Presentation on theme: "Role of Pharmaceutical Statistician March 10, 2009 The Role of the Pharmaceutical Statistician What can be improved? Per Larsson Head of Biostatistics."— Presentation transcript:
Role of Pharmaceutical Statistician March 10, 2009 The Role of the Pharmaceutical Statistician What can be improved? Per Larsson Head of Biostatistics Knowledge Management & Competency Development Novo Nordisk
Role of Pharmaceutical StatisticianSlide no 2March 10, 2009 Stephen Senn (from Dicing with Death) “Statisticians are engaged in an exhausting but exhilarating struggle with the biggest challenge that philosophy makes to science: how do we translate information into knowledge?” Are pharmaceutical statisticians today really translating information into knowledge? Or are they just struggling with producing more and more information? What can be improved?
Role of Pharmaceutical StatisticianSlide no 3March 10, 2009 Roles and responsibilities for statisticians Statistics heavily relies on consistency in the choice of methods and presentations Statistics and data presentations should be done in a consistent way within projects Statisticians in a company or project cannot act independently of each other There is a need for strong Project Statisticians That are fully accountable for all statistics within their project
Role of Pharmaceutical StatisticianSlide no 4March 10, 2009 AstraZeneca Statistical Roles Global Product Statisticians Accountable for all statistics for their product Coordinate statistics worldwide for the product Therapeutic Area Statistical Experts Coordinate statistics worldwide for a therapeutic area e.g. Respiratory medicines Chief Statistical Expert Has an overall statistical responsibility within the company Coordinates Therapeutic Area Statistical Experts and Global Product Statisticians Member of the Project Review Board Statistics is a rather strong function in AstraZeneca
Role of Pharmaceutical StatisticianSlide no 5March 10, 2009 Novo Nordisk Statistical Roles ? – July 2008 Project Statisticians with unclear mandate Unclear with regard to different phases in drug development global responsibility manager Worked differently in different projects Many small projects, large number of project statisticians A title rather than a role, mainly used for promotion Statistics was a rather weak function in Novo Nordisk From July 2008 International Project Statistician Accountable for all statistics within the Key Project Area globally A role, not a title Requires both statistical skills, project management skills, and good communication skills Currently under implementation
Role of Pharmaceutical StatisticianSlide no 6March 10, 2009 Novo Nordisk Statistical Roles (cont.) The new International Project Statistician role, together with a corresponding role for statistical programmers, will be used to standardise statistics within projects Regular meetings with all International Project Statisticians to Share knowledge Standardise between projects Will build a stronger statistical function
Role of Pharmaceutical StatisticianSlide no 7March 10, 2009 Are we doing statistics in the right way? We still have problems with the basics
Role of Pharmaceutical StatisticianSlide no 8March 10, 2009 Analysis populations Lots of people in drug development still believe that one should make the statistical analyses for both the ITT and the PP populations, and that these two analyses correspond to two different questions: ITT – how effective is the drug work if prescribed PP – how effective is the drug work if taken according to instructions A clinical trial cannot answer any of these questions Clinical trials do not mimic real life at all In addition the PP analysis is severely biased
Role of Pharmaceutical StatisticianSlide no 9March 10, 2009 Analysis populations (cont.) And sometimes it gets even worse PK Population PD Population Alternative PP Population...
Role of Pharmaceutical StatisticianSlide no 10March 10, 2009 Analysis populations (cont.) There is only one question a clinical trial can answer Does the drug have any effect given the conditions in the trial We should answer this question with as little bias as possible (and use a conservative approach) ICH E9, Full Analysis Set Use it in the correct way! All data for each individual endpoint And who wants two results anyway??? Use one analysis set (FAS) and report one result All other investigations should be referred to as stability analyses
Role of Pharmaceutical StatisticianSlide no 11March 10, 2009 Non-inferiority trials Lots of people in drug development still believe that a non-inferiority trial can show that treatment A is at least as effective as treatment B Even more scary, some still believe that a p-value of 0.1 means that there is no effect EMEA guidance 2005 “The objective of a non-inferiority trial is sometimes stated as being to demonstrate that the test product is not inferior to the comparator. However, only a superiority trial can demonstrate this.”
Role of Pharmaceutical StatisticianSlide no 12March 10, 2009 Non-inferiority trials (cont.) Statisticians have caused much confusion and contributed to many bad trial designs by not understanding the difficulties with the non-inferiority concept David Brown, MHRA GET RID OF NON-INFERIORITY TRIALS And bring back active control trials The difference between A and B was x, with a 95% confidence interval. (Thus A is likely to be superior to placebo, with difference z etc). Now make your judgements…
Role of Pharmaceutical StatisticianSlide no 13March 10, 2009 What information do we present in tables Some statisticians still believe that it is ALWAYS useful to present SDs or SEMs, and that the median should always be included Is the SD or SEM EVER useful? We should focus more on USEFUL data presentations Treatment A Treatment B Treatment C Total
Role of Pharmaceutical StatisticianSlide no 14March 10, 2009 Error bars Lots of people in drug development still like to see error bars Lots of statisticians still provide them The reader either ignore the error bars or misinterpret them
Role of Pharmaceutical StatisticianSlide no 15March 10, 2009 Use of error bars, Example A truly bad data presentation Primary endpoint P=
Role of Pharmaceutical StatisticianSlide no 16March 10, 2009 The Statistical Analysis Plan Some people in drug development believe that you can vaguely describe the statistical analyses in the protocol and write a SAP later ICH E9, 1998 “Only results from analyses envisaged in the protocol (including amendments) can be regarded as confirmatory.” The SAP is more of an exception, and can introduce minor changes based on a blind review of data Without the statistical methods sufficiently well described in the protocol, one cannot judge whether the design is appropriate
Role of Pharmaceutical StatisticianSlide no 17March 10, 2009 The Statistical Analysis Plan Many protocols are unclear, and some designs are bad, because the statistician did not carefully plan the statistics when the trial was planned There is usually no reason not to include the full details of statistical analyses in the trial protocol
Role of Pharmaceutical StatisticianSlide no 18March 10, 2009 Pre-specification Some people in drug development believe that it is important to pre-specify descriptive statistics and graphs in the trial protocol or the SAP Analyses should be pre-specified, not data presentations Some people believe that is important to pre-specify the significance level There is no multiplicity problem associated with the choice of significance level Pre-specification should not have any impact on the reviewers interpretation of the results There is no value in pre-specifying a higher significance level than 5% (two-sided)
Role of Pharmaceutical StatisticianSlide no 19March 10, 2009 Has “statistically significant” became a buzz- word? Is this better or worse than just “better”? Statisticians have a responsibility to ensure that their terms are used in a correct way
Role of Pharmaceutical StatisticianSlide no 20March 10, 2009 One of the most important improvement we can make Write statistical reports Explain analyses and results in non-technical terms Put the different analyses in their right perspective Interpret the analyses Suggest conclusions Translate the information into knowledge Thanks!