Wear a mask

 WEAR A MASK.

Pollute and poison yourself, other people and the planet.

Teach the children fear-based decision-making; craven compliance with any Government dictate no matter how stupid; rejection of common sense, reason and facts when deciding how it is best to live. Children are malleable and the most powerful lessons they learn are unspoken. Actions speak louder than words.

We have a duty to ensure they will fit the system whatever the system may be, some appear to believe. I personally believe we have a duty to help children make their way through an often challenging world but to do so with courage, common sense, integrity, independence and a questioning mind, albeit, to lesser and greater degrees.

Remember, what you are doing is for the good of all, we are being told. That approach is always a great vote-winner. Either what you do is what you truly believe is for the greater good or it is what someone else tells you is for a greater good which only they understand properly. Which is why it can be so confusing. That ploy also works well with a lot of people.

However, for those prepared to think outside the box, or actually within the box of facts, reason and common sense, some data taken from the Australian Government website which demonstrates the level of risk from Covid is very low. https://www.health.gov.au/.../coronavirus-covid-19-case...

It is harder to find the research articles showing masks are useless on this count, including surgical masks, because the censors now hide it, but do some research into virology and decide if you think a mask prevents viral particles escaping. As one cardiologist said, it would be like controlling mosquitos behind a chain fence.

But, let us trust that there will always be some research being done and it can be found. At least temporarily. Anything which challenges the running narrative is subject to interference so it does not come up quickly in any online search.

Quote: For scientists, the prospect of continued mask use in everyday life suggests areas for further research. In our view, further research is particularly desirable in the gynecological (fetal and embryonic) and pediatric fields, as children are a vulnerable group that would face the longest and, thus, most profound consequences of a potentially risky mask use.

Basic research at the cellular level regarding mask-induced triggering of the transcription factor HIF with potential promotion of immunosuppression and carcinogenicity also appears to be useful under this circumstance. Our scoping review shows the need for a systematic review.

The described mask-related changes in respiratory physiology can have an adverse effect on the wearer’s blood gases sub-clinically and in some cases also clinically manifest and, therefore, have a negative effect on the basis of all aerobic life, external and internal respiration, with an influence on a wide variety of organ systems and metabolic processes with physical, psychological and social consequences for the individual human being.

https://www.mdpi.com/1660-4601/18/8/4344/htm

Australian Government data.

Australia has 25.7 million people as at July 2021.

There have been 32,269 cases of Covid diagnosed. That represents 1.2 people per thousand.There have been 915 deaths recorded out of the 32,269 cases recorded.

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That means the survival rate in Australia is 92.78%. Currently 1,068 cases. They are classified as mild, serious, critical. 99% of all Covid cases in Australia are considered mild. At present, and this is a couple of days since these numbers were crunched from the Federal Government site, of cases, 1,590 were deemed mild, isolating at home and 18 are serious or critical.

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In the last 24 hours 180,180 tests performed. 23 million 310,294 tests done since Covid was recognised. Each test costs a minimum of $100 to perform.

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For every 1,000 people tested 1.4 people are diagnosed in Australia. 183,180 tests in last 24 hours and less than .7 people per thousand diagnosed.

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Covid deaths – 317 deaths were in people 90 plus, 381 deaths in people 80-89; 162 deaths in group 70 to 79, 38 deaths in 60-69; 21 deaths in 0-59. No female under the age of 50 has died from Covid in Australia. Five males under the age of 50 have died of Covid. That includes everyone who is chronically ill, with co-morbidities.

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So this is not a disease of everyone statistically. It is no threat to the young and not a great threat to the healthy of any age.

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9,149,817 shots administered with 39,077 reports of adverse effects. So 4.3 people per thousand experience an adverse reaction.

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According to the TGA 377 people have died after the jab. The TGA confirmed four died FROM the jab, with 883 confirmed cases of bloodclotting. Other diseases and syndromes reported by the TGA and many others are being investigated. What those investigations reveal may or may not be made public I would guess.

