The question of the use of performance-enhancing drugs has been in the newspapers lately, especially as regards baseball players and anabolic steroids. We are here going to discuss the use of many "performance-enhancing" drugs, with regard to their utility (do they really improve performance?), their legality, and whether or not a particular sport bans them. This blog will not discuss the morality of such drug use. I would like to point out, however, that if a drug both improves performance AND has dangerous side-effects, then it may be unfair to force other athletes to risk their health to compete on the same level as a drug-user.
We are not here going to discuss in detail the problem of false-positive urine tests. For those interested in this topic, there was an article several years ago in JAMA, or you can go back in time and read Jerry Rubin's "Steal This Urine Test". We all recall the Seinfeld episode (based on true events) when Elaine tested positive for opium because she had eaten a poppy-seed bagel. There is also the problem of finding a possible drug-masking chemical in your urine (e.g. a diuretic can dilute your urine below the testing cutoff), so the presence of a diuretic can be considered evidence of an attempt to falsify the test. Some companies who refuse to hire cigarette smokers test their urine for co-nicotine, a metabolite of nicotine. Unfortunately, tomatoes and several other vegetables contain a high concentration of co-nicotine, so some vegetarians will test positive for this chemical. As I recall, Advil can give a false-positive urine test for marijuana, and Robitussin a false positive test for PCP.
The question of legality of the drug is an interesting one. Lasix, which reduces pulmonary hemorrhage in horses, is legal only in New York State horse races.
Some sports events permit the use of inhaled beta-agonists for preventing exercise-induced asthma, and others do not. Some drugs are available in certain countries only by prescription, while in other countries they can be purchased over-the-counter, and they may be illegal in a third country.
The first question is whether or not a drug actually enhances athletic performance. We immediately run into the placebo effect, which has been shown to be as high as 30% in some cases. That is, a drug may not actually improve performance, but if an athlete believes that it does, it may. This is not farfetched: if you tell a hypnotized person that you are going to burn them and touch their forearm with an ice cube, they will develop a blister and reddening, just as if they were burned. Or, you can tell a group of subjects that they are about to receive a sleeping pill (or a CNS stimulant), and many will fall asleep rapidly in one case, and stay awake in the other.
STEROIDS (Anabolic, i.e. chemically related to testosterone): I have seen no published research that anabolic steroids improve baseball performance, or any other peak athletic performance. Yes, they do increase muscle mass (so weight lifters and body builders will use them), but it has not been shown that it enables you to drive a baseball or golf ball further, or serve harder in tennis, or throw a football further. It can make football lineman heavier, but it has not been shown to make them any quicker.
GROWTH HORMONE: I have read three studies. One of the two done in older men showed an increase in lean muscle mass, and the other did not. The third was done in younger men, and again showed a slight increase in muscle mass, but increase in athletic performance was not tested for.
CAFFEINE: I suppose we all used No-Doz (= caffeine in two cups of coffee) in college to help us stay up all night to study and then be awake enough to take the test the next day. I know of one study that showed increased scores in the GRE's, and caffeine does increase overall mental alertness. It also may decrease reflex reaction time in some sports, so you can hit the tennis ball earlier, etc. Caffeine also potentiates the pain relieving effects of aspirin and acetimenophen.
BETA-BLOCKERS (INDERAL): These medicines block the effect of adrenalin in your body and, if the drug is lipophilic and crosses the blood-brain barrier (as does Inderal) can exert a CNS calming effect, and I find it especially useful to treat patients with Mitral Valve Prolapse with associated palpitations and/or anxiety attacks. Because it also reduces tremors, many solo concert violinists will take Inderal before a performance. However, in the Biathlon in the Olympics, beta-blockers are banned. In the Biathlon, you ski and then shoot at a target. Obviously, the sooner your heart rate decreases and your hand tremor decreases, the sooner you will have a more accurate shot.
RITALIN: This (and Strattera, Concerta, and several other methylphenidate congeners) are used for students with ADD or ADHD.It seems to enhance concentration and improve academic performance. Since there can be a genetic component, often when a male teenager is diagnosed with ADD, his father also tries Ritalin, and finds that he functions more efficiently at the office. From what I have seen, Ritalin and other CNS stimulants do improve concentration (think of it as a stronger and longer-lasting form of caffeine). It seems that Ritalin makes the brain function more rapidly and more efficiently, and one wonders if all students would be better students if they were on Ritalin. In addition, if one is diagnosed with ADD, then, in addition to Ritalin, the student is given more time to take the SAT's, LSAT's, etc., even though (and this is not a pejorative comment) in the real world a judge will not give a lawyer with ADD an extra week in which to write a brief.
