- Email: [email protected]
Observational versus randomised trial evidence
Correspondence
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Concato J, Feinstein AR, Holford TR. The risk of determining risk with multivariable models. Ann Intern Med 1993; 118: 201–10. Concato J, Peduzzi P, Holford TR, Feinstein AR. Importance of events per independent variable in proportional hazards analysis, 1: background, goals, and general strategy. J Clin Epidemiol 1995; 48: 1495–501. Peduzzi P, Concato J, Feinstein AR, Holford TR. Importance of events per independent variable in proportional hazards regression analysis, 2: accuracy and precision of regression estimates. J Clin Epidemiol 1995; 48: 1503–10. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996; 49: 1373–79. Heggie SJ, Wiseman MJ, Cannon GC, et al. Defining the state of knowledge with respect to food, nutrition, physical activity, and the prevention of cancer. J Nutr 2003; 133: 3837S–42S.
I read with enthusiasm John Concato and Ralph Horwitz’s call for the creation of a “human phenotype and habits project”.1 They propose that “patient-oriented researchers should emulate the sense of purpose demonstrated by laboratorybased researchers in the Human Genome Project with a corresponding focus on studying and understanding clinical, social, and behavioural factors”. Success in the Human Phenotype and Habits Project will prevent us from again falling prey to the residual confounding that plagued epidemiological studies touting the cardiovascular benefits of antioxidant vitamins and hormone replacement. Concato and Horwitz’s ambitious goal “to conquer confounding by 2015” might indeed be within our reach, since, as they note, “the number of pertinent factors is likely to be smaller than the 3 billion basepairs in the human genome”. I hereby nominate myself to direct this project, since I have already done some preliminary work to address this very issue. Specifically, I have used existing medical literature to create prediction rules for all patients’ health behaviours and attitudes. These include (but are not limited to): smoking, aerobic exercise, non-aerobic exercise, dental hygiene, perceived psychosocial stress, financial wellbeing, sleep quality, dietary fibre intake, safe-sex practices, and whether or not an individual is able to successfully diffuse muscle tension with yoga. www.thelancet.com Vol 364 August 28, 2004
Of course, since many of these variables are affected by social norms, I have generated interaction terms that modify each of these behaviours and attitudes as a function of geography (by state or province), religious faith, degree of religiosity (in quintiles), and sexual orientation (self-designated and repressed as predicted by use of a validated scoring system [Freud S, unpublished observation]). In anticipation that my model, when made public, might affect patients’ and physicians’ practices (in a vain attempt to control their own destiny), I have also included a correction term that accounts for anticipated attenuation of the model’s predictive accuracy as a function of time. Although it is impossible for me to convey the full scope of my preliminary work in this Correspondence letter, I am pleased to note, as predicted by Concato and Horwitz, that the number of pertinent factors is indeed smaller than the 3 billion basepairs in the human genome. Even by my most conservative estimate, the number of factors is unlikely to exceed 2·7 billion. Once this model is complete, I anticipate that there might be some biostatisticians afraid to use it owing to fear of overfitting. However, I have condensed the output into 53 discrete variables that can be used to predict all human behaviours, from those as mundane as whether an individual eats salad at the beginning or end of the meal, to those as complex as whether a 46-year-old female Jewish internist in London is likely to use an angiotensinconverting-enzyme inhibitor or diuretic as first-line antihypertensive treatment in elderly Australian men (which, incidentally, but not surprisingly, is partly determined by whether or not Mozart is playing in the patients’ waiting area). I look forward to a rich and rewarding collaboration with other researchers interested in this exciting project.
Daniel J Brotman [email protected] Department of General Internal Medicine, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44195, USA 1
Concato J, Horwitz RI. Beyond randomised versus observational studies. Lancet 2004; 363: 1660–61.
HIV/HCV coinfection, HAART, and liver-related mortality Recent discussion of the Article by Nazifa Qurishi and colleagues (Nov 22, p 1708),1 which reported reduced longterm liver-related mortality in patients coinfected with HIV and hepatitis C virus and receiving highly active antiretroviral therapy (HAART), has raised the question of whether the results presented could be the result of survivorship bias. In particular, Julia Del Amo and colleagues2 suggested that the reported reductions in liver-related mortality could simply be the result of a disproportionate number of slow progressors in the HAART group because HIVpositive haemophiliacs in the study who survived long enough to receive HAART (>10 years) might have better outcomes than other infected individuals. Unfortunately, neither this Correspondence letter, nor the authors’ response,3 addressed the major source of survivorship bias in this study, namely the definition of the other two groups: those who received non-HAART regimens (referred to as “patients with ART” in the paper) and those who were untreated. Patients in the study were grouped according to the highest level of antiretroviral treatment that they had ever received. Antiretroviral therapy became available in Europe from the mid-1980s and was selectively used in those with advanced disease. Once HAART regimens became available (from July 1995 at this clinic), most patients on nonHAART regimens would have been switched to HAART. Therefore, the only way in which patients could fall into this non-HAART treated group is if they died before HAART became available, or either chose not to switch to HAART or stopped therapy (both unlikely occurrences). Survival in this group would be expected to be particularly poor as a result. By a similar logic, patients in the untreated group would largely be those who died before antiretroviral therapy first became available (with very short survival times), counterbalanced by slow progressors who remained well and off treatment for long periods of time. Thus, 757
1
2
3
4
5
Concato J, Feinstein AR, Holford TR. The risk of determining risk with multivariable models. Ann Intern Med 1993; 118: 201–10. Concato J, Peduzzi P, Holford TR, Feinstein AR. Importance of events per independent variable in proportional hazards analysis, 1: background, goals, and general strategy. J Clin Epidemiol 1995; 48: 1495–501. Peduzzi P, Concato J, Feinstein AR, Holford TR. Importance of events per independent variable in proportional hazards regression analysis, 2: accuracy and precision of regression estimates. J Clin Epidemiol 1995; 48: 1503–10. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996; 49: 1373–79. Heggie SJ, Wiseman MJ, Cannon GC, et al. Defining the state of knowledge with respect to food, nutrition, physical activity, and the prevention of cancer. J Nutr 2003; 133: 3837S–42S.
