A bibliography of articles – studies and opinion, on the wearing of masks for reducing infection spread and severity.

I consider it prudent to wear a mask when in close contact with people not in your personal “bubble.” I do not feel it makes sense to wear one when outside in the fresh air while maintaining a couple of meters of distance from those who are not in my “bubble.” This is risk management 101. I also think that claims of harm, physical and psychological from the wearing of masks are overblown to the point of absurdity. The other issue, that of social control, seems possibly overblown as well, although I am aghast at some of the draconian steps taken by authorities is some places. There are also a number of ill-informed interpretations of the research literature.

I am by most indicators some sort of socialist libertarian, so yes, a libertarian. I still am not sold on the idea of masks being some part of a plot to control us. I believe that a lot of conspiracist views are well founded, since there are clearly many deep state conspiracies. I am not so sure about this one.

See for instance: https://www.opednews.com/articles/Motivated-Reasoning-by-Mike-Zimmer-Belief_Bias_Ideas_Motivated-Reasoning-200804-599.html and https://www.opednews.com/articles/Masks-and-Motivated-Reason-by-Mike-Zimmer-Belief_Bias_Covid-19_Empathy-200805-253.html

Here is a possibility for Covid-19.

There is evidence that it could be over effectively, with either Ivermectin, or a Hydroxychloroquine (HCQ) + zinc regime. The evidence on the efficacy of the latter for early stage treatment and prophylaxis is incredibly strong now. See: COVID-19 Treatment – Analysis of 107 global studies, with many showing high effectiveness for early treatment 

There is something incredibly rotten in various health bureaucracies. This doctor outlines a case of malfeasance going back decades, driven by ruthless profit making: https://jpands.org/vol25no3/merritt.pdf. It rings true to me.

With respect to Hydroxychlorquine, there are 107 studies now. Some are good, some are not so good; some appear designed to fail, and a couple were entirely fraudulent, but overall, the evidence to me says that authorities are killing thousands of people with their approach in various countries, and saving thousands in others. Some of these authorities are undoubtedly rotten to the core, and some are just embracing group think and looking at bad, bad science.

My friend Elizabeth Woodworth has written about the Hydroxychloroquine issues:

Diagnosis of cases creeping up, deaths staying low. Reasons? Varied I am sure. However, that long tail on death statistics still represents lives lost unnecessarily.

Not all countries show the same pattern, but if you break the numbers out regionally, things become a little clearer. Areas that have not had bad infection rates start to show worse numbers, and this can make the overall country statistics look like a second wave is occurring, but this may only be a new region starting to develop cases. Speculation again. In any case, this may be what we are seeing with the case numbers.

None of this says that if we are in a vulnerable category – age, existing co-morbidities – we should relax our vigilance, our efforts to control infection. I believe in the efficacy of social distances, limited contacts, wearing a mask when I can’t keep my distance, keeping out of poorly ventilated spaces, and recreating out-of-doors. Also, even if you are not likely to be vulnerable, you can still transmit the infection to those who are. Anything you can do to reduce your personal viral load is to the good. Anything you can do to reduce any viral inoculum you are transmitting is to the good. This is not just the flu in a number of dimensions. See for instance: https://www.youtube.com/watch?v=cy1kdZhXsP8 and also https://www.youtube.com/watch?v=cy1kdZhXsP8

“The ideal of a knowledge embodied in strictly impersonal statements now appears self-contradictory, meaningless, a fit subject for ridicule. We must learn to accept as our ideal a knowledge that is manifestly personal.”