BENZEDRINE: This and other CNS stimulants was issued by the U.S. Gov't. to pilots on long-range missions, and other military members for PRN use when falling asleep could be dangerous.
VIAGRA: (or CIALIS or LEVITRA): This definitely improves the rapidity of penile erection in response to direct physical stimulation, and makes the penis firmer for a longer period of time. The drug is also a direct dilator of the pulmonary artery and its branches in the lung, and has an accepted medical use to treat primary pulmonary hypertension (a disease that can be fatal) as well as prevent high-altitude pulmonary sickness.
HYPNOTICS: The use of sleeping pills is well-accepted, and the only arguments seem to be the length of time they should be used, and whether or not all early-morning awakening is a sign of depression.
ATIVAN/VALIUM/XANAX: I have several patients who will take one of these half an hour before giving a speech or attending a meeting when senior executives will be present. When taken for this reason, the anxiolytic prevents paralyzing anxiety attacks, and permits the patient to function more efficiently.
PROZAC/ZOLOFT/PAXIL: Social phobia and panic attacks are real phenomena. Patients who need this drug to function do function more efficiently. They are encouraged to also get psychiatric help, but often their HMO does not cover it. Whether their personality when they take the drug is the "real" them is, I think, a question for metaphysicians, not family doctors.
TESTOSTERONE: Both men and women manufacture testosterone. I baseline the free testosterone level of all my patients at 40 years of age. If their sex drive then decreases to the point that they are bothered, (whether or not they are depressed), I repeat the measurement, and I use testosterone gel if it has decreased. There have been several articles by a Canadian Ob-GYN showing that menopausal women will also have increased sexual satisfaction if their testosterone level is raised to their pre-menopausal level.
ESTROGEN: Pills or vaginal cream. Some women have a decrease in sex drive and/or severe vaginal dryness in menopause. Many of them benefit from topical or oral estrogen replacement (which may also lead to a decrease in urinary tract infections). I explain this to them and refer them to their gynecoligist.
DIET PILLS: Whether or not they work, they can act as a mild anti-depressant, much in the same way that psychiatrists will prescribe low-dose Dexedrine to nursing home patients to improve their appetites.
DIURETICS: Too dangerous for chronic use for weight loss, but this does not stop jockeys and wrestlers to "make the weight".
PROVIGIL: The latest CNS stimulant on the market. It is prescribed for sleep-shift disorders, or chronic daytime fatigue. It is very popular for medical interns and residents for this very reason. I am unaware of any studies vis-a-vis the improvement of one's abilities as a student.
One of my patients reminded me of Dupont's slogan: "Better living through chemistry". This is ultimately a moral question, of course, in addition to a medical one. Each and every patient is a different combination of biochemicals, brain-body interactions, thought processes and belief systems, so each prescription must be thoroughly discussed with the patient by his family physician, who should be aware of the patient's expectations and limitations, and re-evaluates the patient carefully at periodic intervals.
Sunday, August 23, 2009
Friday, August 7, 2009
Medical Statistics, Research and News
As a former experimental physicist who did funded research for several years, published many papers in physics journals, and was on the editorial board of a physics journal for three years, I have several comments about the quality of medical publications and the interpretation of their results by the lay press.
First, it seems to be a common habit to publish data points in a graph without error bars. This makes it impossible to interpret the results properly. (For lay persons, the error bars show the range of +/- 2 standard deviations, which means that IF the data and error distribution is Gaussian, then there is a 2.5% probability that a repeat measurement would be above and a 2.5% probability of being below the range of the error bars.) Then this mistake is often compounded by connecting the data points by a sequential series of straight lines, rather than a French curve or a least squares fit.
When a result is presented as being "statistically significant", what is meant is that there is only a 5% probability of the result being incorrect (yes, I know I am simplifying here). However, a statistically significant result may not be clinically significant. It is easy to demonstrate that if you have enough subjects, something will be statistically significant. But is it really medically useful to know how to decrease your risk of being killed by a falling meteorite by 50%?
