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'I'm going to live forever': the guarantee-time bias
Some study findings are more probably due to a bias in terms of who is included in the studies than to the miraculous effects of occupation, winning awards, or ER status, or whatever is being studied. How should investigators go about avoiding this bias, and more specifically, what should readers look for when they’re reading about some new miracle cure?
Click on the PDF icon above to read the full article.
Some study findings are more probably due to a bias in terms of who is included in the studies than to the miraculous effects of occupation, winning awards, or ER status, or whatever is being studied. How should investigators go about avoiding this bias, and more specifically, what should readers look for when they’re reading about some new miracle cure?
Click on the PDF icon above to read the full article.
Some study findings are more probably due to a bias in terms of who is included in the studies than to the miraculous effects of occupation, winning awards, or ER status, or whatever is being studied. How should investigators go about avoiding this bias, and more specifically, what should readers look for when they’re reading about some new miracle cure?
Click on the PDF icon above to read the full article.
When is an answer not an answer?
When your beloved authors were studying research and statistics, around the time that Methuselah was celebrating his first birthday, we thought we knew the difference between hypothesis testing and hypothesis generating. With the former, you begin with a question, design a study to answer it, carry it out, and then do some statistical mumbo-jumbo on the data to determine if you have reasonable evidence to answer the question. With the latter, usually done after you’ve answered the main questions, you don’t have any preconceived idea of what’s going on, so you analyze anything that moves. We know that’s not really kosher, because the probability of finding something just by chance (a Type I error) increases astronomically as you do more tests.1 So, in the hypothesis generating phase, you don’t come to any conclusions; you just say, “That’s an interesting finding. Now we’ll have to do a real study to see if our observation holds up.”
Click on the PDF icon at the top of this introduction to read the full article.
When your beloved authors were studying research and statistics, around the time that Methuselah was celebrating his first birthday, we thought we knew the difference between hypothesis testing and hypothesis generating. With the former, you begin with a question, design a study to answer it, carry it out, and then do some statistical mumbo-jumbo on the data to determine if you have reasonable evidence to answer the question. With the latter, usually done after you’ve answered the main questions, you don’t have any preconceived idea of what’s going on, so you analyze anything that moves. We know that’s not really kosher, because the probability of finding something just by chance (a Type I error) increases astronomically as you do more tests.1 So, in the hypothesis generating phase, you don’t come to any conclusions; you just say, “That’s an interesting finding. Now we’ll have to do a real study to see if our observation holds up.”
Click on the PDF icon at the top of this introduction to read the full article.
When your beloved authors were studying research and statistics, around the time that Methuselah was celebrating his first birthday, we thought we knew the difference between hypothesis testing and hypothesis generating. With the former, you begin with a question, design a study to answer it, carry it out, and then do some statistical mumbo-jumbo on the data to determine if you have reasonable evidence to answer the question. With the latter, usually done after you’ve answered the main questions, you don’t have any preconceived idea of what’s going on, so you analyze anything that moves. We know that’s not really kosher, because the probability of finding something just by chance (a Type I error) increases astronomically as you do more tests.1 So, in the hypothesis generating phase, you don’t come to any conclusions; you just say, “That’s an interesting finding. Now we’ll have to do a real study to see if our observation holds up.”
Click on the PDF icon at the top of this introduction to read the full article.
Screening for lung cancer
In one of our previous articles, we discussed a study of screening for prostate cancer.1 Now we’re going to move up a bit, at least anatomically, and discuss a study of screening for lung cancer.2 We have previously defined ourselves as curmudgeons and skeptics; to those self-descriptions we now add a new term, “chutzpahniks.” For those of you who may be unfamiliar with that Yiddish term, it means people who have chutzpah, which was defined by Leo Rosten3 as: “that quality enshrined in a man who, having killed his mother and father, throws himself on the mercy of the court because he is an orphan.” Our chutzpah stems from the fact that we are criticizing the results of a study that was published in the New England Journal of Medicine and highly praised in an editorial in that journal.4 If we had less chutzpah, we wouldn’t contemplate such a critique, but then again, if we had less chutzpah, we—a clinical psychologist and a nuclear physicist—wouldn’t be writing articles in a cancer journal. So, on to the study...
