Meir J. Stampfer

Bio: Meir J. Stampfer is an academic researcher from Harvard University. The author has contributed to research in topics: Risk factor & Relative risk. The author has an hindex of 277, co-authored 1414 publications receiving 283776 citations. Previous affiliations of Meir J. Stampfer include University of California, San Francisco & University of Auckland.

Inflammation, Aspirin, and the Risk of Cardiovascular Disease in Apparently Healthy Men

Abstract: Background Inflammation may be important in the pathogenesis of atherothrombosis. We studied whether inflammation increases the risk of a first thrombotic event and whether treatment with aspirin decreases the risk. Methods We measured plasma C-reactive protein, a marker for systemic inflammation, in 543 apparently healthy men participating in the Physicians' Health Study in whom myocardial infarction, stroke, or venous thrombosis subsequently developed, and in 543 study participants who did not report vascular disease during a follow-up period exceeding eight years. Subjects were randomly assigned to receive aspirin or placebo at the beginning of the trial. Results Base-line plasma C-reactive protein concentrations were higher among men who went on to have myocardial infarction (1.51 vs. 1.13 mg per liter, P<0.001) or ischemic stroke (1.38 vs. 1.13 mg per liter, P = 0.02), but not venous thrombosis (1.26 vs. 1.13 mg per liter, P = 0.34), than among men without vascular events. The men in the quartile wit...

Reproducibility and validity of a semiquantitative food frequency questionnaire

Abstract: The aim of this study was to evaluate the reproducibility and validity of a 61-item semiquantitative food frequency questionnaire used in a large prospective study among women. This form was administered twice to 173 participants at an interval of approximately one year (1980-1981), and four one-week diet records for each subject were collected during that period. Intraclass correlation coefficients for nutrient intakes estimated by the one-week diet records (range = 0.41 for total vitamin A without supplements to 0.79 for vitamin B6 with supplements) were similar to those computed from the questionnaire (range = 0.49 for total vitamin A without supplements to 0.71 for sucrose), indicating that these methods were generally comparable with respect to reproducibility. With the exception of sucrose and total carbohydrate, nutrient intakes from the diet records tended to correlate more strongly with those computed from the questionnaire after adjustment for total caloric intake. Correlation coefficients between the mean calorie-adjusted intakes from the four one-week diet records and those from the questionnaire completed after the diet records ranged from 0.36 for vitamin A without supplements to 0.75 for vitamin C with supplements. Overall, 48% of subjects in the lowest quintile of calorie-adjusted intake computed from the diet records were also in the lowest questionnaire quintile, and 74% were in the lowest one of two questionnaire quintiles. Similarly, 49% of those in the highest diet record quintile were also in the highest questionnaire quintile, and 77% were in the highest one or two questionnaire quintiles. These data indicate that a simple self-administered dietary questionnaire can provide useful information about individual nutrient intakes over a one-year period.

Total energy intake: implications for epidemiologic analyses

Abstract: Associations between intake of specific nutrients and disease cannot be considered primary effects of diet if they are simply the result of differences between cases and noncases in body size, physical activity, and metabolic efficiency. Epidemiologic studies of diet and disease should therefore be directed at the effect of nutrient intakes independent of total caloric intake in most instances. This is not accomplished with nutrient density measures of dietary intake but can be achieved by employing nutrient intakes adjusted for caloric intake by regression analysis. While pitfalls in the manipulation and interpretation of energy intake data in epidemiologic studies have been emphasized, these considerations also highlight the usefulness of obtaining a measurement of total caloric intake. For instance, if a questionnaire obtained information on only cholesterol intake in a study of coronary heart disease, it is possible that no association with disease would be found even if a real positive effect of a high cholesterol diet existed, since the caloric intake of cases is likely to be less than that of noncases. Such a finding could be appropriately interpreted if an estimate of total caloric intake were available. The relationships between dietary factors and disease are complex. Even with carefully collected measures of intake, consideration of the biologic implications of various analytic approaches is needed to avoid misleading conclusions.

