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      Six Persistent Research Misconceptions

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          ABSTRACT

          Scientific knowledge changes rapidly, but the concepts and methods of the conduct of research change more slowly. To stimulate discussion of outmoded thinking regarding the conduct of research, I list six misconceptions about research that persist long after their flaws have become apparent. The misconceptions are: 1) There is a hierarchy of study designs; randomized trials provide the greatest validity, followed by cohort studies, with case–control studies being least reliable. 2) An essential element for valid generalization is that the study subjects constitute a representative sample of a target population. 3) If a term that denotes the product of two factors in a regression model is not statistically significant, then there is no biologic interaction between those factors. 4) When categorizing a continuous variable, a reasonable scheme for choosing category cut-points is to use percentile-defined boundaries, such as quartiles or quintiles of the distribution. 5) One should always report P values or confidence intervals that have been adjusted for multiple comparisons. 6) Significance testing is useful and important for the interpretation of data. These misconceptions have been perpetuated in journals, classrooms and textbooks. They persist because they represent intellectual shortcuts that avoid more thoughtful approaches to research problems. I hope that calling attention to these misconceptions will spark the debates needed to shelve these outmoded ideas for good.

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          Estimating interaction on an additive scale between continuous determinants in a logistic regression model.

          To determine the presence of interaction in epidemiologic research, typically a product term is added to the regression model. In linear regression, the regression coefficient of the product term reflects interaction as departure from additivity. However, in logistic regression it refers to interaction as departure from multiplicativity. Rothman has argued that interaction estimated as departure from additivity better reflects biologic interaction. So far, literature on estimating interaction on an additive scale using logistic regression only focused on dichotomous determinants. The objective of the present study was to provide the methods to estimate interaction between continuous determinants and to illustrate these methods with a clinical example. and results From the existing literature we derived the formulas to quantify interaction as departure from additivity between one continuous and one dichotomous determinant and between two continuous determinants using logistic regression. Bootstrapping was used to calculate the corresponding confidence intervals. To illustrate the theory with an empirical example, data from the Utrecht Health Project were used, with age and body mass index as risk factors for elevated diastolic blood pressure. The methods and formulas presented in this article are intended to assist epidemiologists to calculate interaction on an additive scale between two variables on a certain outcome. The proposed methods are included in a spreadsheet which is freely available at: http://www.juliuscenter.nl/additive-interaction.xls.
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            A prospective, observational study of postmenopausal hormone therapy and primary prevention of cardiovascular disease.

            Most primary prevention studies have found that long-term users of postmenopausal hormone therapy are at lower risk for coronary events, but numerous questions remain. An adverse influence of hormone therapy on cardiovascular risk has been suggested during the initial year of use; however, few data are available on short-term hormone therapy. In addition, the cardiovascular effects of daily doses of oral conjugated estrogen lower than 0.625 mg are unknown, and few studies have examined estrogen plus progestin in this regard. To investigate duration, dose, and type of postmenopausal hormone therapy and primary prevention of cardiovascular disease. Prospective, observational cohort study. Nurses' Health Study, with follow-up from 1976 to 1996. 70 533 postmenopausal women, in whom 1258 major coronary events (nonfatal myocardial infarction or fatal coronary disease) and 767 strokes were identified. Details of postmenopausal hormone use were ascertained by using biennial questionnaires. Cardiovascular disease was established by using a questionnaire and was confirmed by medical record review. Logistic regression models were used to calculate relative risks and 95% CIs, adjusted for confounders. When all cardiovascular risk factors were considered, the risk for major coronary events was lower among current users of hormone therapy, including short-term users, compared with never-users (relative risk, 0.61 [95% CI, 0.52 to 0.71]). Among women taking oral conjugated estrogen, the risk for coronary events was similarly reduced in those currently taking 0.625 mg daily (relative risk, 0.54 [CI, 0.44 to 0.67]) and those taking 0.3 mg daily (relative risk, 0.58 [CI, 0. 37 to 0.92]) compared with never-users. However, the risk for stroke was statistically significantly increased among women taking 0.625 mg or more of oral conjugated estrogen daily (relative risk, 1.35 [CI, 1.08 to 1.68] for 0.625 mg/d and 1.63 [CI, 1.18 to 2.26] for >/=1.25 mg/d) and those taking estrogen plus progestin (relative risk, 1.45 [CI, 1.10 to 1.92]). Overall, little relation was observed between combination hormone therapy and risk for cardiovascular disease (major coronary heart disease plus stroke) (relative risk, 0.91 [CI, 0.75 to 1.11]). Postmenopausal hormone use appears to decrease risk for major coronary events in women without previous heart disease. Furthermore, 0.3 mg of oral conjugated estrogen daily is associated with a reduction similar to that seen with the standard dose of 0.625 mg. However, estrogen at daily doses of 0.625 mg or greater and in combination with progestin may increase risk for stroke.
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              The mortality of doctors in relation to their smoking habits; a preliminary report.

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                Author and article information

                Contributors
                +1-617-9640977 , KRothman@rti.org
                Journal
                J Gen Intern Med
                J Gen Intern Med
                Journal of General Internal Medicine
                Springer US (Boston )
                0884-8734
                1525-1497
                23 January 2014
                23 January 2014
                July 2014
                : 29
                : 7
                : 1060-1064
                Affiliations
                [ ]Research Triangle Institute, Research Triangle Park, NC USA
                [ ]Boston University School of Public Health, Boston, MA USA
                Article
                2755
                10.1007/s11606-013-2755-z
                4061362
                24452418
                7c1e508f-a589-4c53-afd9-4f2cee0d66ae
                © The Author(s) 2014

                Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

                History
                : 1 November 2013
                : 27 November 2013
                : 18 December 2013
                Categories
                Review
                Custom metadata
                © Society of General Internal Medicine 2014

                Internal medicine
                study design,data interpretation,epidemiologic methods,representativeness,evaluation of interaction,multiple comparisons,percentile boundaries,statistical significance testing

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