In the randomized clinical trial (RCT), the process of deciding the randomization method and implementing is critically important. Unfortunately, it is not unheard of for problems to arise. In an article (Downs et al 2010 1), it is noted that as well as initial errors of trial design, problems can arise from errors with programming of the randomization or even human error during the course of the trial. Maintaining the rigor of the RCT relies on robust and reliable randomization with no errors. If treatment allocation is inadequately concealed then overestimation of treatment effect can occur, and the ‘randomized’ control trial becomes effectively ‘non-randomized’ – putting the entire study at risk (2).
Mar 20, 2017 9:54:00 AM
Mar 10, 2017 7:19:33 AM
At the recent Biosimilars Summit in Philadelphia, Cytel's Pantelis Vlachos presented on statistical challenges and flexible approaches in biosimilar development. In this blog we summarize some of the challenges and share the slides from talk.
Mar 2, 2017 8:45:00 AM
We continue our case study series with this example of a Phase 3 design that uses Bayesian decision making combined with frequentist final analysis.
Clinical Development Background
Our biopharmaceutical client’s lead drug candidate is a late clinical-stage cancer immunotherapy for treatment of a rare oncology indication. Clinical development of therapies in this indication faces inherent challenges of patient recruitment and scarcity of data.
The sponsor had previously conducted a randomized, double-blind, placebo-controlled Phase 2 study. Moving into a confirmatory clinical trial setting, they came to Cytel for support with a trial design to address their key questions:
Feb 27, 2017 7:39:00 AM
It’s been hard to miss the prevalence of estimand-related discussions in the last year. This is a topic which is very much at the forefront of statistics discussions right now. We are lucky enough to welcome Mouna Akacha to the blog to give us the lowdown on estimands and the problems and opportunities they represent for the global biopharma industry.
Mouna is a Consultant in the Statistical Methodology Group of Novartis Pharma AG, based in Basel, Switzerland. She has a wide range of research interests including topics on missing data, longitudinal data and recurrent event data and is an active participant in the current estimand discussions.
Read on to find out everything you ever wanted to know about estimands but were afraid to ask…..
Feb 21, 2017 9:39:07 AM
In a previous blog, we provided an overview of basic data structures in R. In this follow up piece, we will provide a snapshot of basic syntax in R for programmers who want to get up to speed in this increasingly important programming language.
Feb 15, 2017 9:33:05 AM
Outsourcing solutions should never be a one size fits all process, and smaller and emerging biopharma companies may have different priorities and processes when working with external vendors to larger pharmaceutical organizations.
Feb 9, 2017 7:34:58 AM
In this blog, Adam Hamm, PhD, Director Biostatistics at Cytel shares some of the most important knowledge he uses in his day to day work as a biostatistician working extensively in oncology research. Adam has broad experience with statistical analysis and methodology over all phases (I-IV) of development, in particular working in the oncology arena.
As a Director of Biostatistics at Cytel, I work on design, statistical analysis and reporting projects for a range of biotechnology and pharmaceutical sponsors. During my career, I’ve developed a particular focus on oncology trials, so in this blog I’ll share some insights into the knowledge which I have found particularly vital as a biostatistician working in this area. This knowledge spans specific statistical methodologies and understanding of the clinical issues across the phases of clinical development. The summary is not exhaustive, but provides a glimpse into the broad exposure which is needed for a biostatistician to develop a fully rounded understanding in the area. To learn more, read on...
Jan 30, 2017 9:50:24 AM
Single ascending dose (SAD) and multiple ascending dose (MAD) studies are typically the first in human studies. They seek to gain information on safety and tolerability, general pharmacokinetic (PK) and pharmacodynamic ( PD) characteristics, and of course identify the maximum tolerated dose (MTD).
Conventionally, SAD and MAD studies were conducted separately, but increasingly are combined into an ‘umbrella’ protocol which addresses both SAD and MAD objectives.
Jan 23, 2017 10:35:00 AM
As a group, Cytel had over 40 successful regulatory interactions last year, many of which supported approvals for innovative trial design approaches. In this blog we look at some of the key success factors for regulatory interactions regarding adaptive designs.
Jan 19, 2017 7:05:00 AM
R is on the rise in biopharma, and as we have previously discussed on the blog, it is now time for SAS programmers to get up to speedwith this popular and powerful programming language. Indeed, one of the advantages of R is its ability to integrate with other languages like C, C++, Python and SAS. Its strong graphical capabilities allow output in PDF, JPG, PNG, and SVG formats and table outputs for LaTeX and HTML. Importantly, as an open source resource, there is a strong community around R and extensive support for users in the form of forums like R-bloggers, StackOverflow and GitHub Repository
In this blog, we’ll provide an overview of R basic structures for programmers.
Jan 16, 2017 8:41:00 AM
The Global Cardiovascular Clinical Trialists Forum is a key event bringing together leading experts from across the spectrum of opinion leaders, clinical trialists, investigators, regulators, statisticians and practitioners to address the most pressing questions in cardiovascular clinical development today. At the December conference, eminent biostatisticians Cyrus Mehta and Stuart Pocock led a packed workshop tackling the advantages and limitations of adaptive designs within this space.
