On 13 October 2016, managing directors and press officers of biotech, pharmaceutical and venture capital companies, as well as journalists, were meeting at the Faculty Club G2B of the IZB in Martinsried near Munich. There were three compact presentations held before a networking lunch:

• “Therapy advantage in cardiology through biotech” Dr. Stefan Kropff (Executive Medical Director, Amgen GmbH)
• “How to turn dust into stone with the help of bacteria”
  Martin Spitznagel (Managing Director, Dust Biosolutions)
• “New genomic methods for blood analysis:
  Liquid Biopsies” Dr. Rainer Schubbert (Head of Operations Applied Genomics,
  Eurofins Genomics)

For Staburo it was very exciting to see what is developed currently at the IZB in Munich, but also to learn about the successful history of several companies. As a specialised service provider for biostatistics solutions, it was also very interesting to meet current and potential clients in a relaxed atmosphere – we will definitely take part in more IZB events in the future!

The next Biotech Press Lounges will be on 16 February 2017, 11 May 2017 and 12 October 2017. Reservations can be made at marketing@izb-online.de.

We are very glad to welcome Vicky Stahl and Maximilian Siebold back in our team. Vicky and Max already worked as student interns at Staburo earlier and will support our clients in the area of statistical programming. We are very happy to have you back in the team!

The statistical principles of equivalence trials and non-inferiority, as well as the special case bioequivalence, were presented. From a statistical perspective, being non-inferior or bioequivalent means to be only irrelevantly inferior or different, respectively. Based on this idea, the null hypotheses for non-inferiority or bioequivalence are shown.

The test decision, based on confidence intervals and the duality to the test problem, were discussed. A simple example of how to determine the acceptance range in a clinical trial was introduced. Problems with the constancy assumption and advances in standard of care were shown based on examples.

Further thoughts on populations, sample size and regulatory aspects as well as practical implementation in the protocol or statistical analysis plan were depicted.

We are very happy to welcome Julia Breitenbruch in our team. Julia will support our clients with her experience in the areas of statistical programming and biostatistics. We are looking forward to a great cooperation!

Multiple Imputation (MI) is a technique to replace missing data with substituted values. The promise of this method is to estimate less biased estimates compared to state-of-the-art methods like Last Observation Carried Forward (LOCF).

In a MI approach, the same missing value is imputed multiple times in order to reduce noise in contrast to single imputation.

The introduced implementation is especially suited for the MI of longitudinal data. The approach utilizes the longitudinal information of the data to further minimize the prediction error of the imputation process.

Two step process for longitudinal data

The implementation is realized by a two step process. The first MI step serves as input for the second MI step. Step 1 uses an EM algorithm to impute the data until a monotone missing pattern is achieved. Monotone missing is achieved if the sequence of longitudinal information does not contain any “holes”. A hole is hereby a missing value between observed values, assuming a chronologic order in a longitudinal setting. This process is repeated m-times to get multiple imputations. The second step can utilize multiple prediction algorithms, e.g. regression, predictive mean matching or propensity score. However, the underlying process is always the same. Every remaining missing value is now predicted from the information that occurs earlier in the chronologically sequence.

After the imputation, the statistical analysis is done separately for every imputation dataset and the results are pooled according to Rubin’s Rules.

The talk discussed the practical implementation in SAS with proc MI.

In the past few years, more and more articles dealing with Bayesian statistics and Bayesian designs in clinical trials were published. Such designs are being developed especially for clinical trials with small number of patients, where it is crucial to get the most information from the limited available data.

The Bayesian Logistics Regression Model (BLRM), presented here in the context of oncology dose escalation trials, fulfills these requirements. This design allows to estimate the probabilities that pre-defined treatment doses are underdosing, target dosing, or overdosing, according to criteria such as the number of Dose Limiting Toxicities (DLT) observed. Using prior information (literature, earlier trials) as well as current data, in a logistic regression model, this design recommends the next dose to be tested until the Maximum Tolerated Dose (MTD) is achieved.

The talk includes a presentation of the model, guided with an example, as well as possible tools to evaluate the model. Several possible extensions of this model are briefly presented.

Bayesian designs in phase II trials are mentioned at the end of the presentation, in order to show the constant development taking place in this domain.