To read the full article, click on the PDF icon above.
In one of our previous articles, we discussed a study of screening for prostate cancer.1 Now we’re going to move up a bit, at least anatomically, and discuss a study of screening for lung cancer.2 We have previously defined ourselves as curmudgeons and skeptics; to those self-descriptions we now add a new term, “chutzpahniks.” For those of you who may be unfamiliar with that Yiddish term, it means people who have chutzpah, which was defined by Leo Rosten3 as: “that quality enshrined in a man who, having killed his mother and father, throws himself on the mercy of the court because he is an orphan.” Our chutzpah stems from the fact that we are criticizing the results of a study that was published in the New England Journal of Medicine and highly praised in an editorial in that journal.4 If we had less chutzpah, we wouldn’t contemplate such a critique, but then again, if we had less chutzpah, we—a clinical psychologist and a nuclear physicist—wouldn’t be writing articles in a cancer journal. So, on to the study...
To read the full article, click on the PDF icon above.
In one of our previous articles, we discussed a study of screening for prostate cancer.1 Now we’re going to move up a bit, at least anatomically, and discuss a study of screening for lung cancer.2 We have previously defined ourselves as curmudgeons and skeptics; to those self-descriptions we now add a new term, “chutzpahniks.” For those of you who may be unfamiliar with that Yiddish term, it means people who have chutzpah, which was defined by Leo Rosten3 as: “that quality enshrined in a man who, having killed his mother and father, throws himself on the mercy of the court because he is an orphan.” Our chutzpah stems from the fact that we are criticizing the results of a study that was published in the New England Journal of Medicine and highly praised in an editorial in that journal.4 If we had less chutzpah, we wouldn’t contemplate such a critique, but then again, if we had less chutzpah, we—a clinical psychologist and a nuclear physicist—wouldn’t be writing articles in a cancer journal. So, on to the study...
To read the full article, click on the PDF icon above.
Size, follow-up, data analysis—good; post hoc analysis, interpretation—not so much
It’s easy to know whether a critique of some article or other was written by a statistician or a methodologist—it states how badly the study was done and how incompetently the data were analyzed. Indeed, it is extremely easy to criticize any study, no matter how well it was conducted, because all applied research involves compromises of one sort or another. Well, be prepared for a surprise. In this column, we will be discussing a study that we believe was carried out well and analyzed correctly. That’s not to say that we agree with their conclusions (we don’t), but at least the study yields data that people can argue about without dismissing the paper as a whole.
Click on the PDF icon at the top of this introduction to read the full article.
It’s easy to know whether a critique of some article or other was written by a statistician or a methodologist—it states how badly the study was done and how incompetently the data were analyzed. Indeed, it is extremely easy to criticize any study, no matter how well it was conducted, because all applied research involves compromises of one sort or another. Well, be prepared for a surprise. In this column, we will be discussing a study that we believe was carried out well and analyzed correctly. That’s not to say that we agree with their conclusions (we don’t), but at least the study yields data that people can argue about without dismissing the paper as a whole.
Click on the PDF icon at the top of this introduction to read the full article.
It’s easy to know whether a critique of some article or other was written by a statistician or a methodologist—it states how badly the study was done and how incompetently the data were analyzed. Indeed, it is extremely easy to criticize any study, no matter how well it was conducted, because all applied research involves compromises of one sort or another. Well, be prepared for a surprise. In this column, we will be discussing a study that we believe was carried out well and analyzed correctly. That’s not to say that we agree with their conclusions (we don’t), but at least the study yields data that people can argue about without dismissing the paper as a whole.
Click on the PDF icon at the top of this introduction to read the full article.