- Email: [email protected]
Principles of epidemiological research on drug effects
CORRESPONDENCE
Prospective, randomised trial of sleep deprived versus rested surgeons
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Sir—N Taffinder and colleagues (Oct 10, p 1191)1 studied the proficiency and speed of surgeons using a virtual reality laparoscopic surgery system. They found that surgeons who had been deprived of sleep made about 20% more errors and took about 14% longer, than when rested. Unfortunately, these findings and those of other research 2 of negative effects of sleep deprivation have failed to curtail this common, and correctable, current medical practice. The work of Taffinder and colleagues can be considered as invitro evidence that a current medical practice—using surgeons who have not had much sleep—may be potentially hazardous. Furthermore, they provide a potential method to improve care, namely letting surgeons rest. However, the question remains of whether sleep deprivation causes worse clinical outcomes. Thus, following standard practice in other areas of medical research, a proper prospective trial should be done to study the question. Consider the following trial: “A randomised, prospective, doubleblind, controlled trial of sleep deprived versus rested surgeons.” I have not yet submitted this trial for approval, because, informal conversations with UCSD IRB committee administrators revealed that the probability of approval was small since it would be unethical for a patient to be randomised to a sleepdeprived surgeon. It would be hard to find any institution that would approve such a trial. In other words, the current standard of care—sleepdeprived surgeons—is indeed too unethical to be part of a clinical trial! In the USA, airline pilots and most bus drivers are required to be allowed a certain number of hours sleep each night. No surgeon (or bus driver) would want to ride on bus driven by a sleep-deprived driver. Given the current evidence, unless a randomised, prospective, doubleblind, controlled trial shows that sleep deprivation of surgeons does not diminish the quality of surgical (or medical) care, patients should not have to suffer from the iatrogenic disease of having a sleep-deprived physician. Eric Lewin Altschuler School of Medicine, University of California at San Diego, and Brain and Perception Laboratory, La Jolla, CA 92093, USA (e-mail: [email protected])
THE LANCET • Vol 353 • February 6, 1999
Taffinder NJ, McManus IC, Gul Y, Russell RCG, Darzi A. Effect of sleep deprivation on surgeons’ dexterity on laparoscopy simulator. Lancet 1998; 352: 1191. Editorial. The dangers of not going to bed. Lancet 1989; i: 138–39.
Principles of epidemiological research on drug effects Sir—Hershel Jick and colleagues (Nov 28, p 1767)1 provide a useful overview of pharmacoepidemiological methods. We wish to add to their discussion of confounding by indication, an important source of bias in studies of drug effects with data from observational studies rather than from randomised controlled clinical trials. They used the example of the effect of an antihypertensive drug on the risk of myocardial infarction, where the decision to prescribe the drug was related to a patient’s a priori risk of this event. In this situation, one might incorrectly conclude that the drug increases risk of myocardial infarction unless adjustment is made for this risk in the analysis. Because it is difficult to measure the magnitude of a priori risk, they conclude that “satisfactory control of this potential confounder is not possible”. However, confounding by indication, as in this example, can frequently be controlled if the data are analysed using methods that have been termed as case series, casecrossover, or self-control.2–4 Rather than compare individuals predisposed to using the drug with those who do not use the drug, these methods compare the outcome events within predetermined periods before versus after start of drug therapy within the same individuals. By treating each individual as a separate stratum, the models adjust for a subject effect that serves as a proxy for many unmeasured (or unmeasurable) potential confounders associated with the individual (eg, severity of underlying medical conditions, diet and exercise habits, exposure to chemicals or allergens in the workplace or at home, genetic predisposition to the disease under study). The ability to adjust for many unmeasured confounders comes at a price, however, in that the researcher is limited to studying changes in incidence of outcome events through time and is not able to compare individuals with different characteristics. Only individuals who have one or more outcome events
