Evidence Based Medicine/Health- Part 2

Reporting of Statistics-Absolute vs Relative Risk
How Risky Is A Risk? -Dr Malcom Kendrick

 
 everyone’s eyes glaze over whenever you start talking about statistics, most researchers manage to get away with using relative risk reduction figures when, in reality, they should be shot for doing so. ’
 

You must know the time period, and the absolute risk, for the relative risk to have any meaning.When you talk about a risk, you need to know the absolute risk of a thing happening.. However, whilst the time factor is important, people don’t just bend statistics by ignoring the time factor. What also happens is that people inflate the risk by using relative instead of absolute risks. 

Sunbathing is good for you- Dr Malcolm Kendrick-https://drmalcolmkendrick.org/category/doctoring-data/

‘How about frightening people to stay out of the sun, or slap on factor 50 cream at the first suspicion that a deadly photon may sneak through 10 layers of protective clothing. Not necessarily a good idea, because without vitamin D synthesis in the skin, from exposure to the sun, there is significant danger that we can become vitamin D deficient, which can lead to all sort of other problems.

To what I wrote in Doctoring Data, I would further add that sun exposure is the best known way of increasing NO synthesis throughout the body. This protects the endothelium and, as you would expect, lowers blood pressure (the natural way). So, you are far less likely to die from CVD.

What this study highlights, once again (as with all advice on diet), what we are told to do by mainstream medical research, turns out to be actively damaging to health

 

Calculating absolute risk and relative risk

Authored by   , Reviewed by Prof Cathy Jackson-31-Jan-18,  6 mins read

 

Absolute risk of a disease is your risk of developing the disease over a time period. We all have absolute risks of developing various diseases such as heart disease, cancer, stroke, etc. 

Relative risk is used to compare the risk in two different groups of people. For example, the groups could be smokers and non-smokers

Number needed to treat (NNT)

A figure which is often quoted in medical research is the NNT. This is the number of people who need to take the treatment for one person to benefit from the treatment.

 Helping to decide about taking a treatment

The decision on whether to take a treatment needs to balance various things, such as:

  • What is the absolute risk of getting the disease to start with?
  • How serious is the disease anyway?
  • How much is the absolute risk reduced with treatment?
  • What are the risks or side-effects in taking the treatment?
  • How much does the treatment cost? Is it worth it to an individual if the individual is paying, or is it worth it to the country if the government is paying?

 Treatments for medical conditions are often quoted in the press along the lines ... "New treatment reduces your risk of X disease by 25%". Whilst this sounds good, it usually refers to the relative risk. However, the benefit really depends on how common or rare the disease is. A large reduction of relative risk for a rare disease might not mean much reduction in the absolute risk.

 When deciding on whether to take a treatment, ideally you should decide with your doctor if the reduction in the absolute risk outweighs the risks, side-effects and costs of treatment.

 

 In ‘Today ‘s Coronavirus’ -Data from screening and from Vaccine Trials

 

Screening the healthy population for covid-19 is of unknown value, but is being introduced nationwide

(November 9, 2020)
 
 Many people think that screening is a simple, straightforward approach to prevention, yet experience has taught us that it is counterintuitive, complex, and paradoxical.
 
A key lesson is that you must not assume that screening will achieve the outcome you think it will. Observational studies claim to “show” a benefit because people turn up and you find positive cases, but once a well-designed control trial takes place the real balance of benefit and harm often proves very different. What matters is not just test performance, but the full consequences, desired, unintended, and collateral, of the entire system.
 
Without seeing the research protocol we do not know whether the pilot will measure undesired outcomes. These will include false reassurance for infectious or about-to-be-infectious people who test negative, unnecessary isolation in non-infectious people who test positive, trauma or distress from the testing process especially in children, and massive opportunity cost from diversion of material and intellectual resources away from other more beneficial uses. 
 
COVID 'VACCINES'
 
Ninety per cent
 
Ladies and gentlemen, roll-up, roll-up, roll-up. My new product, just brought to the market this very day, prevents ninety per-cent, yes ninety per-cent of all known things happening to you. Yes, a remarkable ninety per cent. Not sixty per cent, not seventy per cent, no…not even eighty per cent. But ninety of your finest American per cent – of things’.
 

You sir, you still want to know what a thing is. Goodness me, you’re not one of those anti-product protestors are you. Our products undergo the most rigorous testing for safety, themost rigorous. How many, why, at least thirty people sir. We are not one of those fly-by-night organisations.’

‘You still want more information on things? Have I not just told you everything you could possibly need to know sir? Our product can prevent ninety per cent of things. If that is not enough to convince you sir, then I have not idea what else I can say.

 

Peter Doshi: Pfizer and Moderna’s “95% effective” vaccines—let’s be cautious and first see the full data

Because some of the adverse reactions to the vaccine are themselves also symptoms of covid-19 (e.g. fever, muscle pain), one might expect a far larger proportion of people receiving vaccine to have been swabbed and tested for SARS-CoV-2 than those receiving placebo.
 