— MICHAEL POLANYI

A bibliography on “The Myth of Objectivity”

From the Stanford Encyclopedia of Philosophy,  https://plato.stanford.edu/entries/epistemology-social/

“Until recently, epistemology—the study of knowledge and justified belief—was heavily individualistic in focus. The emphasis was on evaluating doxastic attitudes (beliefs and disbeliefs) of individuals in abstraction from their social environment. Social epistemology seeks to redress this imbalance by investigating the epistemic effects of social interactions and social systems. After giving an introduction, and reviewing the history of the field in sections 1 and 3, we move on to discuss central topics in social epistemology in section 3. These include testimony, peer disagreement, and judgment aggregation, among others. Section 4 turns to recent approaches which have used formal methods to address core topics in social epistemology, as well as wider questions about the functioning of epistemic communities like those in science. In section 5 we briefly turn to questions related to social epistemology and the proper functioning of democratic societies. “

Here is a possibility:

Chris Martenson’s X,Y,Z hypothesis says:

X – Natural immunity – nutrition, genetics, epi-genetics, stress, sleep, youth, reduced co-morbidities

Y – long term T-cell immunity from previous Corona virus infections

Z – short term anti-body immunity from Covid-19

The percentages are debatable, but the overall scheme seems sound. We don’t know for sure what we need to have large scale group immunity, but previous estimates may have been on the high side.

Both DrBeen and Dr. Martenson have given their versions of this. Martenson suggested some percentages, just for the sake of argument, knowing that the data was not there.  See: https://www.youtube.com/watch?v=G7TWiweluwA&list=PLRgTUN1zz_oeQpnJxpeaEkFimDeepqyWf&index=11

Also

Also

Also in Australia, a bimodal distribution of cases and deaths. I would like to see this broken down by region.

Even in France, where cases are growing, deaths are not keeping pace. Why is that? Is it an artifact of the amount of testing? I dunno.

Prophylaxis

Also, places using HCQ for prophylaxis and early stage treatment have in general faired a lot better. There are now 102 studies on it, most showing positive results when taken early, preferably with Zinc. Ivermectin seems to be even better, but I have not found a site that collects studies on that yet.

For HCQ, see https://c19study.com/, the most comprehensive study aggregation site. Studies will routinely contradict one and other, all are confounded in one way or another.  It is the job of systematic meta-analysis to try to make sense of them all. This is not for the faint of heart, and the methods require a deep understanding of the field, and of very advanced statistics. There have been several meta-analyses of HCQ studies.  Some studies have looked at the wrong thing – late stage infection.

 

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I am surprised to find that there is so much material on the limitations of randomized controlled trials (RCT). It is funny that this knowledge has not reached the consciousness of many specialists in the medical community.

In order to understand randomized controlled trials, it helps to understand experimental research methods in general. There are big issues pertaining to research designs, controls, confounds, randomization, statistics, bias, incentive and deception which anyone using scientific evidence to make a judgment or buttress an argument should understand. The discussions become very technical. All scientific evidence is underdetermined in some manner; that is another way of saying that our understanding is confounded. One study does not establish a case; it only points in a certain direction.

A Cheat Sheet on Key Ideas Underlying Research

Below is a cheat sheet on some key scientific ideas, of relevance to research, including randomized controlled trials. It may seem peripheral to the topic of RCT, but I think that understanding some basic issues around research and inference will help in better understanding the documents discussed in the second part of the essay.

Epistemology

Epistemology is the study of knowledge. The philosophy of science applies epistemological thinking to scientific research. Those espousing methods of research should really have some familiarity with this discipline, since it underpins science.

Inference

Inference is the process of making a judgment or buttressing an argument from available evidence. It has three varieties:

1. Deduction is the method of applying the established patterns of formal logic to assertions to assess whether conclusions follow correctly from the premises. There is no guarantee that the original premises are correct, are sound.
2. Induction is the process of generalizing from past experience to predict future events based on perceived patterns, perceived regular occurrences.
3. Abduction is sometimes called “inference to the best explanation.”

Scientific evidence

One study does not establish a case; it only points in a certain direction. All scientific evidence is underdetermined. Evidence follows these dictates:

1. It must be obtained, either produced or found
2. It must be evaluated for reliability, correctness, provenance
3. It must be interpreted, the implications made clear, the fit within an existing body of knowledge examined

Is there scientific objectivity? All of this assessment of evidence happens within the context of current beliefs, scientific and other, and biases. It could not be otherwise.