(cases) are included in the analysis, because only they contribute information about how incidence changes through time. Also, only individuals exposed to the drug in question are analysed. This focus on exposed cases explains the term caseseries analysis. These methods are appropriate when the outcomes of interest can be measured as discrete countable events. We are using an adaptation of conditional Poisson regression3,4 in a case-series study of safety of influenza vaccination for children with asthma.5 The outcome of interest is severe asthma attack. A history of severe asthma, however, predisposes a child to vaccination. With conventional study designs, the confounding by indication is difficult to control, resulting in a strong positive association between vaccination and asthma attacks. When we use the case-series approach to compare the incidence rate of asthma attacks before versus after influenza vaccination in the same children, however, our analysis suggests that confounding is eliminated. Case-series methods can be powerful tools for controlling confounding by indication and we would encourage their appropriate use in pharmacoepidemiological research. *P M Gargiullo, P Kramarz, F DeStefano, R T Chen Vaccine Safety Datalink Team, National Immunization Program, Centers for Disease Control and Prevention, Mailstop E62, Atlanta, Georgia 30333, USA 1
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Jick H, García Ridríguez LA, PérezGutthann S. Principles of epidemiological research on adverse and beneficial drug effects. Lancet 1998; 352: 1767–70. Maclure M. Case-crossover design: a method for studying transient effects on the risk of acute events. Am J Epidemiol 1991; 133: 144–53. Farrington CP. Relative incidence estimation from case series for vaccine safety evaluation. Biometrics 1995; 51: 228–35. Farrington CP, Nash J, Miller E. Case series analysis of adverse reactions to vaccines: a comparative evaluation. Am J Epidemiol 1996; 143: 1165–73. Kramarz P, DeStefano F, Gargiullo P, Chen RT. Accounting for disease severity in assessing the association of influenza vaccine with asthma exacerbation. Pharmacoepidemiology 1998; 7: S113–14 (abstr).
Sir—We share the concerns of Hershel Jick and colleagues1 that observational research on adverse and beneficial drug effects could be discredited if it is not done properly, despite being a useful tool. However, we would like to clarify further some of the points made on the basis of our work at the Tayside Medicines Monitoring Unit.2
501
Prospective, randomised trial of sleep deprived versus rested surgeons
1
2
Sir—N Taffinder and colleagues (Oct 10, p 1191)1 studied the proficiency and speed of surgeons using a virtual reality laparoscopic surgery system. They found that surgeons who had been deprived of sleep made about 20% more errors and took about 14% longer, than when rested. Unfortunately, these findings and those of other research 2 of negative effects of sleep deprivation have failed to curtail this common, and correctable, current medical practice. The work of Taffinder and colleagues can be considered as invitro evidence that a current medical practice—using surgeons who have not had much sleep—may be potentially hazardous. Furthermore, they provide a potential method to improve care, namely letting surgeons rest. However, the question remains of whether sleep deprivation causes worse clinical outcomes. Thus, following standard practice in other areas of medical research, a proper prospective trial should be done to study the question. Consider the following trial: “A randomised, prospective, doubleblind, controlled trial of sleep deprived versus rested surgeons.” I have not yet submitted this trial for approval, because, informal conversations with UCSD IRB committee administrators revealed that the probability of approval was small since it would be unethical for a patient to be randomised to a sleepdeprived surgeon. It would be hard to find any institution that would approve such a trial. In other words, the current standard of care—sleepdeprived surgeons—is indeed too unethical to be part of a clinical trial! In the USA, airline pilots and most bus drivers are required to be allowed a certain number of hours sleep each night. No surgeon (or bus driver) would want to ride on bus driven by a sleep-deprived driver. Given the current evidence, unless a randomised, prospective, doubleblind, controlled trial shows that sleep deprivation of surgeons does not diminish the quality of surgical (or medical) care, patients should not have to suffer from the iatrogenic disease of having a sleep-deprived physician. Eric Lewin Altschuler School of Medicine, University of California at San Diego, and Brain and Perception Laboratory, La Jolla, CA 92093, USA (e-mail: [email protected])
THE LANCET • Vol 353 • February 6, 1999
Taffinder NJ, McManus IC, Gul Y, Russell RCG, Darzi A. Effect of sleep deprivation on surgeons’ dexterity on laparoscopy simulator. Lancet 1998; 352: 1191. Editorial. The dangers of not going to bed. Lancet 1989; i: 138–39.