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Three times more likely statistically to report or suffer an adverse reaction to the vaccine than you are to get Covid following Government guidelines.

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It is worth tracking the US system of VAERS while remaining aware that estimates are less than 10% of vaccine injuries are reported. In other words, while Australia might do better, the reported injuries/deaths are not going to be accurate given human nature and the power of the system to repress such information.

Lies

 The words roll ever

outwards, the calls

for fear are bold; so

lies can form the 

dance of truth, and

power is denied. 

Conspiracy and theory

There is a lot of talk about fake news and conspiracy theories which in the past we would have called rumours. This is when the art of ‘cherry-picking’ becomes very useful because in rumours or conspiracy theories, the ‘cherries’ are the fruit you must find.

There are always rumours. They are stories which generally come to life because someone who knows something says something to someone else and so it goes. The jungle drums begin beating. It has ever been thus for humans and social media is just the modern version of jungle drums.

Rumours also tap into that great human survival quality, intuition. Without going into it too deeply, as humans we are connected beyond mere words, machines and obvious communication. We communicate with each other at unseen and generally unacknowledged levels.

In other words, like the bees in a hive, we humans know things because other humans know something. That is why ‘word spreads’ so easily and while ‘word of mouth’ is not necessarily completely reliable, neither is it ever completely wrong either. There will be elements of fact, truth, reality, scattered noisily between the thoughts and words, and however scrambled the message may become, there will be a message for those who take the time to look.

Social media also works very hard to censor the 'drums' and to limit exchanges of information, rumours, and what are called conspiracy theories. Fake News it is now called.

It is worth remembering that rumours will always have some basis in truth even if the story becomes more colourful as it passes through human minds and mouths. So, what could the 'truths' be in what are now called conspiracy theories?

WHAT ARE THE CONSPIRACY THEORIES

1. That Covid is a bioweapon.

2. That a cabal of powerful people want to reset society and the world.

3. That someone, somewhere wants to kill off a lot of people to reduce the world's population, or rather, solve what is seen as a problem of over-population.

4. That these Covid vaccines are designed to sterilise humans.

5. That these Covid tests or vaccines are designed to implant microchips so society can be tracked and more easily controlled.

6. that the Covid vaccines will change your DNA

There are probably more, but let us consider these few.

 

BIOWEAPONS

There is no doubt that ‘gain of function’ research goes on and while the claims are that this is done for the best of intentions, any student of history and human nature knows that the ‘best of intentions’ can lead to the worst of outcomes very easily. In truth, wherever things like, ‘for the good of all,’ ‘in your best interests,’ ‘a better world,’ ‘for your own sake,’ accompany any proposal, think twice and implement even more common sense and caution for an even more important maxim is: No good deed goes unpunished. In other words, in every gift there is a curse.

Is ‘gain of function’ research dangerous? It most certainly is. Could it be used in a bioweapon? It most certainly could.

Is ‘gain of function’ research an important part of vaccine development? Probably not.
Quote: Thomas Inglesby, director of the Center for Health Security at Johns Hopkins, told me last year that he doesn’t think the benefits for vaccine development hold up in most cases. “I haven’t seen any of the vaccine companies say that they need to do this work in order to make vaccines,” he pointed out. “I have not seen evidence that the information people are pursuing could be put into widespread use in the field.” https://www.vox.com/2020/5/1/21243148/why-some-labs-work-on-making-viruses-deadlier-and-why-they-should-stop

So, ‘gain of function’ research, which we now know does happen could certainly be part of the development of a bioweapon. Is the belief, rumour, conspiracy theory therefore so silly? Not at all. That Covid began as part of a bioweapon experiment is highly likely. That it would be used or is being used as a bioweapon is not so likely.

A belief, rumour, theory associated with this is that research began in the United States but security fears led to it being outsourced to China? Is this likely? Certainly. Is this possible? Absolutely. And could many of the world’s top science-medical organisations have been involved in this research? Of course they could.

