PHASTAR listed in the Sunday Times Virgin Fast Track 100 league table – A personal note of thanks

Phastar-2The company I founded in 2007, PHASTAR, is today listed on the prestigious Sunday Times Virgin Fast Track 100 league table of Britain’s fastest growing companies. 2015 Fast Track 100 logoThis award recognises the extraordinary growth of the company, due to hard working staff, as well as satisfied and loyal customers returning with repeat business.

Personally, this achievement means a huge amount to me, as I do not get much time to take a step back and realise that the company I have been working tirelessly on for the last 8 years is now a success. Coming from a working class background – I grew up on a council estate in the suburbs of Glasgow – with no initial capital added additional hurdles in establishing a successful business. There have been many challenges along the way, and there have been numerous occasions when I considered progressing the business an impossible task. Running a company can be an emotional roller coaster, with fantastically rewarding days but also occasions when you have to face some really tough challenges. Without the support of everyone around me – friends, family, staff members and loyal customers – the business would not be a viable proposition. I want to recognise the efforts that everyone has made, and express my sincere thanks for support over the years.

This achievement is absolutely a team award. There have been many contributors involved over the years whose support has been instrumental. My partner, Mardi, who has not only put up with my long working hours, but has also been a great support along the way, and has always been there in good times and bad. Since day 1 of setting up the company, there has been a marked change in the effort I am able to make socially, with friends and family. I used to be a social organiser, but don’t get the time these days. I am very grateful to all my friends and family who have put up with my pitiful efforts over the last 8 years, but are still there when I need them.

The team at PHASTAR amaze me. I always revel in the fact that I have personally chosen most of the people at PHASTAR, which has the pleasant side effect that I really like the team of people I work with. I was deeply touched earlier this year when we had a challenging deadline – working on a revolutionary drug for lung cancer – which had been developed from the laboratory bench to being approved for sales in a record time. The team rallied, with most of the company stepping in to help, and we delivered what had seemed like an impossible task, in the wee hours that morning. I was amazed at the level of dedication that everyone in the company demonstrated that day, and continue to demonstrate each and every day. I want to sincerely thank all PHASTAR team members – each of you have contributed to the success of the company, and to achieving this award.

There are quite a few ex-employees who have moved on to fresh pastures, but have made a significant contribution to the company. Many of these individuals I still consider to be friends, and I absolutely value the work that was carried out when they were at PHASTAR, and am happy to have remained friends through thick and thin.

Particularly in the last few weeks, I have spent a significant amount of time talking to our current clients. I am overwhelmed by the positive feedback I have received – that our work is head and shoulders above our much larger competitors. As well as the quality of our work, I hear that we are much more flexible and easier to work with than most other suppliers of statistical and medical writing services. It is certainly my ambition to focus on delivering the best quality work with the minimum of fuss and administrative overhead. Without the support and repeat business from these customers, we would not be a growing and successful company.

Every year at PHASTAR I learn new skills, face new challenges, welcome new staff and work with new customers. I relish and look forward to facing the future challenges, with your help and support.

Thank you so much!

Kevin.

Lamivudine in Ebola

On the internet, I came across a story that a doctor in Liberia who was desperate for Ebola treatments, tried lamivudine, an anti-viral used in hepatitis and HIV on 15 patients infected with Ebola. This caught my attention, as there’s currently no approved treatments for Ebola.

I checked the estimated death rates on the WHO Ebola Factsheet page. They have a table of the number of people infected with Ebola, and the number of subsequent deaths. Although there are lots of references to this table with an estimate of a 50% death rate, I calculate that there were 1590 deaths in 2387 Ebola cases, giving an estimated death rate of 67%.

The statistical analysis from this point is fairly easy – it’s a simple binomial distribution. If the “true” death rate is 67%, then the probability of seeing only 2 deaths or less out of 15 is 3.2 x 10^5, which would usually be represented as p<0.001. In other words, it would be extremely likely that the lamivudine is having a beneficial effect.