The lay press is also woefully ignorant of the concept of statistical variation. If you tabulate, for instance, cases of breast cancer in every county in a state, one county has to be the highest, and one has to be the lowest, without any "cause". Every time there is a clustering of cases (as in lymphoma in Passaic, N.J. about 15 years ago), there is a rush to find the cause.
Then the relative risk rather than absolute risk is emphasized. Again, if your chance by being killed by a falling meteorite is one in a million, then if I decrease your relative risk by 50%, I have only lowered your absolute risk by 0.0001%.
Finally, and this is the most egregious mistake of all, is the use of surrogate endpoints. For instance, in the study of the effect of lowering cholesterol by the use of Zetia, instead of looking at heart attacks or stroke as a primary endpoint, the thickness of the intima of the carotid artery was used as a surrogate endpoint. If the most common cause of arterial blockage is rupture of a plaque rather than embolic, then this surrogate endpoint is not medically useful. (Personally, I think the evidence points to the anti-inflammatory effects of ASA and statins as reducing the risk of plaque rupture and acute blockage, but no one has yet been able to detect such an acute event when it happens in humans.)
Actually, in fairness to medical researchers, I should mention the main limitation that they face. In physics, if an experimental result is published, other researchers rush to try to repeat the result by a different experimental technique, using different apparatus, to help establish the validity and uniform applicability of the result. Thus, after Wu, Ambler, Heyward and Hoppes verified the theoretically predicted non-conservation of parity in beta decay using electrons, Lederman and Steinberger verified it by studying the decay of muons (possibly aided by Garwin's suggestions). There have been at least ten different verifications of Bell's inequality, using different experimental setups and techniques, which verifies the "spooky" action-at-a-distance required by quantum measurement theory. As soon as Mossbauer announced his effect, physicists rushed to duplicate it around the world. The speed of light has been measured many, many times, as has the dilation of time predicted by the theory of special relativity. On the other hand, in medical research, there is only one direct way to do the experiment, since no other "experimental equipment" exists. This has at least two consequences:(1) there is less glory in verifying a medical result, even if it is done to higher probability by studying more people , and (2) if a result is "very convincing", then virtually no one will repeat it because it would seem to be a waste of money to the funding office, or a risk of malpractice to the research group.
I should also mention that some experiments are not done, because they are deemed not to be in the public interest:There have been several studies in Europe (usually published in Lancet) that seem to indicate that cigarette smokers have a lower incidence of Parkinson's Disease. This suggests a relationship between nicotinic receptors in the brain and dopaminergic neurons. But I predict that no one in the USA would receive federal funding to do a prospective study to see if, in fact, cigarette smoking does protect against Parkinson's Disease, or, indeed has any other benefit.
All studies of new drugs, and many studies of existing drugs, are done on pharmacologically naive patients, who are on no drugs at the time of the experiments. Since most of my patients are on at least four drugs, the results of the study may not apply to them, both as regards to benefits and side effects.(This is typified by the fact that if I have a patient with diabetes, hypertension, osteoarthritis, chronic hepatitis, and GERD, and try to follow all five of the government guidelines, drugs used to treat one problem conflict with the guidelines for another problem.)
Now that I have expressed several of my opinions, let me complete this article by reviewing definitions of several common words and phrases that you may see in research articles:
NNT---Number Needed to Treat---the statistically suggested number of patients to treat with the studied drug in order to achieve the expected outcome in one.
NNH---Number Needed to Harm---similar in concept to NNT,except it is the number to treat to get a bad outcome. If NNT is greater than NNH, you have a problem, unless, perhaps, the outcome in NNT is preventing certain death.
NNS---Number Needed to Sue----this is not generally listed in statistical textbooks. It is the number of patients out of a million who get a bad enough result that a malpractice lawyer thinks it worthwhile to start a class action suit.
Statistically significant---there is less than a 5% probability (one-in-twenty) that the result is due to chance. Alternatively, if you repeat the experiment 20 times, then you would expect to get the same "result" nineteen times.
Type I/Alpha Error---you conclude that there is a statistically significant difference between the control group and the treated group, when there is really NO difference. Similar in concept to a false positive conclusion.
