Key Strategies for Overcoming Data Analysis Difficulties During NDA/MAA Submissions

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August 14, 2018

By Amanda Truesdale, MA, MBA, VP Biometrics

When database locks are delayed, or regulatory agencies request additional analyses for new drug applications (NDAs) or marketing authorization applications (MAAs), it seems there is no alternative but to take a hit to the timeline — or is there?

At Veristat, we believe that careful timeline configuration and strategic data analysis planning can optimize timelines without sacrificing quality. To achieve this, we follow the practical tips below:

Construct a strategic timeline

  • Coordinate planning across cross-functional teams as well as within functions to ensure consistency, especially when data analysis for one or more clinical study reports (CSRs) are required at the same time as an integrated summary of safety (ISS) or integrated summary of efficacy (ISE).
  • Prioritize any statistical analysis plans (SAPs), which dictate the analysis requirements, including the approach to data pooling. By working backward from the submission deadlines, and having a clear understanding of the analysis requirements, the timeline can be optimized to ensure the appropriate studies and types of data (domains) are prioritized to facilitate subsequent activities.
  • Provide final draft SAP(s) to relevant regulatory agencies for early input to avoid late-breaking changes that may require additional time adjustments.
  • By understanding analysis requirements via a well-vetted SAP, the roadmap to data migration (SDTM), as well as subsequent ADaM and TLF production can be optimized to allow medical writing activities to begin before biostatistics activities are completed. Well established timelines, technical processes and careful communication are the keys to achieving a shortened timeline while preserving the quality of the submission.

Be proactive about risk mitigation

  • Involve key stakeholders in timeline planning, SAP planning and other key planning points to incorporate input a priori rather than after the project is underway.
  • Hold a risk mitigation meeting where the team brainstorms possible hurdles and solutions in advance. This helps ease the way forward when challenges and/or delays occur.
  • Ensure all biostatistics and programming teams understand the analyses involved for each CSR to better inform decision making for integrated analyses.

Start as early as possible

  • Hold a pre-SAP meeting early in the process to bring biostatistics, regulatory, clinical/medical, and medical writing teams to the table. This helps identify key messages, critical data analysis requirements, data pooling requirements and other key decisions. This meeting aligns the submission team with a clear and unified strategy.
  • Perform multiple dry runs with the last one close to the final data cut. This can help identify concerns prior to the official final data release, allowing time for resolution before challenges arise that would impact the timelines at critical milestones.
  • As batches of analyses are completed, biostatistics teams can provide the outputs to medical writing for incorporation into written modules on a rolling basis. Be sure to track which versions are utilized in the written drafts and replace or update as necessary via well documented procedures and frequent real time communication.

Incorporate quality checks

  • Establish independent quality control via electronic comparisons. By automating these processes, the timeline post final data receipt can be minimized.
  • Statistical review checks are essential to achieve the highest quality. These checks are typically higher-level global reviews across ADaMs and TLFs that go above and beyond the electronic programmatic quality control on each individual output.  Define and execute these checks before all data are final.  This makes the final review faster given the technical processes and steps will already be in place, while also limiting the findings to new data received since the final lock.
  • Use rigorous quality monitoring processes to ensure all data are current and accurate. When outputs are passed on a rolling basis to medical writers, tools such as Word Compare can locate differences in data tables that need to be updated, thereby automating the identification of changes and minimizing the risk for errors.

There is, of course, no fail-proof plan for coping with all the possible challenges data analytics can face during complex or simultaneous NDA/MAA submission processes. But with careful planning, proactive approaches to risk mitigation and data analysis, and an absolute prioritization of quality, it is our belief at Veristat that data analysis needn’t be a bottleneck for any submission project. Instead, our approach to overcoming the many inherent challenges involved in complex submissions has enabled the data analysis team to create efficiencies, surpass deadlines and accommodate even drastic shifts in database locks to help ensure timely submissions.

 


 

Learn More:

Read more about how to manage operationally complex submissions — as well as tips for data analysis and medical writing in these complicated projects — in our Insight Brief on the topic: “Successful Preparation Strategies for NDA/MAA Marketing Applications.”

You can also read our advice for medical writing management during simultaneous submissions and navigating operationally complex submissions in our other blog posts.

 


 

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