Probabilities

Probabilities are the odds of future events happening. They can be based on deduction and counting, or based on intuition and conjecture, possibly well-informed, but subjective.

Statistics are generally classed as descriptive or inferential. In descriptive statistics, data is collected, sorted, categorized and summarized in various mathematical ways. In inferential statistics, statistics based on samples are generalized to larger populations of interest, based on the odds of any observed results being true.

Frequentist thinking looks at chances as frequencies, or proportions, in similar events or items.

Bayesian thinking looks at prior probabilities, determined in some fashion, and applies the rules of conditional probability to assess fresh probabilities.

Inferential statistics reasons from sample to populations. From samples we can make predictions about the broader population from which the samples are drawn. This has problematic aspects.

Association

Association lets us see that certain things seem to occur together with regularity.

Correlation gives us a measure of how well, how often this association holds. Mathematically a correlation of zero says that there is no relationship, a correlation of positive one says that they are perfectly correlated, always going together, and a correlation of negative one says that they are perfectly inversely correlated.

Causation

Causation gives us the idea that this association is one in which one factor depends, to a lesser or greater extent, on a second factor. By varying the first factor, the independent factor, we can produce a repeatable change in the second factor, the dependent factor. We can quantify these factors and abstract them, or operationalize them, and call them variables.

Methods

Scientific research methods do not necessarily involve experimental studies, or laboratory work. There are many fields where experiment plays a secondary role.

However, in many fields, research does involve the manipulation of various independent factors in order to see the effects on assumed dependent factors. In general, the experiment is designed to test a research hypothesis, which may be part of a larger theory. In order to conduct experiments, certain standard methods have been developed in order to design experiments which have a good chance of providing correct results and allowing for interpretation. This interpretation is always done within the context of a broader set of beliefs about the nature of the research area.

One of the key aspects of all research is that the factors being manipulated are not necessarily the factors determining the experimental outcomes. We call factors which might be affecting the outcome and which are not being manipulated confounders, or confounds. These possible confounds may be identified and controlled for to some extent through good research design. However, not all confounds will be identified, and cannot be controlled for so easily. Matching and stratifying across subgroups on known factors and randomization across subgroups are mechanisms used to control for confounds. There is a lot more to research design than this, but these ideas are basic.

It is sometimes said that randomization controls for unconscious bias and for biasing factors independent of human judgment.  This is at least partially true. We can regard bias as a set of confounding factors. Bias can occur at all stages of a study, from sample selection, to recording of data, analysis of data, and interpretation of results. Randomization will not help with all of these sorts of bias.

By making the subjects, if human, unaware of the experimental group that they are in, a certain sort of bias can be controlled. This is called a blind trial. By making the experimenters unaware of the groups, we can remove some experimenter bias. This, combined with the first method, is called a double blind trial. There are cases where this works, but there is a gap between theory and practice.

Research Samples

In performing an experiment, subjects are required. This means that they must be selected in some fashion. There will be at least two groups in a well designed experiment, a control group and a treatment group. If the sample control group is different on some significant confounding factor from the sample treatment group, we can end up with worthless results due to this bias. We can help control for this confound by matching across groups on factors considered important such as age, sex, weight and so on. We can also assign subjects to the different groups on some randomized basis. We can combine these strategies. In order to have some confidence in our results, we should use the largest samples we can obtain. Randomization is not particularly effective as a strategy for small sized groups.

We can have unconscious bias in our sample selection, and end up with groups that are not equivalent in key dimensions. We do not want to use ad hoc methods of assigning subjects to a group, or in obtaining subjects for the sample, but real life contingencies such as funding, time frames, available subjects, and other things often get in the way of experimental rigour.

Integrity and Research Design

Experimental design cannot control for dishonesty, for deception. This is worse than bias, for it can not be detected in a reliable fashion, and research designs can not control for it.

