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Smoller Jordan W. - Biostatistics and epidemiology: a primer for health and biomedical professionals

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Smoller Jordan W. Biostatistics and epidemiology: a primer for health and biomedical professionals

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Since the publication of the first edition, Biostatistics and Epidemiology has attracted loyal readers from across specialty areas in the biomedical community. Not only does this textbook teach foundations of epidemiological design and statistical methods, but it also includes topics applicable to new areas of research. Areas covered in the fourth edition include a new chapter on risk prediction, risk reclassification and evaluation of biomarkers, new material on propensity analyses, and a vastly expanded chapter on genetic epidemiology, which is particularly relevant to those who wish to understand the epidemiological and statistical aspects of scientific articles in this rapidly advancing field. Biostatistics and Epidemiology was written to be accessible for readers without backgrounds in mathematics. It provides clear explanations of underlying principles, as well as practical guidelines of how to do it and how to interpret it. Key features include a philosophical and logical explanation at the beginning of the book, subsections that can stand alone or serve as reference, cross-referencing, recommended reading, and appendices covering sample calculations for various statistics in the text. [Ed.].;The Scientific Method -- Probability -- Statistics -- Epidemiology.-Screening -- Clinical Trials -- Quality of Life -- Genetic Epidemiology -- Risk Prediction and Reclassification -- Research Ethics and Statistics -- Postscript -- Appendix A -- Appendix B -- Appendix C -- Appendix D -- Appendix E -- Appendix F -- References -- Suggested Readings -- Index.

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Springer Science+Business Media New York 2015
Sylvia Wassertheil-Smoller and Jordan Smoller Biostatistics and Epidemiology 10.1007/978-1-4939-2134-8_1
1. The Scientific Method
Sylvia Wassertheil-Smoller 1 and Jordan Smoller 2
(1)
Department of Epidemiology, Albert Einstein College of Medicine, Bronx, NY, USA
(2)
Department of Psychiatry and Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
Science is built up with facts, as a house is with stones. But a collection of facts is no more a science than a heap of stones is a house.
Jules Henri Poincare
La Science et lHypothese (1908)
1.1 The Logic of Scientific Reasoning
The whole point of science is to uncover the truth. How do we go about deciding something is true? We have two tools at our disposal to pursue scientific inquiry:
We have our senses, through which we experience the world and make observations .
We have the ability to reason, which enables us to make logical inferences .
In science we impose logic on those observations.
Clearly, we need both tools. All the logic in the world is not going to create an observation, and all the individual observations in the world wont in themselves create a theory. There are two kinds of relationships between the scientific mind and the world and two kinds of logic we impose deductive and inductive as illustrated in Figure .
Figure 11 Deductive and inductive inference In deductive inference we - photo 1
Figure 1.1
Deductive and inductive inference
In deductive inference , we hold a theory, and based on it, we make a prediction of its consequences. That is, we predict what the observations should be. For example, we may hold a theory of learning that says that positive reinforcement results in better learning than does punishment, that is, rewards work better than punishments. From this theory, we predict that math students who are praised for their right answers during the year will do better on the final exam than those who are punished for their wrong answers. We go from the general, the theory, to the specific, the observations. This is known as the hypothetico-deductive method.
In inductive inference, we go from the specific to the general. We make many observations, discern a pattern, make a generalization, and infer an explanation. For example, it was observed in the Vienna General Hospital in the 1840s that women giving birth were dying at a high rate of puerperal fever, a generalization that provoked terror in prospective mothers. It was a young doctor named Ignaz Phillip Semmelweis who connected the observation that medical students performing vaginal examinations did so directly after coming from the dissecting room, rarely washing their hands in between, with the observation that a colleague who accidentally cut his finger while dissecting a corpse died of a malady exactly like the one killing the mothers. He inferred the explanation that the cause of death was the introduction of cadaverous material into a wound. The practical consequence of that creative leap of the imagination was the elimination of puerperal fever as a scourge of childbirth by requiring that physicians wash their hands before doing a delivery! The ability to make such creative leaps from generalizations is the product of creative scientific minds.
Epidemiologists have generally been thought to use inductive inference. For example, several decades ago, it was noted that women seemed to get heart attacks about 10 years later than men did. A creative leap of the imagination led to the inference that it was womens hormones that protected them until menopause. EUREKA! They deduced that if estrogen was good for women, it must be good for men and predicted that the observations would corroborate that deduction. A clinical trial was undertaken which gave men at high risk of heart attack estrogen in rather large doses, 2.5 mg per day or about four times the dosage currently used in postmenopausal women. Unsurprisingly, the men did not appreciate the side effects, but surprisingly to the investigators, the men in the estrogen group had higher coronary heart disease rates and mortality than those on placebo.2 What was good for the goose might not be so good for the gander. The trial was discontinued, and estrogen as a preventive measure was abandoned for several decades.
During that course of time, many prospective observational studies indicated that estrogen replacement given to postmenopausal women reduced the risk of heart disease by 3050 %. These observations led to the inductive inference that postmenopausal hormone replacement is protective, i.e., observations led to theory. However, that theory must be tested in clinical trials. The first such trial of hormone replacement in women who already had heart disease, the Heart and Estrogen/progestin Replacement Study (HERS), found no difference in heart disease rates between the active treatment group and the placebo group, but did find an early increase in heart disease events in the first year of the study and a later benefit of hormones after about 2 years. Since this was a study in women with established heart disease, it was a secondary prevention trial and does not answer the question of whether women without known heart disease would benefit from long-term hormone replacement. That question has been addressed by the Womens Health Initiative (WHI), which is described in a later section.
The point of the example is to illustrate how observations (that women get heart disease later than men) lead to theory (that hormones are protective), which predicts new observations (that there will be fewer heart attacks and deaths among those on hormones), which may strengthen the theory, until it is tested in a clinical trial which can either corroborate it or overthrow it and lead to a new theory, which then must be further tested to see if it better predicts new observations. So there is a constant interplay between inductive inference (based on observations) and deductive inference (based on theory), until we get closer and closer to the truth.
However, there is another point to this story. Theories dont just leap out of facts. There must be some substrate out of which the theory leaps. Perhaps that substrate is another preceding theory that was found to be inadequate to explain these new observations and that theory, in turn, had replaced some previous theory. In any case, one aspect of the substrate is the prepared mind of the investigator. If the investigator is a cardiologist, for instance, he or she is trained to look at medical phenomena from a cardiology perspective and is knowledgeable about preceding theories and their strengths and flaws. If the cardiologist hadnt had such training, he or she might not have seen the connection. Or, with different training, the investigator might leap to a different inference altogether. The epidemiologist must work in an interdisciplinary team to bring to bear various perspectives on a problem and to enlist minds prepared in different ways.
The question is, how well does a theory hold up in the face of new observations? When many studies provide affirmative evidence in favor of a theory, does that increase our belief in it? Affirmative evidence means more examples that are consistent with the theory. But to what degree does supportive evidence strengthen an assertion? Those who believe induction is the appropriate logic of science hold the view that affirmative evidence is what strengthens a theory.
Another approach is that of Karl Popper,1 perhaps one of the foremost theoreticians of science. Popper claims that induction arising from accumulation of affirmative evidence doesnt strengthen a theory. Induction, after all, is based on our belief that the things unobserved will be like those observed or that the future will be like the past. For example, we see a lot of white swans and we make the assertion that all swans are white. This assertion is supported by many observations. Each time we see another white swan, we have more supportive evidence. But we cannot prove that all swans are white no matter how many white swans we see.
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