Mortality of Veteran Participants in the CROSSROADS Nuclear Test (1996) / Chapter Skim
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Epidemiology Primer
Epidemiology Primer
Pages 11-20
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From page 11... ...
(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.
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From page 12... ...
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 exposure, and use of national statistics.
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From page 13... ...
Using a military comparison group addresses and avoids the healthy soldier effect but does carry other drawbacks. While government and other groups routinely gather statistics (including demographic, health, and employment descriptors)
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From page 14... ...
In this study we pursued data from actual dosimetry measurements made at the time of the nuclear tests, recalculations done to address the known incompleteness of those measures, self-reports of participants, and coherent assumptions based on knowledge of radiation physics, troop logistics, on-site reportage, logs, and documents as well as logic. CONFOUNDERS It will come as no surprise that some characteristics such as age and sexare associated with numerous measures of health status.
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From page 15... ...
To minimize possible biases, a number of general rules and protocols have evolved to guide researchers- regardless of participant or comparison group and regardless of likely outcome. These protocols include developing an understanding of all data sources and how they may be expected to affect data distributions and establishing clear decision rules.
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From page 16... ...
For example, when examining constructs such as "average age at death," one should account for the amount of time available for observations since the average will change over time as larger proportions of the sample die. For example, let's follow the mortality experience of a hypothetical sixth-grade class of 25 students in 1923.
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From page 17... ...
it Is customary to calculate two limits, caned 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. The interval between the upper and lower confidence limits is thus called a 95 percent confidence interval.
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From page 18... ...
By custom, a value is called statistically significant if the operation of chance will produce such a value only about 5 times in 100. However, just as in the case of repeated samples, repeated analyses of different data (for example, death rates due to cancer, to heart disease, to respiratory disease, etc.)
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From page 19... ...
The endpoints considered are mortality rates. Specific causes of death were chosen based on understanding of disease process and a priori expectations based on knowledge and suspicion of radiation effects.
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From page 20... ...
MFUA is itself"disinterested" in that it stands to neither lose nor gain from its findings in this study: it will neither receive nor be denied compensation, nor will it be held fiscally or programmatically responsible for such compensation or related care. Because this study (not unlike many other studies of human suffering and possible blame and responsibility)
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