Biomedical research methodology is being dumbed down, making it impossible for research scientists to render results that lead to knowledge and understanding.
Yikes. Now go see if there was an increase in the use of bayesian statistics. It was just getting popular in my dept as I was finishing up back at the turn of the century.
In a different dept, on a different campus, there was a strong reliance on closed source software, magic black boxes, and proprietary data formats. Then they wondered why the people with analytical tools weren't actually interested in collaborating. 🙄
Doctors around the world came together to sign a Declaration of an International Medical Crisis due to the diseases and death co-related to the COVID-19 “vaccines.”
I'm pretty sure it's much worse than this, because if they've completely junked this kind of analysis, then one can be sure that they've junked rigour in their work in general. It's the new procrustean research methodology of making the facts fit the model, in all respects.
As unsettling as this is, I am grateful for you uncovering poor methodology as a standard practice. If we can't identify the problem, we can't tackle the solution. Is there any policy in universities or the NIH, etc. which has bare minimum standards to approve methodology prior to starting research?
“This means the expenditure on research by Congress has become a calamitous waste.” —-It’s not a waste to the mouth-breathing politicians whose North Star is vote-buying and who consider facts to be inconvenient to whatever narratives they’re pushing at the moment to cement their own power.
I appreciated this post a lot, and didn't realize there had been this decline. As an MS biostats who does occasional consulting work still, I can say there has been a big increase in complex modeling but there is less and less simply looking at the data at basic levels, checking assumptions, checking for interactions, etc. I came into a project once and was asked to re-analyze some data where there turned out to be an enormous outlier in the main predictor which had driven the earlier results. Without this single value one reached the opposite conclusion. This analysis had gotten to the manuscript phase with several pairs of eyes on it and no one had apparently done so much as a scatter plot, or they would have seen the issue. Amazing. This is at a globally respected university.
In cooking, quality ingredients and basic things done well are more important than the fanciest techniques or presentations. This is true of statistics too. Far too little thought is put into gathering good, high quality data and then simply getting to know these data properly, without the black box models du jour.
I'm amazed at how many dupes took this assessment at face value instead of (easily) running the numbers themselves. Unless the author's methodology was far different than what I am doing below, the author cooked the numbers to feed your biases (exactly what many of you asserted researchers are doing).
To find out, I searched Pubmed for research articles that used the term “multiple regression”
I then calculated for each year the percentage of studies published in Pubmed that used multiple regression and considered the interaction of variables.
Download the two spreadsheets, combine the results and compute the percentage and you get this yearly data (pretty much a flat line between 3 and 5%)
Here's what the claimed data actually looks like when charted. Percentage including "interaction" is generally increasing over the years quoted, not decreasing !
Did you consider the interaction term involving Pharma money and researchers?
If researchers' salaries are somehow tied to useful knowledge produced, this would not be a problem.
Today there is a disincentive to understand root cause of ANY problem. If someone found the root cause of autism today, billions in research dollars and jobs would evaporate. So no one wants to find the root cause of ANYTHING. If you don't have to produce results, why do the methods matter?
After the pandemic behavior we witnessed they are no longer worthy of the term researchers they are politicized trash.
far too many people have yet to realize this
Sharing, I appreciate what you are doing James Lyons-Weiler.
Yikes. Now go see if there was an increase in the use of bayesian statistics. It was just getting popular in my dept as I was finishing up back at the turn of the century.
In a different dept, on a different campus, there was a strong reliance on closed source software, magic black boxes, and proprietary data formats. Then they wondered why the people with analytical tools weren't actually interested in collaborating. 🙄
Medical Crisis Declaration
Doctors around the world came together to sign a Declaration of an International Medical Crisis due to the diseases and death co-related to the COVID-19 “vaccines.”
https://jamesroguski.substack.com/p/medical-crisis-declaration
Transhumanism, Nanotechnology and Cybernetics
https://lionessofjudah.substack.com/p/transhumanism-nanotechnology-and
I'm pretty sure it's much worse than this, because if they've completely junked this kind of analysis, then one can be sure that they've junked rigour in their work in general. It's the new procrustean research methodology of making the facts fit the model, in all respects.
As unsettling as this is, I am grateful for you uncovering poor methodology as a standard practice. If we can't identify the problem, we can't tackle the solution. Is there any policy in universities or the NIH, etc. which has bare minimum standards to approve methodology prior to starting research?