And, if they were, would that not make Governments very nervous about anyone finding out what the involvement of their science-medical organisations had been? You betcha. Would that not also make Governments hypersensitive to possible outcomes and liable to over-react, do more, not less, become more than a tad hysterical in response? Of course it would.

So, one could easily argue that this rumour/conspiracy theory has a good chance of being sourced in some solid truths, with no doubt, variations on the various themes involved. What we do know is that never before in human history have the healthy been locked up in the face of an infectious ‘disease’ and never before have people needed to have a test for a viral disease to find out if they are SICK. Asymptomatic infection, as claimed for Covid, is a new invention which has no basis in immunology or virology. In fact it is itself a classic conspiracy theory.

CABAL RESET

How logical or sensible is it for people to believe that a powerful group wishes to re-organise the world, reset societies, make massive changes to how we live?

Since there is such a proposal/plan from very powerful people and organisations this is not such a strange thing for people to believe. The World Economic Forum has mooted such a plan and stated that the Covid Pandemic is a great opportunity to implement it.

Now, the plan is, on paper anyway, not very specific, but rarely do plans in the beginning ever reflect outcomes. And that is because humans are opportunistic and when changes are made there will always be unforeseen actions and consequences because it is impossible to think through every possible action, reaction or outcome. This is why ideas which are great on paper like Communism, Capitalism, Welfare, Universal Healthcare and Religion can fail so terribly: because they do not take into account human nature.

And the most powerful ideas/forces are those with good intentions. It is easier to drag people along if you can convince them and yourself that this is in the best interests of everyone and that ultimately it is for the greater good. No doubt this is why the original plans are always so light and fluffy.

Quote: According to Klaus Schwab the Founder and Executive Chairman of WEF: “The pandemic represents a rare but narrow opportunity to reflect, reimagine, and reset our world.”

The WEF suggests that the most urgent reason for a reset is the COVID-19 crisis which has serious long-term consequences for economic growth, public debt, employment, and human wellbeing. If left unaddressed some believe the world will be left less sustainable, less equal, and more fragile. https://adepteconomics.com.au/what-is-the-great-reset-agenda-and-is-there-need-for-concern/

So, in essence, despite the plethora of stories around trying to calm suspicion about a GREAT RESET there most certainly are such plans in existence and no doubt in motion. I personally don’t believe it is so easy to reset humanity or society but my reading of history means I know that there will be plenty of people prepared to give it a damn good try and at any cost.

So this ‘conspiracy theory’ has more than one leg on which to stand.

REDUCING POPULATION
This one would seem to not make much sense given that profits require consumers and the fewer consumers the lower the profits. Elite lifestyles also require plenty of cogs in the wheels to keep things turning comfortably.

Would the world benefit from reduced population? For the climate change alarmists there might be some gristle in this one, but overall, it seems fairly flimsy unless one is heavily invested in the funeral parlour industry.

However, when humans suspect that someone or something out there is up to no good, they will fear for their life and the lives of those they love first of all.

So this one only stands as a very common and universal human fear of death. In short, it is not a surprising ‘theory’ even if it is unlikely.

STERILISING HUMANS

Given the plethora of material on how the planet is sagging under its population weight, this one is possible. Although the irony is that the human population is on its way down anyway with one-child policies now revealing ‘unseen’ outcomes in the world’s two most populous countries, China and India, combined with fewer children being born to couples in the Western world.

It is hard to see what anyone could gain from this one since fertility rates are dropping horrifically worldwide anyway, no doubt from the fact that people and planet are drowning in synthetic hormones from the contraceptive pill and health is worse than it was, particularly in children.

So this ‘conspiracy theory’ is probably legless. But only if people are aware of the true nature of human infertility over recent decades and increasing.

