Meta-analysis - Some Considerations

Some considerations when planning the statistical approach to an IPD meta-analysis of time-to-event data

We recently undertook a meta-analysis of time-to-adverse-event data across 12 randomized controlled trials in chronic obstructive pulmonary disease and share a few lessons learned when planning and implementing this type of analyses. Meta-analysis of individual patient data (IPD) offers a number of advantages over the traditional meta-analysis of aggregate data - in particular the ability to reliably estimate covariate and interaction effects. IPD can be modelled in a ‘one-stage" analysis (fitting a single model to all trials simultaneously), or the trials can be modelled separately and summary statistics analysed using standard meta-analytic techniques, known as a ‘two-stage" approach. One-stage models are appealing when IPD are available, but two-stage approaches should not be discounted as they are simpler to implement (particularly if assuming random effects) and any loss of power appears to be small (Stewart et al. 2012, Fisher et al. 2011).

In our analysis we aimed to establish three things:

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Specialist providers praised at PCT

Specialist providers praised at PCT

PHASTAR recently attended the annual Partnerships in Clinical Trials conference in Vienna. This conference brings together the clinical trial community including pharma, biotech and medical device professionals, CROs, site investigators and patients, providing a platform for open discussion, debate and knowledge exchange. The key themes for this year's conference were patient centricity, disruptive innovation and collaboration.

One of the recurring discussion points throughout the conference was the merits of full-service versus functional outsourcing. Pharma companies have been outsourcing for over 25 years but there still remains a level of dissatisfaction among sponsors. Many of the roundtable discussions debated the reasons behind this dissatisfaction and highlighted both good and bad experiences. A common emerging theme was the relative success of functional outsourcing where a specialist provider is more open to adopting innovative approaches or have expertise/capacity not available in the sponsor organisation. This is in addition to the growing need for a flexible resourcing strategy which adapts to changes in company portfolios and priorities.

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PHASTAR push for CDASH standards

PHASTAR push for CDASH standards

Most sponsor companies have recognised the need for, and have long been working to, data collection and processing standards. Great. But when you think that these standards are typically specific to a company, a therapeutic area or the project team conducting the study, then the amount of "standards" in play is quite considerable. Now that the FDA is requesting the submission of clinical trial data in common CDISC format, in order to ease the burden of their review process, the various formats employed and held on to so closely and for so long are no longer viable.

The current trend is to convert resulting datasets into the required SDTM format, either during the conduct of an ongoing study, or even once the study has ended for older legacy studies. As a CRO, we receive data from multiple clients, in multiple therapeutic areas and all can attest that the level of programming effort required for this task varies according to the format in which the data are obtained. In instances where data needs to be pooled and the data from each of the studies has been provided in different formats, this effort needs to be duplicated as the programs will need to be tailored to each of the formats. This is obviously very time consuming.

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Pooling Clinical Data - a medical writer's perspective

Pooling Clinical Data - a medical writer's perspective

Pooling of data is used by all companies involved in submitting clinical trial data. There are various reasons to pool data including the production of documents for Development Safety Update Reports (DSURs), publications and parts of an NDA submission.

One of the hurdles a project team (including the medical writer) face is the decision on how to best pool or not pool clinical trial data for submission to regulatory authorities. The first approach to pooling clinical data is planning. A plan should be developed to identify which studies and which data should be pooled for any given deliverable from both an efficacy and a safety perspective.1,2

The role of a medical writer is to tie the pooled datasets and analyses together into the final submission document. Medical writers are required to work closely with biostatisticians to understand what is expected of regulatory authorities.

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PhUSE 2016

Summary from PhUSE 2016, Barcelona

Barcelona, the beautiful Catalonian city on the Mediterranean coast of Spain hosted the annual PhUSE conference for 2016. With nearly 600 attendees, 44 exhibitors, 15 streams including more than 120 papers, six Hands-on Workshops and Discussion Clubs, this was a great opportunity for the data scientists in Pharma, CRO and related industries to come together and share latest development within the whole lifecycle of clinical data analyses and reporting. Phastar has proudly presented 2 posters and a paper in the data visualization stream.

The theme of this year" conference was "Fast Track to Approval: Speed and Efficiency". Many of the workshops and presentations discussed how we as data scientists can standardize, automate and become more efficient in order to reduce the time to approval for our drugs. This year two new streams were introduced "Real World Evidence" and "Data Visualization" both were well attended and received positive feedback from the audience.

There were presentations from three different regulatory authorities in the regulatory stream. Those were informative and a good experience to see things from their point of view.

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