This amounts to asking investigators to make guesses as to which intervention group patients were in. But when the disease and the vaccine side-effects overlap, how is a clinician to judge the cause without a test? And why were they asked, anyway?
 

Covid-19: Should doctors recommend treatments and vaccines when full data are not publicly available?

BMJ 2020370 doi: https://doi.org/10.1136/bmj.m3260 (Published 24 August 2020)
 

No—Peter Doshi and David Healy

The trust we place in licensed medicines is a strong reason for insisting on full data transparency and reporting, even in the face of a pandemic. Few would disagree with the importance of data transparency, but even during normal times it remains a challenge—so, why demand it during a pandemic? The reason is that data transparency builds the foundation for information we can trust. Data secrecy, by contrast, creates risks too large to take.

 The first critical risk is that of an exaggerated estimate of a product’s benefits when relying on scientific publications alone, not the underlying data. 

 The second critical risk is underestimating a product’s side effects.

 Copious evidence already shows that adverse event data collected in trials are under-reported in journal publications.Moreover, serious adverse events may disappear if classified under rubrics such as “intercurrent illness” or “new medical histories,” which do not require serious adverse event reports—as has happened in vaccine and treatment trials.

Only publicly available full datasets will allow for a thorough assessment of side effects.

 Before any covid-19 treatment or vaccine is made widely available, study protocols should be in the public domain, along with statistical analysis plans, clinical study reports, patient level data, and copies of the correspondence with regulators and other key stakeholders.

Data transparency is not a “nice to have.” Claims made without access to the data—whether appearing in peer reviewed publications or in preprints without peer review—are not scientific claims. Products can be marketed without access to the data, but doctors and professional societies should publicly state that, without complete data transparency, they will refuse to endorse covid-19 products as being based on science.

 

 From  book Doctoring Data by Malcolm Kendrick

 The simple fact is that you will find it very difficult to come across any research that is not biased in some way or other. Some of this is just basic human nature in action. We like to confirm, rather than confront. However, some of this bias is far from innocent. Much of it is deliberately conceived, and done with a clear end in sight. Unfortunately, the problem of ‘conscious’ scientific bias has increased dramatically over the years. We have now reached the point where it has become almost impossible to understand what is being said, as facts are being manipulated for a purpose. Not always a commercial purpose, although money now plays an increasingly powerful role.

If you read medical papers from 50 or 60 years ago they are crystal clear, and the findings are presented in such a way that you can actually understand what the authors are trying to say. Incomprehensible statistical tests were kept to an absolute minimum. It is also difficult to spot any underlying agenda – other than an attempt to establish the truth.

 The truth toolkit .Ten things to remember, to help you make sense of a medical story; 

Association does not mean causation

Lives cannot be saved; we’re all going to die 

Relative mountains are made out of absolute molehills 

Things that are not true are often held to be true 

Reducing numbers does not equal reducing risk 

Challenges to the status quo are crushed – and how! 

Games are played and the players are… 

Doctors can seriously damage your health 

Never believe that something is impossible

 ‘Facts’ can be, and often are, plucked from thin air

 

My Observation

- There is a need to simplify  the distribution of information and cooperation between researcher , clinician and patient. The media should be held accountable for inaccurate reporting .

 

Doctors/ Health Professionals 

- need to be educate on  how to read and interpret  research 

- need to take responsibility in understanding the research and  the way they practice medicine and not hide behind the legality of the system

- need to  be given independence to use their own judgement for case selection within the guidelines  .( The  universal application  of one rule fits all  has lead  to practicing defensive medicine to avoid litigation as opposed to doing what is best for the patient ) 

-  funding and constraints in the legality(/censoring  introduces bias into the type of research used by a clinician for further education  . ( eg courses/lectures . Also the overuse of statistics reporting and linking them  to funding introduses bias towards the treatments provided and therefore the type of research the clinicain seeks out)

 

Research

-reform in research and reporting of data and a more active clinician involvement in the process

- the research needs to be triaged into clinically relevant   and academic 

-the data needs to be presented in a simple , relevant form (  as time limitation for a clinicain  makes it nearly impossible  to spend hours searching through the vast data base)

-research should pertain  to clinical application  that most benefits the patient as opposed to funding and generating statistical data

-cooperation between clinician and researcher .(  At the moment it is a one way process via company reps  and journals , there is censoring and  selective  presentation   of research  to maximise profits)

Media

-People should be  educated and empowered to take responsibility for their health via clear  uncensored information system  as opposed to monopolised, sensationalised and conflicting media reporting articles .

- information should be practical and  pertain  to the socio-economic status and level of education to facilitate accuracy, credibility and to be easier understood and relevant  to the indivdual and target population

 

 

 

 

 

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