RCT major themes

There seem to be a number of significant considerations for the use and interpretation of randomized controlled trials in research, and arguments for the use of other methods in many cases. This is not to say that RCTs do not have an important role to play in research, but they are just one of various methods.  For further reading, see Medicine’s fundamentalists and Randomized controlled trials.

I will not attempt to summarize such a voluminous literature on RTC, but instead will focus on a few articles, published in journals, by respected researchers. The first is by Thomas R. Frieden, M.D.,M.P.H., a former director of the Center for Disease Control and Prevention. In the New England Journal of Medicine he writes “Evidence for Health Decision Making – Beyond Randomized, Controlled Trials.”

The second is by Angus Deaton, FBA, Dwight D. Eisenhower Professor of Economics and International Affairs Emeritus at the Princeton School of Public and International Affairs and the Economics Department at Princeton University and a winner of the Nobel Memorial Prize in Economic Sciences and by co-author Nancy Cartwright, FBA FAcSS, Professor of Philosophy at Durham University and a Distinguished Professor at the University of California, San Diego (UCSD). In Social Science & Medicine, they write, “Understanding and misunderstanding randomized controlled trials.”

I also reference in the Bibliography a piece Alexander Krauss in the Annals of Medicine,“Why all randomised controlled trials produce biased results” and a response by Althouse, Andrew D., Abebe, Kaleab Z. , Collins, Gary S. & Harrell Jr, Frank E. , Journal Annals of Medicine,  Response to “Why all randomized controlled trials produce biased results”, without attempting commentary.

Evidence for Health Decision Making – Beyond Randomized, Controlled Trials

In this paper by a healthcare professional, randomized controlled trials are discussed with respect to benefits and limitations, and other methods of gathering evidence for medical research are presented, along with considerations for use.

From the paper by Frieden (above), the author makes the following points (précised here):

1. There is no single, best approach to the study of health interventions
2. Clinical and public health decisions are almost always made with imperfect data
3. We should be promoting transparency in study methods
4. We should be ensuring standardized data collection for key outcomes
5. We need to use new approaches to improve data synthesis
6. Improved data synthesis provides critical steps in the interpretation of findings
7. Improved data synthesis allows us to better identify data for action
8. It must be recognized that conclusions may change over time
9. There will always be an argument for more research and for better data
10. Waiting for more data is often an implicit decision not to act or to act on the basis of past practice rather than best available evidence
11. The goal must be actionable data — data that are sufficient for clinical and public health action
12. We need methods which produce data that have been derived openly and objectively
13. We need data which enable us to say, “Here’s what we recommend and why.”

Understanding and misunderstanding randomized controlled trials

In this paper by a Nobel prize winning economist and by co-author who is a professor of philosophy discuss various issues in research and in the use of randomized controlled trials.

From the paper by Deaton and Cartwright (above), the authors make the following points:

Bibliography:

Overview

There is a great amount of information available from scholars in medical research and research in general who are able to point out the benefits and limitations of random controlled trials (RCT) in research. RCT can be of value in many cases, but in others they are inappropriate. They are not by any stretch of the imagination “the gold standard.” They are just another mechanism for producing evidence, and can help to reduce some sorts of confounds. It is epistemologically naive to suggest that they somehow trump other investigative methods.

Claiming that Random Controlled Trials (RCT) are the only method to gain scientific understanding shows a limited knowledge of the history of science.  Scientific evidence, the production, evaluation and interpretation, is much more complicated than that.

There are big issues pertaining to research designs, controls, confounds, randomization, statistics, bias, incentive and deception which anyone using scientific evidence to make a judgment or buttress an argument should understand. The discussions become very technical. All scientific evidence is underdetermined. One study does not establish a case; it only points in a certain direction.

Limitations of RCT are discussed briefly in the article here and here.

Below is a reading list obtained via Google Internet searching. I have not looked at more than a fraction of these, and have looked carefully at only a very few.

Some Readings on the Topic of Randomized Controlled Trials