Kaplan-Meier voor Dummies

The now infamous Chapter 6 in the excess mortality report of Meester-Jacobs, by Bram Bakker, has caused quite a stir. The pictures that caused the most controversy were the Kaplan-Meier graphs. All kinds of things were written about it. But not everyone reads graphs with ease. In a subsequent post, about Nivel and an Italian study, they play an important role. Below is a basic explanation and then applied in a discussion of a few graphs from Bram Bakker's piece.

What are KM charts?

A Kaplan-Meier graph shows survival curves. It can also be about the repair success of devices, but we will stick to mortality and medical interventions for now.

The horizontal axis shows the time lapse from the moment of an intervention, the vertical axis shows the percentage of people who are still alive. By putting two of these curves together, you can clearly show the effect of different medical interventions (a prick, for example), or the difference between intervening and not intervening in the event of an incident. Ideally, the intervention is the only difference between the people you are comparing. This is what we strive for in research. In reality, the consideration of 'whether or not to act' already causes major differences between the two groups.

Illustrating. The effect of AED (Automated External Defibrillator CPR) in heart failure is as follows.

You can see here that five days after heart failure, 55% of the people who have undergone AED treatment are still alive and only 40% of those who have not had an AED (it's just an example). In any case, the 'belly' at the beginning of the dotted curve in particular is characteristic: those people were already in critical condition on day 0. So there was an incident with each patient separately after which action was taken (or that was not possible).

In patients with a more chronic, even progressing disease, the curve is more sloping. In this case, the difference between treatment and control grows steadily over the first two years after the start of the medication. The effect then seems to diminish somewhat, the lines come closer together again. This is not about mortality, but the idea is the same: disease inhibition or not.

Now these were graphs that were about people with a disorder.

Charts of Preventive Treatments at healthy people, think of vaccinations, look very different. Ideally, the lines should be as good as straight (or they curve in a similar way, which has to do with age and the term), but with a pure design and a working intervention, they deviate further and further apart during an effective intervention. No heavy belly. After all, the moment of the intervention is independent of having complaints or illness as with the AED. And terminal people did not participate in the measurement here. So no huge dip in the first days or weeks of the intervention in those who were too sick for that.

So much for the theory. Now for the practice.

Bram Bakker analysed the CIMS data: databases with death and vaccination data. In the Meester/Jacobs report, he presented graphs such as these:

We have just seen where such a huge belly comes from from day 0: those people were already (deathly) ill. So these were not just healthy people. It could also be the result of the intervention itself, but in this case that is not possible: it is the not-vaccinated people who have a lower chance of survival. Not vaccinated BECAUSE their end was near.

Their deaths have nothing to do with the quality of the vaccine. "You can see that vaccinations were done during the corona wave," shouted a science journalist...

Now it's about nun-Covid deaths, so the argument "during a corona wave" does raise eyebrows, but come on, everyone can contribute something, including him.

The orange line also shows that the vaccine offers 100% protection: not yet a dent due to Covid, for three years we see a straight line while the vaccination moments still fell in the same period. You should be able to see an epidemic again.

The data is hard, the math is right, the graph is clear, but the story doesn't close. Vaccinated people with lower mortality thanks to the jab? No way. This increased mortality among unvaccinated people does not show the effectiveness of the intervention. The experts had lost sight of that last step for a while.

On X, statisticians also looked at the strange "belly" in the blue line. With some pride they throw around terms like "HRD" (High Risk of Death), "Mortality depletion", "Survival Bias" and so on, as if everything has been explained, apparently without having read or at least understood Bram Bakker's accompanying analyses. He discusses these phenomena explicitly, referring to them as degrees of the Healthy Vaccine Effect.

Whatever name you give it: as long as that initial curvature is in the line, there is no point in comparing if you want to know something about healthy people. Those who ended up in the blue curve were not vaccinated because it no longer made sense or was life-threatening. If you include those deaths of people who have not been vaccinated in the calculations, you get unjustifiably very high vaccine effectiveness. When Nivel realized that (there are signs of that), they thought they could get rid of that effect. But Bram Bakker had already tried that. That doesn't work.

In addition to the HVE, we also have to deal with data pollution (read Maurice) and data gaps (see Herman Steigstra's 'waiting period'), as a result of which deceased vaccinated people have ended up in the unvaccinated group. Then the mortality difference will be fine. If, out of a group of 90% of vaccinated deaths, five are counted among the 10% of unvaccinated people, the number of unvaccinated deaths will be one and a half times as large. The poor survival rates in the first period of the graph are therefore explainable.

Optical exploration

What if we ignore the first curvature? See where the line straightens again and thus a stable effect becomes visible. We then move that line segment upwards to be able to compare it properly. The more oblique the line, the more deaths. And yes, the purple line is steeper.

Hurrah! Will the provaxxers shout: do you see that the unvaccinated have a lower chance of survival than the vaccinated!? The purple line runs down faster!

