataset or display generation and quality control (QC). Having the opportunity to develop a process/system which can be used by others is rare. To build any system, a lot of in depth thought is required before any programming begins. We take the requirements and build a robust system to address each potential scenario that may arise, including some which should not.

Within this blog we will explain how statistical programmers can work through a simple request to build a robust system using logic, SAS, UNIX and experience. It will provide a flavour of how robust systems are built and other considerations. 

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Topics: Clinical Programming, SAS, SAS Programming, Quality Control, Quality Process Improvement, SAS Macros

The INTO Statement in PROC SQL to Create Macro Variables

Clinical Programming Team

A member of the Quanticate Programming team writes about their opinions of the INTO statement in PROC SQL.

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Topics: Clinical Programming, SAS Programming, Large Datasets, PROC SQL, SAS Macros

A Review of the Annual PhUSE 2016 Conference

Clinical Programming Team

A number of team members were able to represent Quanticate at the PhUSE 2016 annual conference in Barcelona. The PhUSE annual conference is an opportunity for programmers and statisticians to both learn from and share cutting edge knowledge with the pharmaceutical industry. This year, Quanticate presented on producing high quality SAS graphics using the advanced Graphical Template Language (GTL) to bring individual plots together to aid analysis without sacrificing any aesthetical properties in the process. Conference attendees were spoilt for choice with approximately 5 simultaneous presentations every half-hour across 15 streams in total. Here are some interesting presentations which the team enjoyed over the conference duration.

 

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Topics: Visualization, Clinical Programming, SAS Graph, Conferences, SAS Programming, Programming R

Exploring CDISC Analysis Data Model (ADaM) Datasets

Clinical Programming Team

Clinical Data Interchange Standards Consortium (CDISC) defines and manages industry level data standards that are widely used during the analysis, reporting and submission of clinical data. For instance, the Study Data Tabulation Model (SDTM) is the submission data standard into which raw study data are mapped and collated. ADaM is a companion standard for use with analysis data and it is best practice to use SDTM data as the source for these datasets. Doing this allows for the easy documentation of any data processing with Define-XML, the CDISC standard for data definition files.

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Topics: Traceability, CDISC SDTM, CDISC, Standardization, Adverse Events (AEs), Clinical Trial Documentation, Clinical Programming, SDTM, CDISC Standards, ADaM Datasets, Analysis Data Model, ADSL

Outcomes Research Programming vs Traditional Clinical Trial Programming

Clinical Programming Team

Outcomes research aka health outcomes research, is the study of the end results of particular health care practices and interventions, in other words it is the study of what happens in the real world to patients when they are given a certain treatment or a certain method of care. Outcomes research studies are used to improve the quality and value of healthcare for patients.

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Topics: Statistical Programming, Clinical Study Design, Clinical Programming, Accessible Data, Big Data, Outcomes Research, Real World Data, Teradata SQL, Observation Longitudinal Database

Creating Custom or Non-Standard CDISC SDTM Domains

Clinical Programming Team

Within the Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM), standard domains are split into four main types: special purpose, relationships, trial design and general observation classes. General observation classes cover the majority of observations collected during a study and can be divided among three general classes:

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Topics: Statistical Programming, CDISC Guidelines, CDISC SDTM, SDTM Domains, CDISC, Standardization, Clinical Programming

Your SAS Secrets Exposed! [4 SAS Tips]

Clinical Programming Team

SAS_Tips

During my time in the life science industry I have learnt a lot of SAS techniques through attending training sessions, however some of the best SAS tips I have picked up were from other programmers: for instance when asking for advice on a coding problem or running programs written by colleagues. I found there are many simple SAS® tips you can use in your day to day SAS programming. This blog will provide explanations and examples of four of these.

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Topics: Clinical Programming, SAS Programming, SAS Datasets, SAS Macros, SAS

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