MICROCHIP IMPLANTS

Well they certainly exist and are already in use to a minor degree. https://www.bbvaopenmind.com/en/technology/innovation/technology-under-your-skin/

Could or would a Government insert such a thing into citizens without their knowledge? It is not likely but it is certainly possible under the ‘we believe this is for your own good and in the best interests of society’ clause.

History is littered with horrors done in the name of the ‘greatest good,’ for this is the big catchcry for tyrants and a sucker call for too many citizens. We humans do not change much and the same themes and forces can be found in every tyranny in our recorded history. Which is why, no doubt, the maxim, ‘it seemed like a good idea at the time,’ came into existence.

So, this conspiracy theory probably goes into the, not likely but highly possible basket. Which means as a rumour/theory/belief it is hardly unusual and simply reflects the human capacity to detect a potential ‘shitfest’ before it happens.

VACCINES CHANGING YOUR DNA

All of the vaccines for Covid are genetic vaccines which involve manipulation (meddling) with cell function. The mRNA vaccines, Pfizer, use synthetic RNA to ‘instruct’ cell function. 

Quote: RNA evolved billions of years ago and is naturally found in every cell in your body. Scientists think RNA originated in the earliest life forms, even before DNA existed. The process that converts DNA to mRNA to protein is the foundation for how the cell functions. https://theconversation.com/what-is-mrna-the-messenger-molecule-thats-been-in-every-living-cell-for-billions-of-years-is-the-key-ingredient-in-some-covid-19-vaccines-158511

So, this messenger for DNA has been created artificially, synthetically, in a laboratory and will be injected into your body to manipulate cell function in ways never done before. Does that process equate with ‘changing your DNA?’ If one wished to ‘muddy the public waters,’ the answer would be No.

But if one wished to nitpick ever so slightly, the answer would be, Yes, because synthetic RNA, the messenger for DNA, is manipulating cell function in ways never done before and who really knows what the impact on DNA might be since none of the genetic vaccines are approved which means full and final testing and safety studies, i.e. outcomes, were not completed and are not known. If a process involving the conversion of DNA to mRNA to protein is something which happens naturally and has done for millions of years, then what impact will it have where the mRNA is manmade, synthetic, artificial and it is interfering in a natural process? We have yet to find out.

So, could this new genetic treatment change your DNA in some way? Yes it could. Is it likely? We don’t know. Is it possible? Yes it is. Are the science-medical answers to this fear, rumour, conspiracy theory a bit fluffy? Yes they are because they must be and because even the experts do not truly know what outcomes will be. They believe this synthetic RNA will self-destruct as natural RNA does but, since this genetic experiment is a poorly tested FIRST, the fact is they do not know and public fears are not only natural they are sensible.

The above-cited conspiracy theories are mostly likely to have some grains of truth and mostly possible. In essence, the most sensible thing to do in the face of what is called a ‘conspiracy theory’ is to not summarily reject it, but do a bit of work and have a good, long, hard think about whether or not it is possible, if it is likely, and are these outcomes you would support and defend?

One thing is certain, the human capacity to be suspicious, to exercise scepticism and to communicate feelings, thoughts, theories, doubts, fears, hopes, facts is what has enabled us to survive and generally thrive for millennia.

There is also such a thing as gut instinct and we need to remember that. Humans lie and never more so than when they have powerful vested agendas. They lie even more when there are profits at risk and when they know they can ‘sell their story’ to the public in the name of good intentions. These realities are recorded throughout our human history and we ignore and forget their truths at our peril.

When we stop asking questions, stop thinking for ourselves and censor those who try, we are betraying the freedoms for which so many fought and died and squandering the future and hopes of our children. Sure, it might not be that bad, or that dangerous, or that terrible – then again it might be. We don’t know, we just do not know what is going on at present or what outcomes we potentially face. And hindsight, while a wonderful thing, is seeing things clearly when it is too late.

