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Appendix C: Epidemiology Primer
Pages 159-167

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From page 159...
... (Note: We have not proven that X causes Y; we have shown that in this sample X and Y occur together more often than we would have expected them to by chance.) What, however, takes scores of technical textbooks and fuels ongoing debates are the "how to" and "what if," "buts," "on the other hands," and "howevers" that make all the difference between error-laden, error-tinged, and accurate study results.
From page 160...
... For example, because age and sex are associated with health risks and conditions, data regarding age and sex are collected, making it possible in the analysis to either compare like age distributions and sexes or statistically adjust the data to account for known differences. CHOICE OF COMPARISON GROUP In studying CROSSROADS participants, comparison group options include the development of a specific control group, internal comparisons by level of ex posure, and use of national statistics.
From page 161...
... The current CROSSROADS study is structured around a military comparison group, chosen to match on age, rank, time period, and military occupation-all available characteristics but specifically not CROSSROADS test participants. Secondarily, we included statistical comparisons with the general U.S.
From page 162...
... In specific studies investigators may hypothesize potential confounders such as ethnicity; military service-related exposures, including sunlight, altitude, and preventive and therapeutic attention to infectious disease, as well as the diseases themselves; and other risks based on lifestyle, geography, and postmilitary careers.
From page 163...
... ; medical record technologies change; whether patients or family members have concerns about benefits or suspicions of causation could influence whether they notify the recordkeeping agency; data may be missing due to circumstances beyond human control, such as a fire destroying paper files; and data accuracy is associated with level of ascertainment, such as completeness of fact-of-death, date-of-death, or cause-of-death information.
From page 164...
... The decisions made should be definable. Researchers should examine according to biologic, logistical, and cost implicationschoices involving latency periods, cohort age, or pending compensation questions.
From page 165...
... In general, it turns out that the larger the sample, the smaller the variability. It is customary to calculate two limits, called the lower and upper 95 percent confidence limits, that have the property that if we repeatedly drew samples and recalculated the statistic, these different values would lie between the upper and lower confidence limits 95 times out of 100.
From page 166...
... Customarily, a p value of less than 0.05 is considered "unusual." For example, take the above null hypotheses of no difference between mortality rates in groups A and B.; that is, the rate ratio is 1.0. If observed data yield an actual rate ratio of 1.5, for instance, and an associated test statistic with a p-value less than 0.05, then we reject the null hypothesis and conclude that such a high risk ratio is unlikely (only 5 times out of 100)
From page 167...
... Because this study (not unlike many other studies of human suffering and possible blame and responsibility) has an historical overlay of tremendous emotion and distrust, we must be especially careful to follow generally accepted ground rules for valid studies and to describe openly our rationale for various decisions throughout.


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