“Health authorities in the Democratic Republic of Congo are preparing to use an experimental vaccine to help fight an outbreak of Ebola, in what could be an important step towards getting regulatory approval for the world’s first vaccine against the deadly virus,” reports the FT.
First off, can the vaccine be used without regulatory approval? If so, what steps are WHO and the Congo taking to ensure that people received accurate information about the risks of using an unlicensed vaccine? Informed consent is an ethical and also a legal obligation.
According to WHO and the Congo, the Ebola outbreak in an isolated area is now under control.
Standard epidemic methods can be used to contain it, admits the FT. So why take the risk of using the unlicensed Merck vaccine? It might be in the interests of Merck and organizations like WHO, the CDC or the ECDC. But is it in the interests of the people of Congo to be used a guinea pigs for a vaccine that gives patients Ebola according to the first Lancet study published online in July 2015?
In the second, rehashed version of the Lancet study, which has no control group, which was published in August 2015, the original data sample seems to have been changed to arbitrarily exclude all cases of Ebola under ten days after vaccination.
“The vaccine, officially known as rVSV-ZEBOV, was studied in a trial involving 11,841 people. In results reported in the Lancet, a UK medical journal, among the 5,837 people who received the vaccine, no Ebola cases were recorded 10 days or more after vaccination. By comparison, there were 23 cases among those who did not receive the vaccine,” says the FT.
The conclusion that the vaccine did not cause the Ebola cases recorded in under 10 days after the vaccination can only be based on a skewed data sample set.
It is possible that the vaccines caused the Ebola cases because the incubation period of Ebola can be as short as 2 days or as long as 21 days.
Extremely short or long incubation periods for Ebola of two or 21 days may be statistically less likely but they are a fact that can’t just be excluded from any study.
Any claim that the vaccine could not have caused the Ebola cases under ten days is therefore, false and can only be made to appear true to the basis of a statistical lie.
Any statistics showing that the incubation period of Ebola is always, in every single case, more than ten days must be based on a data sample with a built in bias.
If all, real world cases of Ebola where incubation periods are less than 10 days are excluded from a data sample, then, it follows, necessarilty that the application of statistics on that data sample is bound to show that the incubation period is always more than 10 days.
How might a statistical lie in a case like this look?
Statistics could, for example, claim to show that the mean incubation period of Ebola, is, say 14.7 days, with a standard deviation of, for example, 4.3 days. The conclusion to be drawn from the above figures might seem to be that Ebola incubation period is always over ten days.
However, that mean incubation period statistic is meaningless if the sample has a built in bias, and all Ebola cases with a shorter period are excluded in the original data set.
Picking a pre selected sample to give the desired result can be justified using the term “log normal distributon”.
Again, Ebola incubation does not have a log normal distribtion. The incubation period varies from between 2 days to 21 days. An accurate data set would have to include all these additional incubation period distributions from 2 to 21 days, and so be only approximately log normal.
Any data sample that is picked on the basis of log normal distributon is positively skewed and is going to give a false result. There is no scientific justification for assuming that the Ebola incubation period is always under ten days as the second Lancet study does after the first one openly stated that getting Ebola was the main adverse event linked to the vaccine.
Read about statistical lies ( a field related to logic) at Quizlet…
The sample with a built-in bias : the origin of the statistics problems – the sample. Any statistic is based on some sample (because the whole population can’t be tested) and every sample has some sort of bias, even if the person wanting the statistic tries hard to not create any.
Arithmetic Mean: Evenly distributes the total among individuals. Can be unrepresentative when measurements are highly skewed right. (e.g. per capita income)
Median: Value dividing distribution into two equal parts. 50th percentile. (e.g. median household income)
Mode: Most frequently observed outcome (rarely reported with numeric data)
The well-chosen average: how not qualifying an average can change the meaning of the data. Before I delve into this, quickly, when I say, average – what comes to your mind? Sum(x1….xn) / N – right? The arithmetic mean. But I said average, not arithmetic average did I? Not many people know that there are 3 averages