Scepticism is needed more than ever in times like this. Not cynicism, but healthy, questioning, open-minded, clear-headed scepticism. Your Government does not have your best interests at heart. It has its own. The same applies to the science-medical industries, the media and every other system or organisation which can take an opportunity to take an opportunity. Become a questioner. Carpe Diem!

 

There can never be too many questions

 In the modern age science has become a religion and allopathic medicine, one of its cults. People put enormous faith in scientists and doctors and no doubt that brings great comfort.

The qualifier is, of course, that modern allopathic medicine does have some valuable skills, particularly in the realms of crisis/trauma and necessary surgery. However, it is possible to recognise such benefits without putting absolute faith in the system or its scientists and doctors. All systems have flaws and the more faith invested in them, the greater the flaws. Whatever the acknowledged benefits of modern allopathic medicine, the reality is it also does harm, fails on many counts, and has the potential to do enormous harm to people and the planet and increasingly so.

A little scepticism is a healthy quality when dealing with such systems. Because, in recent decades it has become increasingly clear that such faith in science-medicine is not only undeserved, it is dangerous. Modern science-medicine has such a powerful force within society, as we can see with the ongoing Covid hysteria, that trusting it to 'get it right' can not only be dangerous to health, it can be dangerous to freedom and our human rights.

Science set out to challenge religion a couple of centuries ago and it succeeded more than perhaps many imagined. However, the scientific system of enquiry decided that its adversary was fundamentalist religion and so, it took its place at the opposite end of that broad spectrum, and unwittingly became a 'shadow' or doppleganger, of that which it decried and despised.

Modern science now dictates, abuses and distorts in the same sorts of ways as did orthodox or fundamentalist religions in the past and as some still do today. The constant cry, that the 'system' has its own checks and balances, which manages, monitors and moderates the mere mortals who work within it, is being revealed as at best inadequate and at worst, a lie. It is a lie which most within the scientific system also believe and one repeated so frequently over recent centuries that most people would believe it without question.

Lies which take hold are frequently vastly more powerful than truths because at some level the human mind can recognise something which is not true, but if there is a deep need to believe the lie, then the defences will grow greater and stronger and all challenges to the lie will be mightily rejected. Until, as often happens, a weakness develops in the 'wall' of belief and the defences become vulnerable.

We should always question everything, every system and every 'expert,' and never more so than in the realms of science-medicine if science is to be credible and medicine is to be safe.

Quote: Why Most Published Research Findings Are False

Published on July 13, 2021

Written by journals.plos.org

There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field.

In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance.

Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.

Published research findings are sometimes refuted by subsequent evidence, with ensuing confusion and disappointment. Refutation and controversy is seen across the range of research designs, from clinical trials and traditional epidemiological studies [1–3] to the most modern molecular research [4,5].

There is increasing concern that in modern research, false findings may be the majority or even the vast majority of published research claims [6–8]. However, this should not be surprising. It can be proven that most claimed research findings are false. Here I will examine the key factors that influence this problem and some corollaries thereof.

Modeling the Framework for False Positive Findings

Several methodologists have pointed out [9–11] that the high rate of nonreplication (lack of confirmation) of research discoveries is a consequence of the convenient, yet ill-founded strategy of claiming conclusive research findings solely on the basis of a single study assessed by formal statistical significance, typically for a p-value less than 0.05. Research is not most appropriately represented and summarized by p-values, but, unfortunately, there is a widespread notion that medical research articles should be interpreted based only on p-values.

Research findings are defined here as any relationship reaching formal statistical significance, e.g., effective interventions, informative predictors, risk factors, or associations. “Negative” research is also very useful. “Negative” is actually a misnomer, and the misinterpretation is widespread. However, here we will target relationships that investigators claim exist, rather than null findings.

It Can Be Proven That Most Claimed Research Findings Are False

As has been shown previously, the probability that a research finding is indeed true depends on the prior probability of it being true (before doing the study), the statistical power of the study, and the level of statistical significance [10,11]. Consider a 2 × 2 table in which research findings are compared against the gold standard of true relationships in a scientific field. In a research field both true and false hypotheses can be made about the presence of relationships.