Principles of epidemiological research on drug effects Sir—Hershel Jick and colleagues (Nov 28, p 1767)1 provide a useful overview of pharmacoepidemiological methods. We wish to add to their discussion of confounding by indication, an important source of bias in studies of drug effects with data from observational studies rather than from randomised controlled clinical trials. They used the example of the effect of an antihypertensive drug on the risk of myocardial infarction, where the decision to prescribe the drug was related to a patient’s a priori risk of this event. In this situation, one might incorrectly conclude that the drug increases risk of myocardial infarction unless adjustment is made for this risk in the analysis. Because it is difficult to measure the magnitude of a priori risk, they conclude that “satisfactory control of this potential confounder is not possible”. However, confounding by indication, as in this example, can frequently be controlled if the data are analysed using methods that have been termed as case series, casecrossover, or self-control.2–4 Rather than compare individuals predisposed to using the drug with those who do not use the drug, these methods compare the outcome events within predetermined periods before versus after start of drug therapy within the same individuals. By treating each individual as a separate stratum, the models adjust for a subject effect that serves as a proxy for many unmeasured (or unmeasurable) potential confounders associated with the individual (eg, severity of underlying medical conditions, diet and exercise habits, exposure to chemicals or allergens in the workplace or at home, genetic predisposition to the disease under study). The ability to adjust for many unmeasured confounders comes at a price, however, in that the researcher is limited to studying changes in incidence of outcome events through time and is not able to compare individuals with different characteristics. Only individuals who have one or more outcome events
(cases) are included in the analysis, because only they contribute information about how incidence changes through time. Also, only individuals exposed to the drug in question are analysed. This focus on exposed cases explains the term caseseries analysis. These methods are appropriate when the outcomes of interest can be measured as discrete countable events. We are using an adaptation of conditional Poisson regression3,4 in a case-series study of safety of influenza vaccination for children with asthma.5 The outcome of interest is severe asthma attack. A history of severe asthma, however, predisposes a child to vaccination. With conventional study designs, the confounding by indication is difficult to control, resulting in a strong positive association between vaccination and asthma attacks. When we use the case-series approach to compare the incidence rate of asthma attacks before versus after influenza vaccination in the same children, however, our analysis suggests that confounding is eliminated. Case-series methods can be powerful tools for controlling confounding by indication and we would encourage their appropriate use in pharmacoepidemiological research. *P M Gargiullo, P Kramarz, F DeStefano, R T Chen Vaccine Safety Datalink Team, National Immunization Program, Centers for Disease Control and Prevention, Mailstop E62, Atlanta, Georgia 30333, USA 1
2
3
4
5
Jick H, García Ridríguez LA, PérezGutthann S. Principles of epidemiological research on adverse and beneficial drug effects. Lancet 1998; 352: 1767–70. Maclure M. Case-crossover design: a method for studying transient effects on the risk of acute events. Am J Epidemiol 1991; 133: 144–53. Farrington CP. Relative incidence estimation from case series for vaccine safety evaluation. Biometrics 1995; 51: 228–35. Farrington CP, Nash J, Miller E. Case series analysis of adverse reactions to vaccines: a comparative evaluation. Am J Epidemiol 1996; 143: 1165–73. Kramarz P, DeStefano F, Gargiullo P, Chen RT. Accounting for disease severity in assessing the association of influenza vaccine with asthma exacerbation. Pharmacoepidemiology 1998; 7: S113–14 (abstr).
Sir—We share the concerns of Hershel Jick and colleagues1 that observational research on adverse and beneficial drug effects could be discredited if it is not done properly, despite being a useful tool. However, we would like to clarify further some of the points made on the basis of our work at the Tayside Medicines Monitoring Unit.2
501