I read with enthusiasm John Concato and Ralph Horwitz’s call for the creation of a “human phenotype and habits project”.1 They propose that “patient-oriented researchers should emulate the sense of purpose demonstrated by laboratorybased researchers in the Human Genome Project with a corresponding focus on studying and understanding clinical, social, and behavioural factors”. Success in the Human Phenotype and Habits Project will prevent us from again falling prey to the residual confounding that plagued epidemiological studies touting the cardiovascular benefits of antioxidant vitamins and hormone replacement. Concato and Horwitz’s ambitious goal “to conquer confounding by 2015” might indeed be within our reach, since, as they note, “the number of pertinent factors is likely to be smaller than the 3 billion basepairs in the human genome”. I hereby nominate myself to direct this project, since I have already done some preliminary work to address this very issue. Specifically, I have used existing medical literature to create prediction rules for all patients’ health behaviours and attitudes. These include (but are not limited to): smoking, aerobic exercise, non-aerobic exercise, dental hygiene, perceived psychosocial stress, financial wellbeing, sleep quality, dietary fibre intake, safe-sex practices, and whether or not an individual is able to successfully diffuse muscle tension with yoga. www.thelancet.com Vol 364 August 28, 2004
Of course, since many of these variables are affected by social norms, I have generated interaction terms that modify each of these behaviours and attitudes as a function of geography (by state or province), religious faith, degree of religiosity (in quintiles), and sexual orientation (self-designated and repressed as predicted by use of a validated scoring system [Freud S, unpublished observation]). In anticipation that my model, when made public, might affect patients’ and physicians’ practices (in a vain attempt to control their own destiny), I have also included a correction term that accounts for anticipated attenuation of the model’s predictive accuracy as a function of time. Although it is impossible for me to convey the full scope of my preliminary work in this Correspondence letter, I am pleased to note, as predicted by Concato and Horwitz, that the number of pertinent factors is indeed smaller than the 3 billion basepairs in the human genome. Even by my most conservative estimate, the number of factors is unlikely to exceed 2·7 billion. Once this model is complete, I anticipate that there might be some biostatisticians afraid to use it owing to fear of overfitting. However, I have condensed the output into 53 discrete variables that can be used to predict all human behaviours, from those as mundane as whether an individual eats salad at the beginning or end of the meal, to those as complex as whether a 46-year-old female Jewish internist in London is likely to use an angiotensinconverting-enzyme inhibitor or diuretic as first-line antihypertensive treatment in elderly Australian men (which, incidentally, but not surprisingly, is partly determined by whether or not Mozart is playing in the patients’ waiting area). I look forward to a rich and rewarding collaboration with other researchers interested in this exciting project.
Daniel J Brotman [email protected] Department of General Internal Medicine, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44195, USA 1
Concato J, Horwitz RI. Beyond randomised versus observational studies. Lancet 2004; 363: 1660–61.
HIV/HCV coinfection, HAART, and liver-related mortality Recent discussion of the Article by Nazifa Qurishi and colleagues (Nov 22, p 1708),1 which reported reduced longterm liver-related mortality in patients coinfected with HIV and hepatitis C virus and receiving highly active antiretroviral therapy (HAART), has raised the question of whether the results presented could be the result of survivorship bias. In particular, Julia Del Amo and colleagues2 suggested that the reported reductions in liver-related mortality could simply be the result of a disproportionate number of slow progressors in the HAART group because HIVpositive haemophiliacs in the study who survived long enough to receive HAART (>10 years) might have better outcomes than other infected individuals. Unfortunately, neither this Correspondence letter, nor the authors’ response,3 addressed the major source of survivorship bias in this study, namely the definition of the other two groups: those who received non-HAART regimens (referred to as “patients with ART” in the paper) and those who were untreated. Patients in the study were grouped according to the highest level of antiretroviral treatment that they had ever received. Antiretroviral therapy became available in Europe from the mid-1980s and was selectively used in those with advanced disease. Once HAART regimens became available (from July 1995 at this clinic), most patients on nonHAART regimens would have been switched to HAART. Therefore, the only way in which patients could fall into this non-HAART treated group is if they died before HAART became available, or either chose not to switch to HAART or stopped therapy (both unlikely occurrences). Survival in this group would be expected to be particularly poor as a result. By a similar logic, patients in the untreated group would largely be those who died before antiretroviral therapy first became available (with very short survival times), counterbalanced by slow progressors who remained well and off treatment for long periods of time. Thus, 757