Data Management Technologies and Directives

Data Management Technologies and Directives

These are exciting times in Data Management. In recent years new technologies and directives have induced a subtle change in the role of a Data Manager. The raison d’etre for Data Managers has always been the delivery of high quality data thereby ensuring the validity and integrity of the statistical results. With the advent of CDISC standards, EDC and ePRO and sponsors adopting a Risk- Based Monitoring approach, the role of the Data Manger as an interface with clinical and statistical programming teams has never been more crucial or diverse.

Data collection in accordance with CDASH initiatives means we have the tools to streamline our processes by standardising the definitions for the data that is collected over multiple studies facilitating easier transformation to SDTM for submissions.  Working with the programming team on the challenges of implementing CDASH (it's by no means a one-size fits all solution) brings an understanding across roles) what's good for SDTM does not always fit the bill for good eCRF design and optimal data collection - or abide by the flow of the Protocol. In these situations, additional mapping may be needed to create the final SDTM compliant datasets.  This essential understanding of the starting point to endpoint has promoted lively discussions and ensured all needs are met in the most efficient way.

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SAS Art Competition 2016

SAS Art Competition 2016 - Open to all

Did you know that you can use SAS to create interesting graphics and even conceptual art? We would like to invite all SAS users to take part in a competition to create interesting art and graphics. The winning entry will receive a £500 shopping token (which can be donated to your chosen charity, if preferred). The entries should be sent to This email address is being protected from spambots. You need JavaScript enabled to view it. by 5pm UK time on Friday 25th November 2016. We would ideally like the artwork to be printable in large format (A0 or 1 metre square approximately), although the file can be sent as either a bitmap or JPEG file in smaller format. Please include the SAS code and graphic in your email, along with any other explanation required to understand and/or interpret the artwork. All entries should incorporate at least one of the colours in the PHASTAR logo (the darker colour is cx73002E and the lighter colour is CXAF95A6).

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Statistical issues in oncology trials

Statistical issues in oncology trials

A key issue in assessing the efficacy of new drugs in oncology is getting the balance right between choosing a hard endpoint survival - and an endpoint that allows evidence to be assessed more quickly progression. Often discussions such as these are required after interactions with regulatory agencies. Progression free survival is used in many studies to assess whether drugs are providing benefit, but it comes with difficulties in analysis and interpretation. In any survival analysis (what we call any "time to-event" analysis), the statistician needs to decide on censoring – patients who "run out" of data. One of the fundamental assumptions in survival analysis is that the censoring is unrelated to the survival times. If a treatment works then you'd hope for longer survival times in one treatment group. If more patients, or more of a certain type of patients, on one of the treatment groups are censored, then this assumption will not hold.


One of the main points of contention relates to handling patients who stop randomised therapy and start a new therapy while on the study. Should these patients be censored or not? There is a good chance that patients who are changing therapies are not doing well on their current therapy, either for safety or efficacy reasons. If the patient is censored, then the negative information about the treatment is ignored, and it's very likely that a bias will be introduced in the analysis, with resultant incorrect estimates of treatment effect. Recently, in a PhRMA sponsored study, it was found that anything other than a strict intent to treat analysis introduces bias. To avoid bias, patients should be followed up until progression, irrespective of whether they remain on the treatment being studied.

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Programming issues in oncology trials

Programming issues in oncology trials

Oncology trials are complex and require a different approach to trials compared with many other therapeutic areas and can generate significant programming challenges.

Oncology can be associated with fast developing disease and short survival times. Due to the fact that many oncology trials are event-driven, the timelines and resources are regularly reviewed and updated. In earlier phase oncology trials, there are frequent data reviews to assess for safety and/or efficacy, which can present challenges in the planning and delivery of programming tasks. Standards programs are often developed so that they can be used across different studies, and re-used across multiple deliveries. This helps reducing the programming time on each study and therefore meeting the tight timelines specific to the oncology therapeutic area. Close collaboration between programmers, data managers and clinicians is required to ensure data issues are promptly resolved.

Clinical oncology trials are also more complex than those in other therapeutic area. The design is often more complicated, with additional data being collected such as quality of life questionnaires, genetic and biomarkers data. Analyses performed are often more specific to this therapeutic area. Therefore, the datasets can be quite complex and below are some examples of the biggest programming challenges.

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CRM in Phase 1 Oncology trials

Use of the Continual Reassessment Method (CRM) in phase 1 oncology trials

By Gareth James, Senior Statistician, PHASTAR

The purpose of a phase I clinical trial is to determine the recommended dose for further testing whilst minimising the number of patients used and preserving safety. Oncology trials are different to those for other indications as patients have metastatic disease and have exhausted other treatment options. It is therefore important to minimise allocation to toxic and sub-optimal doses.

The 3+3 method is the most common dose-escalation design, with over 96% of phase I studies using this method, but it is not statistically efficient as it does not use all available data to recommend the next dose level to allocate. Approximately 100 publications have demonstrated the advantages of using model based methods such as the CRM compared to the 3+3 method. This included reducing the number of patients allocated to toxic and sub-optimal doses, and identifying the true maximum tolerated dose (MTD) more frequently, which would reduce the likelihood of making a costly and potentially unsafe decision.

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