And indeed: if you had well-composed groups and this graph was about Covid mortality, that could be the conclusion.

But this graph is also about NON-covid mortality: ALL causes of death except Covid! This means that we are still looking at vaccinated people who were already healthier than those who would not be vaccinated (or you have to believe in the Elixir Effect of Life that CBS and RIVM applauded early on. And Dijkgraaf too, for that matter).

Now the difference between those two lines provides valuable information because you can also see approximately how much healthier the vaccinated people were, how much those mortality rates differ. If you correct for this in the graph that does deal with Covid mortality, you correct that away and you may be able to get an impression of how effective the vaccine really is! I'm treading on thin ice with this, but I'm trying to get a feel for the proportions.

So we do that provisionally in the graph with the covid mortality, but oops: those lines are almost the same without that implemented correction...!

If you apply the intended correction to this, we will get a negative vaccine effectiveness...

The real vulnerable

But maybe it's not fair. We have now looked at healthy people in their fifties/sixties without nursing homes, etc. So let's look at a more vulnerable group, people in their seventies/eighties including WLZ, for whom the vaccines were really intended. The more oblique the line, the worse, we remember. Three graphs: Covid deaths on the left, non-Covid on the right and all causes of death below.

Even at the Covid mortality graph at the top left, my sketchily displaced unvaccinated line indicates a slightly higher chance of survival than that of the vaccinated. Not to mention the other two graphs. It's all just experimentation, but still: they are graphs that ask for explanations, instead of providing answers to questions. They don't speak for themselves.

For example, the last graph indicates that unvaccinated oldies do much better than the vaccinated after the initial mortality curve. This phenomenon is known as "survival bias". The composition of the group changed after a year. After those who would die within a year have indeed passed away, a strong group remains that is doing better than the average and even better than the vaccinated average. Vaccination is certainly not a panacea.

The group that does so well at the end is interesting. They are healthy, have assessed this well themselves and feel - or know - that a vaccine will mean little to them. They may also have heard something about risks involved. Or they thought they would die soon and that turned out not to be true. Or they are religious or anti-vaxxers by birth. You should ask them that.

To find out, you would want to split up such a 'subcohort' in a matched way and then vaccinate one half and not vaccinate the other half. This encounters practical and ethical problems because some of them did not want to be vaccinated, which is precisely why they are in the unvaccinated group. They will therefore never participate in an RCT. Each RCT lacks the subjects who know that a vaccination will not do them any good. Are you one of them? Then at least there has been no testing for you! 🤷

Conclusion

We'd better stop with all those Dutch analyses and first see if those CIMS data can still be repaired. I doubt it: Bram Bakker pulled all kinds of things out of the dining cabinet and was still not satisfied.

The file requested by the Biomedical Court of Auditors could have contained crucial information. Unfortunately, the institutes have carried out post-processing on this just to be sure. As a result, the relevant data was impoverished to such an extent that it could no longer serve the purpose for which it was requested.

The lesson to be learned from all this: only a Randomized Controlled Trial generates the best quality information available, provided that it is carried out and interpreted by independent researchers. Transparent data is required. Looking back on fait accompli, the uncertainty always remains too high, especially if the data has been incompetently collected and managed and strict monitoring has been neglected. The Netherlands will not be the only country that has made a mess of it. All over the world, governments have blood on their hands – not something to want to be transparent.

Without a decent, carefully matched serially executed Phase I – II – III RCT, it's Russian Roulette with those vaccinations. Incitement or coercion to play Russian roulette is attempted manslaughter. That will also have been going through the minds of those who tried to boost the now Nivel report.

Afterburners

Armand Girbes used a different strategy. He tried to calm it down: never mind, there is simply nothing to be gained from that data, what happened is what happened. He ignores the fact that we are at the mercy of grumpiness. And that was it: on the basis of the same ignorance, which did not recognize even the most deformed data as such, citizens have been deceived for years. This has cost thousands, possibly tens of thousands of lives.

The fact that new types of "self-replicating" vaccines have already come onto the market in Japan is unthinkable. Again, no proper trials were held, only examinations by butchers inspecting their own meat. That seems unconscionable and it is: institutions and commercial strongholds of power cannot do anything with "conscience". They are reptiles. These organizations, associations, government institutions, they all have the sole task of maintaining and strengthening themselves. Striving for unbridled power growth helps to stay relevant. And they do so with the mandate they have received from us. Surely we want a decisive policy? Well then!

The languishing media have proven to be fused with those institutions. The "Fourth Estate", which should have negotiated transparency instead of propaganda. The media should have exposed and eliminated malignant cell growth in a timely manner. However, our social immune system has been paralyzed by the now metastatic cell growth that it is supposed to fight. In this way, the government was able to blow in a totalitarian direction through institutions and NGOs, towards a globalist central world power, starting with a belligerent Europe. The media is following suit. How is that ever going to work out... Bob de Wit to the rescue?