Let R be the ratio of the number of “true relationships” to “no relationships” among those tested in the field. R is characteristic of the field and can vary a lot depending on whether the field targets highly likely relationships or searches for only one or a few true relationships among thousands and millions of hypotheses that may be postulated. Let us also consider, for computational simplicity, circumscribed fields where either there is only one true relationship (among many that can be hypothesized) or the power is similar to find any of the several existing true relationships.

The pre-study probability of a relationship being true is R/(R + 1). The probability of a study finding a true relationship reflects the power 1 – β (one minus the Type II error rate). The probability of claiming a relationship when none truly exists reflects the Type I error rate, α. Assuming that c relationships are being probed in the field, the expected values of the 2 × 2 table are given in Table 1. After a research finding has been claimed based on achieving formal statistical significance, the post-study probability that it is true is the positive predictive value, PPV.

The PPV is also the complementary probability of what Wacholder et al. have called the false positive report probability [10]. According to the 2 × 2 table, one gets PPV = (1 – β)R/(R – βR + α). A research finding is thus more likely true than false if (1 – β)R > α. Since usually the vast majority of investigators depend on a = 0.05, this means that a research finding is more likely true than false if (1 – β)R > 0.05.

What is less well appreciated is that bias and the extent of repeated independent testing by different teams of investigators around the globe may further distort this picture and may lead to even smaller probabilities of the research findings being indeed true. We will try to model these two factors in the context of similar 2 × 2 tables.

Bias

First, let us define bias as the combination of various design, data, analysis, and presentation factors that tend to produce research findings when they should not be produced. Let u be the proportion of probed analyses that would not have been “research findings,” but nevertheless end up presented and reported as such, because of bias. Bias should not be confused with chance variability that causes some findings to be false by chance even though the study design, data, analysis, and presentation are perfect.

Bias can entail manipulation in the analysis or reporting of findings. Selective or distorted reporting is a typical form of such bias. We may assume that u does not depend on whether a true relationship exists or not. This is not an unreasonable assumption, since typically it is impossible to know which relationships are indeed true. In the presence of bias (Table 2), one gets PPV = ([1 – β]R + uβR)/(R + α − βR + u − uα + uβR), and PPV decreases with increasing u, unless 1 − β ≤ α, i.e., 1 − β ≤ 0.05 for most situations.

Thus, with increasing bias, the chances that a research finding is true diminish considerably. This is shown for different levels of power and for different pre-study odds in Figure 1. Conversely, true research findings may occasionally be annulled because of reverse bias. For example, with large measurement errors relationships are lost in noise [12], or investigators use data inefficiently or fail to notice statistically significant relationships, or there may be conflicts of interest that tend to “bury” significant findings [13].

There is no good large-scale empirical evidence on how frequently such reverse bias may occur across diverse research fields. However, it is probably fair to say that reverse bias is not as common. Moreover measurement errors and inefficient use of data are probably becoming less frequent problems, since measurement error has decreased with technological advances in the molecular era and investigators are becoming increasingly sophisticated about their data.

Regardless, reverse bias may be modeled in the same way as bias above. Also reverse bias should not be confused with chance variability that may lead to missing a true relationship because of chance.

Testing By Several Independent Teams

Several independent teams may be addressing the same sets of research questions. As research efforts are globalized, it is practically the rule that several research teams, often dozens of them, may probe the same or similar questions. Unfortunately, in some areas, the prevailing mentality until now has been to focus on isolated discoveries by single teams and interpret research experiments in isolation.

An increasing number of questions have at least one study claiming a research finding, and this receives unilateral attention. The probability that at least one study, among several done on the same question, claims a statistically significant research finding is easy to estimate. For n independent studies of equal power, the 2 × 2 table is shown in Table 3: PPV = R(1 − βn)/(R + 1 − [1 − α]n − Rβn) (not considering bias). With increasing number of independent studies, PPV tends to decrease, unless 1 – β < a, i.e., typically 1 − β < 0.05.