Type II/Beta Error---you conclude that there is no statistically significant difference between the control and the treated group, when there really is. Similar in concept to a false negative conclusion.
Sensitivity---the probability that if you test positive for a disease, you have the disease; i.e. a test with a low false negative rate.
Specificity---the probability that if you test negative for a disease, you do not have the disease; i.e. a test with a low false positive rate.
Common clinical sense---tells you not to believe a positive test result in a particular patient. If you order a panel of 20 tests, each of which has a Gaussian distribution, then the odds are 50:50 that at least one of the tests will fall outside the "normal" range without indicating true disease.
Correlation/Causation---two events can be related in time or space without having a cause-and-effect relationship. Propinquity can always be coincidental, but can also suggest paths for future research.
Confounding---a factor not considered when looking for a cause-and-effect relationship that affects the effect. The best example would be the initial statistical demonstration that coffee drinkers had a higher rate of heart attacks, without allowing for the confounding effect that more coffee drinkers than non-drinkers smoked cigarettes. It is probably impossible to ensure the absence of all confounding effects, since we don't know about many confounding effects, and it is virtually impossible to test for their existence.
Confidence Interval---Similar in concept to error bars around a measured data point. The confidence interval of a result suggests to you the range of the result in which we expect 95% of the studied population to fall.
Intention-to-treat---included all patients who registered for the randomized drug study, whether or not they dropped out of the study.
Incidence---the percentage of the population that develops a given disease in a given period of time.
Prevalence---the percentage of the population that has the disease at a given time. Note that the prevalence of a disease helps to determine whether you want to emphasize avoiding a Type I error or emphasize avoiding a Type II error, as well as a test with high sensitivity or high specificity.
Endpoint---the result you are looking for to determine that a treatment "works".
First, it seems to be a common habit to publish data points in a graph without error bars. This makes it impossible to interpret the results properly. (For lay persons, the error bars show the range of +/- 2 standard deviations, which means that IF the data and error distribution is Gaussian, then there is a 2.5% probability that a repeat measurement would be above and a 2.5% probability of being below the range of the error bars.) Then this mistake is often compounded by connecting the data points by a sequential series of straight lines, rather than a French curve or a least squares fit.
When a result is presented as being "statistically significant", what is meant is that there is only a 5% probability of the result being incorrect (yes, I know I am simplifying here). However, a statistically significant result may not be clinically significant. It is easy to demonstrate that if you have enough subjects, something will be statistically significant. But is it really medically useful to know how to decrease your risk of being killed by a falling meteorite by 50%?
The lay press is also woefully ignorant of the concept of statistical variation. If you tabulate, for instance, cases of breast cancer in every county in a state, one county has to be the highest, and one has to be the lowest, without any "cause". Every time there is a clustering of cases (as in lymphoma in Passaic, N.J. about 15 years ago), there is a rush to find the cause.
Then the relative risk rather than absolute risk is emphasized. Again, if your chance by being killed by a falling meteorite is one in a million, then if I decrease your relative risk by 50%, I have only lowered your absolute risk by 0.0001%.
Finally, and this is the most egregious mistake of all, is the use of surrogate endpoints. For instance, in the study of the effect of lowering cholesterol by the use of Zetia, instead of looking at heart attacks or stroke as a primary endpoint, the thickness of the intima of the carotid artery was used as a surrogate endpoint. If the most common cause of arterial blockage is rupture of a plaque rather than embolic, then this surrogate endpoint is not medically useful. (Personally, I think the evidence points to the anti-inflammatory effects of ASA and statins as reducing the risk of plaque rupture and acute blockage, but no one has yet been able to detect such an acute event when it happens in humans.)
Actually, in fairness to medical researchers, I should mention the main limitation that they face. In physics, if an experimental result is published, other researchers rush to try to repeat the result by a different experimental technique, using different apparatus, to help establish the validity and uniform applicability of the result. Thus, after Wu, Ambler, Heyward and Hoppes verified the theoretically predicted non-conservation of parity in beta decay using electrons, Lederman and Steinberger verified it by studying the decay of muons (possibly aided by Garwin's suggestions). There have been at least ten different verifications of Bell's inequality, using different experimental setups and techniques, which verifies the "spooky" action-at-a-distance required by quantum measurement theory. As soon as Mossbauer announced his effect, physicists rushed to duplicate it around the world. The speed of light has been measured many, many times, as has the dilation of time predicted by the theory of special relativity. On the other hand, in medical research, there is only one direct way to do the experiment, since no other "experimental equipment" exists. This has at least two consequences:(1) there is less glory in verifying a medical result, even if it is done to higher probability by studying more people , and (2) if a result is "very convincing", then virtually no one will repeat it because it would seem to be a waste of money to the funding office, or a risk of malpractice to the research group.
