Problem Definition: -Sickle Cell disease specific drug trials and pooled data analysis.
Challenges: As per the nature of sickle cell disease, the patients needed treatment when there is an acute pain to the patients. This resulted in a complex dosing algorithm.
Solution: Our biostatistics and statistical reporting team analyzed the pain instances closely and ensured the correct dosing algorithm applied for these clinical trials. This along with appropriate interpretation and usage and curation of clinical terms, as well as representation and analysis of data resulted into successful submission of these trials to regulatory body.
Outcome: Successful submission of trials and pooled data to regulatory bodies.
Problem Definition: Determination of microbial endpoint.
Challenges: The study had several complexities with microbiological data. There were issues with data collection, tricky baseline and endpoint definitions, and several instances of completely missing information due to the nature of collection which required invasive procedures.
Solution: Complete and in-depth understanding of data collection, collection challenges and work-arounds, and study-specific microbiology along with strong programming skills were crucial for this. The biostatistics and statistical programming team worked very closely with study microbiologist to go over the data (and specific cases in several instances), understanding microbiology and genetic information relevant to study, determination of robust baseline definition and, handling of missing information including leveraging other indirect endpoints to facilitate endpoint derivation.
Outcome: Successful determination of microbiological endpoints. Better understanding of issues and challenges associated with microbiology data along with more robust collection requirements.
Problem Definition: Lupus disease regulatory submission.
Challenges: Lot of missing data. Difficult efficacy analysis resulted in multiple regulatory queries. Complex up-versioning of dictionary terms.
Solution: In-depth knowledge of clinical data and domain was extremely important in this trial. Our team worked extensively in understanding and knowing the data properties, learnt about the disease thoroughly and focused on data processing as per business requirements.
Outcome: Successful regulatory submission along with additional related deliverables such as drug safety report and risk management plan specific reporting.
Problem Definition: Sample size and RP2D determination for first-in-human study for oncology drug.
Challenges: limited population due to the nature of disease and phase of study (Phase I/IIa).
Solution: Due to the nature of study and current available treatments, for the new drug to be considered for further investigation, a preliminary minimum efficacy was deemed as necessary along with safety requirement. To account for efficacy and safety, Bayesian Optimal Interval Phase I/II (BOIN12) model was selected for determination of RP2D and sample size for the phase 1 part. BOIN12 model was selected for the following reasons:
- It accounts for safety and efficacy when determining RP2D.
- BOIN12 allows for selection of toxicity rate. It is not limited to 33% as is the case with the 3+3 design.
- It has been shown to be more effective than the standard 3+3 design model which is most commonly used in Phase I dose escalation studies.
- Based on safety and efficacy, the model allows for escalation and de-escalation of dose.
Outcome:The sample size was gleaned from the operating characteristics for proposed toxicity-efficacy relation models. For the phase -IIa part of the study, the sample size was determined using the Simon’s 2-stage model as that allows for early rejection of drug in case of poor efficacy.