“This means the expenditure on research by Congress has become a calamitous waste.” —-It’s not a waste to the mouth-breathing politicians whose North Star is vote-buying and who consider facts to be inconvenient to whatever narratives they’re pushing at the moment to cement their own power.
I appreciated this post a lot, and didn't realize there had been this decline. As an MS biostats who does occasional consulting work still, I can say there has been a big increase in complex modeling but there is less and less simply looking at the data at basic levels, checking assumptions, checking for interactions, etc. I came into a project once and was asked to re-analyze some data where there turned out to be an enormous outlier in the main predictor which had driven the earlier results. Without this single value one reached the opposite conclusion. This analysis had gotten to the manuscript phase with several pairs of eyes on it and no one had apparently done so much as a scatter plot, or they would have seen the issue. Amazing. This is at a globally respected university.
In cooking, quality ingredients and basic things done well are more important than the fanciest techniques or presentations. This is true of statistics too. Far too little thought is put into gathering good, high quality data and then simply getting to know these data properly, without the black box models du jour.
Academia is dead. Institutions in general are dead. The individual is the way forward.
I'm amazed at how many dupes took this assessment at face value instead of (easily) running the numbers themselves. Unless the author's methodology was far different than what I am doing below, the author cooked the numbers to feed your biases (exactly what many of you asserted researchers are doing).
To find out, I searched Pubmed for research articles that used the term “multiple regression”
https://pubmed.ncbi.nlm.nih.gov/?term=%22multiple+regression%22
and then I searched for research articles that used the terms “multiple regression” and “interaction”.
https://pubmed.ncbi.nlm.nih.gov/?term=%22multiple+regression%22+%22interaction%22
I then calculated for each year the percentage of studies published in Pubmed that used multiple regression and considered the interaction of variables.
Download the two spreadsheets, combine the results and compute the percentage and you get this yearly data (pretty much a flat line between 3 and 5%)
Search query: "multiple regression"
Year wo interaction with interaction
2022 2741 122 4%
2021 3883 202 5%
2020 3186 160 5%
2019 2868 144 5%
2018 2876 142 5%
2017 2666 122 5%
2016 2739 140 5%
2015 2787 114 4%
2014 2639 113 4%
2013 2613 132 5%
2012 2435 106 4%
2011 2237 95 4%
2010 2013 82 4%
2009 1925 71 4%
2008 1944 66 3%
2007 1740 71 4%
2006 1597 77 5%
2005 1517 62 4%
2004 1341 45 3%
2003 1358 42 3%
2002 1211 38 3%
2001 1176 50 4%
2000 1072 38 4%
1999 1053 47 4%
1998 961 32 3%
1997 957 37 4%
1996 925 29 3%
1995 799 21 3%
1994 727 27 4%
1993 717 21 3%
1992 641 26 4%
1991 542 24 4%
1990 599 24 4%
1989 520 21 4%
1988 388 15 4%
1987 394 21 5%
1986 306 15 5%
1985 303 13 4%
1984 249 16 6%
1983 250 16 6%
1982 227 14 6%
1981 175 10 6%
1980 164 4 2%
1979 136 6 4%
1978 114 3 3%
1977 100 4 4%
1976 93 7 8%
1975 73 3 4%
1974 13 3 23%
1973 22 0%
1972 10 0%
1971 15 0%
1970 10 0%
1969 5 0%
1968 6 0%
1967 9 0%
1966 2 0%
1965 2 0%
1963 3 0%
1962 3 0%
1961 1 0%
1960 1 0%
1959 1 0%
1948 2 0%
Here's what the claimed data actually looks like when charted. Percentage including "interaction" is generally increasing over the years quoted, not decreasing !
https://davidmuncier.substack.com/p/incorrect-analysis
All sources are URL linked so you can download the data yourself and do the same.
Did you consider the interaction term involving Pharma money and researchers?
If researchers' salaries are somehow tied to useful knowledge produced, this would not be a problem.
Today there is a disincentive to understand root cause of ANY problem. If someone found the root cause of autism today, billions in research dollars and jobs would evaporate. So no one wants to find the root cause of ANYTHING. If you don't have to produce results, why do the methods matter?
I just love this. Main effects that are sizeable or meaningful are rare indeed.