Jan 5, 2017 8:45:00 AM
Nonlinear Mixed Effects Modeling (NONMEM) is a type of population pharmacokinetics/pharmacodynamics (popPK/PD) analysis used in Clinical Pharmacology research. The population PK approach combined with pharmacodynamics modeling, allows integrated analysis, interpretation, and prediction of the drug’s safety, efficacy, dose-concentration relationship, and dosing strategy.
Dec 15, 2016 11:53:14 AM
In April, we interviewed NIHR research fellow Munya Dimairo about the paper, ‘Adaptive designs undertaken in clinical research: a review of registered clinical trials’ (Hatfield et al, 2016), for which Munya was a co-author.
During the previous interview, we discussed the barriers to uptake of adaptive designs, and the urgent need for a cross-sector discussion and work on reporting guidance of adaptive design. As a follow up to this, Munya is now Lead Investigator of the ACE project which aims to develop a consensus-driven reporting guidance tailored for Adaptive designs in the form of a CONSORT extension. The ACE Project is funded by the NIHR and is led by a multidisciplinary Steering Working Group of international experts in collaboration with the CONSORT Executive Group and the MRC HTMR Adaptive Designs Working Group. The goal is to enhance transparency, credibility, reproducibility, and replicability of adaptive trials as well as facilitate uptake of ADs in clinical trials research when appropriate.
We are delighted to welcome Munya Dimairo back to the blog to give us the inside scoop on this project.
Dec 2, 2016 9:19:10 AM
At a recent Pfizer/ Cytel seminar on rare disease and oncology development, Cytel’s Lingyun Liu presented innovative work on a patient enrichment design. In this blog, we share some design and operational considerations. This approach can help mitigate against underpowering of a clinical trial where there is uncertainty and heterogeneity of treatment effect among subpopulations.
Nov 18, 2016 10:04:00 AM
Our client had the following key questions which they wanted our pharmacometrics group to address for an upcoming phase 2 trial of their ulcerative colitis compound .
1) Can knowledge from pre-clinical and Phase 1 data inform on the optimal range of doses for an upcoming Phase 2 dose-ranging study?
2) How may the dose response observed in PD markers in Phase 1 healthy volunteers translate to the patient population?
Nov 14, 2016 8:13:29 AM
In our last blog, we shared some of Angelo Tinazzi and Cedric Marchand's recommendations on how to ensure independence of QC in statistical programming. Now, we've put together an infographic highlighting some key do's and don'ts in a handy checklist.
Oct 5, 2016 9:48:00 AM
While some progress has been made in terms of scientific development in Neuroscience and Neuropsychiatry indications, the pace of translation into more effective treatments remains elusive.
At the recent Cytel seminar co-hosted with Pfizer, Abdul J. Sankoh of Sage Therapeutics presented on some of the challenges in these therapeutic areas and discusses strategies moving forward. He bases his presentation on his broad industry experience.
Sep 13, 2016 10:15:00 AM
Exposure-response data gained from clinical studies can provide a basis for model-based analysis and simulation, helping to predict the expected relationships between exposure and response. Using this approach, it may be possible to optimize dosage regimens and to individualize treatment in specific patient subsets for which there are limited data. In this blog, we examine a case study of an exposure response modeling project conducted by our Quantitative Pharmacology and Pharmacometrics team.
Sep 9, 2016 9:24:00 AM
Our client, an emerging biotechnology company, was preparing for the next stage of development for their novel compound in a rare disease. They had two major concerns which they wanted the clinical trial design to address- an anticipated difficulty in recruiting subjects to the trial, and the cost and time investment associated with running separate phase 2 and phase 3 trials. They approached Cytel’s strategic consulting team for an innovative solution.
An inferentially seamless Phase 2/ 3 design with promising zone was proposed as a means to address the sponsor’s objectives. Because of uncertainty regarding which dose would be selected and what the effect size of the selected dose would be, the team proposed design options which allowed for adjustment of the sample size using information learned at the interim analysis. Several seamless phase 2/3 designs, with and without adaptive sample size re-estimation were evaluated through simulations using East 6.4.
The simulations evaluated various design parameters such as maximal sample size, timing of the interim analysis, size of the promising zone, and efficacy and futility boundaries. Designs were compared on the basis of overall power, average sample size, conditional power, probability of entering each interim zone, and number of overruns.
The inferentially seamless design has the potential to accelerate clinical development by removing the ‘white space’ between phases 2 and 3. Where the sample size is increased adaptively at the interim analysis by a specified percentage of the original pre-planned sample size, an overall increase in power could also be achieved. The sample size re-estimation design provided a boost to power where the interim results fell in the promising zone. The client benefited from a design which only calls for additional investment of patients and resources when this investment would meaningfully boost the chances of success.
Cytel's statistical consulting team help you decide if an adaptive approach is right for your trial. Read further examples of our work by clicking below.