This is shown for different levels of power and for different pre-study odds in Figure 2. For n studies of different power, the term βn is replaced by the product of the terms βi for i = 1 to n, but inferences are similar.

Corollaries

A practical example is shown in Box 1. Based on the above considerations, one may deduce several interesting corollaries about the probability that a research finding is indeed true.

Box 1. An Example: Science At Low Pre-Study Odds

Let us assume that a team of investigators performs a whole genome association study to test whether any of 100,000 gene polymorphisms are associated with susceptibility to schizophrenia. Based on what we know about the extent of heritability of the disease, it is reasonable to expect that probably around ten gene polymorphisms among those tested would be truly associated with schizophrenia, with relatively similar odds ratios around 1.3 for the ten or so polymorphisms and with a fairly similar power to identify any of them.

Then R = 10/100,000 = 10−4, and the pre-study probability for any polymorphism to be associated with schizophrenia is also R/(R + 1) = 10−4. Let us also suppose that the study has 60% power to find an association with an odds ratio of 1.3 at α = 0.05. Then it can be estimated that if a statistically significant association is found with the p-value barely crossing the 0.05 threshold, the post-study probability that this is true increases about 12-fold compared with the pre-study probability, but it is still only 12 × 10−4.Now let us suppose that the investigators manipulate their design, analyses, and reporting so as to make more relationships cross the p = 0.05 threshold even though this would not have been crossed with a perfectly adhered to design and analysis and with perfect comprehensive reporting of the results, strictly according to the original study plan. Such manipulation could be done, for example, with serendipitous inclusion or exclusion of certain patients or controls, post hoc subgroup analyses, investigation of genetic contrasts that were not originally specified, changes in the disease or control definitions, and various combinations of selective or distorted reporting of the results.

Commercially available “data mining” packages actually are proud of their ability to yield statistically significant results through data dredging. In the presence of bias with u = 0.10, the post-study probability that a research finding is true is only 4.4 × 10−4. Furthermore, even in the absence of any bias, when ten independent research teams perform similar experiments around the world, if one of them finds a formally statistically significant association, the probability that the research finding is true is only 1.5 × 10−4, hardly any higher than the probability we had before any of this extensive research was undertaken!

Corollary 1: The smaller the studies conducted in a scientific field, the less likely the research findings are to be true. Small sample size means smaller power and, for all functions above, the PPV for a true research finding decreases as power decreases towards 1 − β = 0.05. Thus, other factors being equal, research findings are more likely true in scientific fields that undertake large studies, such as randomized controlled trials in cardiology (several thousand subjects randomized) [14] than in scientific fields with small studies, such as most research of molecular predictors (sample sizes 100-fold smaller) [15].

Corollary 2: The smaller the effect sizes in a scientific field, the less likely the research findings are to be true. Power is also related to the effect size. Thus research findings are more likely true in scientific fields with large effects, such as the impact of smoking on cancer or cardiovascular disease (relative risks 3–20), than in scientific fields where postulated effects are small, such as genetic risk factors for multigenetic diseases (relative risks 1.1–1.5) [7].

Modern epidemiology is increasingly obliged to target smaller effect sizes [16]. Consequently, the proportion of true research findings is expected to decrease. In the same line of thinking, if the true effect sizes are very small in a scientific field, this field is likely to be plagued by almost ubiquitous false positive claims. For example, if the majority of true genetic or nutritional determinants of complex diseases confer relative risks less than 1.05, genetic or nutritional epidemiology would be largely utopian endeavors.

Corollary 3: The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to be true. As shown above, the post-study probability that a finding is true (PPV) depends a lot on the pre-study odds (R). Thus, research findings are more likely true in confirmatory designs, such as large phase III randomized controlled trials, or meta-analyses thereof, than in hypothesis-generating experiments. Fields considered highly informative and creative given the wealth of the assembled and tested information, such as microarrays and other high-throughput discovery-oriented research [4,8,17], should have extremely low PPV.