I should also mention that some experiments are not done, because they are deemed not to be in the public interest:There have been several studies in Europe (usually published in Lancet) that seem to indicate that cigarette smokers have a lower incidence of Parkinson's Disease. This suggests a relationship between nicotinic receptors in the brain and dopaminergic neurons. But I predict that no one in the USA would receive federal funding to do a prospective study to see if, in fact, cigarette smoking does protect against Parkinson's Disease, or, indeed has any other benefit.
All studies of new drugs, and many studies of existing drugs, are done on pharmacologically naive patients, who are on no drugs at the time of the experiments. Since most of my patients are on at least four drugs, the results of the study may not apply to them, both as regards to benefits and side effects.(This is typified by the fact that if I have a patient with diabetes, hypertension, osteoarthritis, chronic hepatitis, and GERD, and try to follow all five of the government guidelines, drugs used to treat one problem conflict with the guidelines for another problem.)
Now that I have expressed several of my opinions, let me complete this article by reviewing definitions of several common words and phrases that you may see in research articles:
NNT---Number Needed to Treat---the statistically suggested number of patients to treat with the studied drug in order to achieve the expected outcome in one.
NNH---Number Needed to Harm---similar in concept to NNT,except it is the number to treat to get a bad outcome. If NNT is greater than NNH, you have a problem, unless, perhaps, the outcome in NNT is preventing certain death.
NNS---Number Needed to Sue----this is not generally listed in statistical textbooks. It is the number of patients out of a million who get a bad enough result that a malpractice lawyer thinks it worthwhile to start a class action suit.
Statistically significant---there is less than a 5% probability (one-in-twenty) that the result is due to chance. Alternatively, if you repeat the experiment 20 times, then you would expect to get the same "result" nineteen times.
Type I/Alpha Error---you conclude that there is a statistically significant difference between the control group and the treated group, when there is really NO difference. Similar in concept to a false positive conclusion.
Type II/Beta Error---you conclude that there is no statistically significant difference between the control and the treated group, when there really is. Similar in concept to a false negative conclusion.
Sensitivity---the probability that if you test positive for a disease, you have the disease; i.e. a test with a low false negative rate.
Specificity---the probability that if you test negative for a disease, you do not have the disease; i.e. a test with a low false positive rate.
Common clinical sense---tells you not to believe a positive test result in a particular patient. If you order a panel of 20 tests, each of which has a Gaussian distribution, then the odds are 50:50 that at least one of the tests will fall outside the "normal" range without indicating true disease.
Correlation/Causation---two events can be related in time or space without having a cause-and-effect relationship. Propinquity can always be coincidental, but can also suggest paths for future research.
Confounding---a factor not considered when looking for a cause-and-effect relationship that affects the effect. The best example would be the initial statistical demonstration that coffee drinkers had a higher rate of heart attacks, without allowing for the confounding effect that more coffee drinkers than non-drinkers smoked cigarettes. It is probably impossible to ensure the absence of all confounding effects, since we don't know about many confounding effects, and it is virtually impossible to test for their existence.
Confidence Interval---Similar in concept to error bars around a measured data point. The confidence interval of a result suggests to you the range of the result in which we expect 95% of the studied population to fall.
Intention-to-treat---included all patients who registered for the randomized drug study, whether or not they dropped out of the study.
Incidence---the percentage of the population that develops a given disease in a given period of time.
Prevalence---the percentage of the population that has the disease at a given time. Note that the prevalence of a disease helps to determine whether you want to emphasize avoiding a Type I error or emphasize avoiding a Type II error, as well as a test with high sensitivity or high specificity.
Endpoint---the result you are looking for to determine that a treatment "works".
Subscribe to:
Posts (Atom)