Vitamin E Consumption and the Risk of Coronary Heart Disease in Men

Abstract: Background The oxidative modification of low-density lipoproteins increases their incorporation into the arterial intima, an essential step in atherogenesis. Although dietary antioxidants, such as vitamin C, carotene, and vitamin E, have been hypothesized to prevent coronary heart disease, prospective epidemiologic data are sparse. Methods In 1986, 39,910 U.S. male health professionals 40 to 75 years of age who were free of diagnosed coronary heart disease, diabetes, and hypercholesterolemia completed detailed dietary questionnaires that assessed their usual intake of vitamin C, carotene, and vitamin E in addition to other nutrients. During four years of follow-up, we documented 667 cases of coronary disease. Results After controlling for age and several coronary risk factors, we observed a lower risk of coronary disease among men with higher intakes of vitamin E (P for trend = 0.003). For men consuming more than 60 IU per day of vitamin E, the multivariate relative risk was 0.64 (95 percent confidence in...

Bias in meta-analysis detected by a simple, graphical test

Abstract: Objective: Funnel plots (plots of effect estimates against sample size) may be useful to detect bias in meta-analyses that were later contradicted by large trials. We examined whether a simple test of asymmetry of funnel plots predicts discordance of results when meta-analyses are compared to large trials, and we assessed the prevalence of bias in published meta-analyses. Design: Medline search to identify pairs consisting of a meta-analysis and a single large trial (concordance of results was assumed if effects were in the same direction and the meta-analytic estimate was within 30% of the trial); analysis of funnel plots from 37 meta-analyses identified from a hand search of four leading general medicine journals 1993-6 and 38 meta-analyses from the second 1996 issue of the Cochrane Database of Systematic Reviews . Main outcome measure: Degree of funnel plot asymmetry as measured by the intercept from regression of standard normal deviates against precision. Results: In the eight pairs of meta-analysis and large trial that were identified (five from cardiovascular medicine, one from diabetic medicine, one from geriatric medicine, one from perinatal medicine) there were four concordant and four discordant pairs. In all cases discordance was due to meta-analyses showing larger effects. Funnel plot asymmetry was present in three out of four discordant pairs but in none of concordant pairs. In 14 (38%) journal meta-analyses and 5 (13%) Cochrane reviews, funnel plot asymmetry indicated that there was bias. Conclusions: A simple analysis of funnel plots provides a useful test for the likely presence of bias in meta-analyses, but as the capacity to detect bias will be limited when meta-analyses are based on a limited number of small trials the results from such analyses should be treated with considerable caution. Key messages Systematic reviews of randomised trials are the best strategy for appraising evidence; however, the findings of some meta-analyses were later contradicted by large trials Funnel plots, plots of the trials9 effect estimates against sample size, are skewed and asymmetrical in the presence of publication bias and other biases Funnel plot asymmetry, measured by regression analysis, predicts discordance of results when meta-analyses are compared with single large trials Funnel plot asymmetry was found in 38% of meta-analyses published in leading general medicine journals and in 13% of reviews from the Cochrane Database of Systematic Reviews Critical examination of systematic reviews for publication and related biases should be considered a routine procedure

Atherosclerosis — An Inflammatory Disease

Abstract: Atherosclerosis is an inflammatory disease. Because high plasma concentrations of cholesterol, in particular those of low-density lipoprotein (LDL) cholesterol, are one of the principal risk factors for atherosclerosis,1 the process of atherogenesis has been considered by many to consist largely of the accumulation of lipids within the artery wall; however, it is much more than that. Despite changes in lifestyle and the use of new pharmacologic approaches to lower plasma cholesterol concentrations,2,3 cardiovascular disease continues to be the principal cause of death in the United States, Europe, and much of Asia.4,5 In fact, the lesions of atherosclerosis represent . . .

Practical statistics for medical research

Abstract: Most medical researchers, whether clinical or non-clinical, receive some background in statistics as undergraduates. However, it is most often brief, a long time ago, and largely forgotten by the time it is needed. Furthermore, many introductory texts fall short of adequately explaining the underlying concepts of statistics, and often are divorced from the reality of conducting and assessing medical research. Practical Statistics for Medical Research is a problem-based text for medical researchers, medical students, and others in the medical arena who need to use statistics but have no specialized mathematics background. The author draws on twenty years of experience as a consulting medical statistician to provide clear explanations to key statistical concepts, with a firm emphasis on practical aspects of designing and analyzing medical research. The text gives special attention to the presentation and interpretation of results and the many real problems that arise in medical research