Another way of looking at the analysis is using a 2 by 2 table – whether the patients survive or not, and whether they received lamivudine or not. Using the previous Ebola outbreaks data (1590/2387), and carrying out a Fisher’s Exact test to see whether the lamivudine data come from the same underlying distribution, gives p<0.001. The odds ratio for surviving ebola is 13.0. Using the numbers from this outbreak (3865/8033), gives a Fisher’s Exact result of p=0.008 (odds ration 6.0).

If these results are confirmed, then it would appear that this is very strong evidence that lamivudine is helpful in saving lives in the fight against the Ebola virus, and should be investigated in further trials as a matter of urgency.

There has been a paper published in the Lancet online that details the reasons why randomised clinical trials, the usual gold standard for collecting medical evidence on the effectiveness of drugs, is difficult and potentially unethical in the current Ebola situation. Given this report of some effectiveness of lamivudine, I expect there will be other reports of success or failure of the treatment of patients with other drugs. It seems imperative that a global resource is set up to capture this information and ensure that each patient treated with Ebola contributes information and knowledge to the treatment of subsequent infected patients. A global registry of information on treatments given and survival outcome is needed as a matter of urgency.

10 most common clinical trials data problems

One of the common tasks I do in my day-to-day job is to review analyses and summaries of clinical trial data. I’ve summarised the most common problems I see with the hope that it might help you avoid these in the future:

  1. White blood cell differential units being mixed up – frequently these lab tests are measured either in absolute terms (number of cells per volume) or as a percentage of the total number of white blood cells. Often the absolute and percentage results are both reported. A decision should be taken early on in the trial as to how the data should be reported. I would recommend reporting in both absolute and percentage terms. Often this will require converting from one to the other. It’s also very useful to do a check by adding up all the white blood cell components and ensuring that the total is in the same ball park as the reported WBC count.
  2. Reasons for withdrawing from the study – often clinical trial investigators have different approaches to reporting the main reason for a patient withdrawing from a study. When looked at overall, these patterns can cause difficulty in interpreting the results. These reasons should be reviewed with the clinical team throughout the trial. It’s also useful to check the number of patients who have withdrawn due to side effects against the adverse event data reported.
  3. Scale of graphs – to help interpretation of results, often clinicians will want to compare graphs, and to facilitate this, the graphs need to be on the same scale. Additionally, if graphs are showing pre-treatment versus post-treatment results, graphs should use the same size of scale on both axes, so the 45 degree line shows the “no-difference” point.
  4. Footnotes – add footnotes to make sure that the results in a table are clear, and can be understood with reference to other documents as much as is possible. Ideally abbreviations used in a table or graph should be footnoted.
  5. Number of subjects – usually tables present the number of subjects in the population under study (“big N”), as well as the number of subjects with available data (“little n”). The “big N” number needs to reflect the number of subjects under study, and this is frequently incorrect for subgroup analyses. I have seen cases where “little n” is bigger than “big N” which is clearly rubbish (not from people in my company though!).
  6. Denominators for percentage calculations – related to the above point with “big N”, the denominators for summaries should be carefully planned and documented. If the denominator is not the total number of subjects in the analysis population, this needs to be justified.
  7. Fasting glucose – someone laboratories and people handling clinical data always seem to struggle with fasting and non-fasting glucose. A patient either has fasted or they haven’t, and hopefully the state of the patient will be accurately recorded. The normal ranges for fasting glucose is different from the non-fasting state, which is why this is important.
  8. Decimal places – often the number of decimal places is not appropriate. There should be enough decimal places to allow adequate interpretation of the results, and ideally not any more!
  9. Units of measurement – always state what the unit of measurement is on every output and analysis.
  10. Outliers – a check should always be carried out when working with any continuous data (e.g. lab measurements, vital signs parameters) to ensure that any outlying values are not erroneous.