Corollary 4: The greater the flexibility in designs, definitions, outcomes, and analytical modes in a scientific field, the less likely the research findings are to be true. Flexibility increases the potential for transforming what would be “negative” results into “positive” results, i.e., bias, u. For several research designs, e.g., randomized controlled trials [18–20] or meta-analyses [21,22], there have been efforts to standardize their conduct and reporting. Adherence to common standards is likely to increase the proportion of true findings. The same applies to outcomes.

True findings may be more common when outcomes are unequivocal and universally agreed (e.g., death) rather than when multifarious outcomes are devised (e.g., scales for schizophrenia outcomes) [23]. Similarly, fields that use commonly agreed, stereotyped analytical methods (e.g., Kaplan-Meier plots and the log-rank test) [24] may yield a larger proportion of true findings than fields where analytical methods are still under experimentation (e.g., artificial intelligence methods) and only “best” results are reported.

Regardless, even in the most stringent research designs, bias seems to be a major problem. For example, there is strong evidence that selective outcome reporting, with manipulation of the outcomes and analyses reported, is a common problem even for randomized trails [25]. Simply abolishing selective publication would not make this problem go away.

Corollary 5: The greater the financial and other interests and prejudices in a scientific field, the less likely the research findings are to be true. Conflicts of interest and prejudice may increase bias, u. Conflicts of interest are very common in biomedical research [26], and typically they are inadequately and sparsely reported [26,27]. Prejudice may not necessarily have financial roots. Scientists in a given field may be prejudiced purely because of their belief in a scientific theory or commitment to their own findings.

Many otherwise seemingly independent, university-based studies may be conducted for no other reason than to give physicians and researchers qualifications for promotion or tenure. Such nonfinancial conflicts may also lead to distorted reported results and interpretations. Prestigious investigators may suppress via the peer review process the appearance and dissemination of findings that refute their findings, thus condemning their field to perpetuate false dogma. Empirical evidence on expert opinion shows that it is extremely unreliable [28].

Corollary 6: The hotter a scientific field (with more scientific teams involved), the less likely the research findings are to be true. This seemingly paradoxical corollary follows because, as stated above, the PPV of isolated findings decreases when many teams of investigators are involved in the same field. This may explain why we occasionally see major excitement followed rapidly by severe disappointments in fields that draw wide attention.

With many teams working on the same field and with massive experimental data being produced, timing is of the essence in beating competition. Thus, each team may prioritize on pursuing and disseminating its most impressive “positive” results. “Negative” results may become attractive for dissemination only if some other team has found a “positive” association on the same question. In that case, it may be attractive to refute a claim made in some prestigious journal.

The term Proteus phenomenon has been coined to describe this phenomenon of rapidly alternating extreme research claims and extremely opposite refutations [29]. Empirical evidence suggests that this sequence of extreme opposites is very common in molecular genetics [29].

These corollaries consider each factor separately, but these factors often influence each other. For example, investigators working in fields where true effect sizes are perceived to be small may be more likely to perform large studies than investigators working in fields where true effect sizes are perceived to be large. Or prejudice may prevail in a hot scientific field, further undermining the predictive value of its research findings.

Highly prejudiced stakeholders may even create a barrier that aborts efforts at obtaining and disseminating opposing results. Conversely, the fact that a field is hot or has strong invested interests may sometimes promote larger studies and improved standards of research, enhancing the predictive value of its research findings. Or massive discovery-oriented testing may result in such a large yield of significant relationships that investigators have enough to report and search further and thus refrain from data dredging and manipulation.

Read the rest here: journals.plos.org

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Time travel

Within our minds we construct time,

to make the world we know, a little

better managed, with limits we can

show, which will within the stretch

of years, make sense of moments 

small, and let us count the living

hours in ways we think we chose.