Epidemiology and Air Pollution (1985) / Chapter Skim
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APPENDIX C: PITFALLS IN DESIGN, ANALYSIS, AND INTERPRETATION
APPENDIX C: PITFALLS IN DESIGN, ANALYSIS, AND INTERPRETATION
Pages 205-224
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From page 205... ...
BIAS Bias is the consequence of any technique used in a study that produces results that tend systematically to be on one side or the other of "true" values. It is sometimes referred to as systematic or nonrandom error.
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From page 206... ...
In air pollution studies, where true relative risks tend to be rather low, a relative risk of 2 could disappear. It is clearly preferable to eliminate or reduce bias during the design phase, but, with proper planning, it can sometimes be estimated (even after data collection)
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From page 207... ...
For example, a study of lung cancer in nonsmokers or a study of lung function in young children would completely avoid confounding by cigarette smoking; a study conducted in a nondeveloped country might avoid biases due to air conditioning. Second, matching or stratification can be used during the design phase to make the prevalence of a confounding variable similar among groups to be compared.
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From page 208... ...
, random error in measuring exposure or effects, and random error of inference regarding overall study results. Some random error in measuring exposure, effect, or a confounding variable is unavoidable, even with the most precise instruments.
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From page 209... ...
By definition, exposure to ubiquitous air pollutants is widespread, and the size of the exposed population is rarely a major constraint. Matters of power and sample size should be explicitly addressed during the design phase, both to make it possible to terminate planning of work that has little likelihood of success and to ensure that negative findings, if they occur, will be properly interpreted.
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From page 210... ...
Therefore, one has to observe a very large population, if one is to detect an impact of air pollution on the number of these events within a reasonable period. The Ontario Air Pollution Study, for example, detected a daily excess of 22 asthma admissions due to air pollution among nearly 6 million exposed and observed people.1 The large population studied provided the needed statistical power, but also dictated the use of aggregate, rather than individual, exposure data.
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From page 211... ...
Although the unnecessary aggregation of data is undesirable, aggregate data are in some ways the preferred type of epidemiologic data for prevention and "community diagnosis." Aggregate data on indexes of respiratory health allow us to compare the health of one community with that of another and to assess the community-wide impact of preventive strategies. Moreover, ambient air pollution is regulated on the basis of aggregate data on exposure.
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From page 212... ...
Small sample size for this group component of variation can increase the uncertainty attached to a study's results and limit generalization from them. For that reason, a recent large epidemiologic study of air pollution in France used aggregate exposure data for study subjects scattered throughout 28 towns or districts.3 SELECTION OF STUDY POPULATION AND ROLE OF SENSITIVE POPULATIONS Study subjects are ordinarily selected because they are exposed to air pollution of different magnitude or because they do or do not have some outcome of interest.
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From page 213... ...
Asthmatics, bronchitics, children, the elderly, and subjects with cardiopulmonary diseases have long been considered to be sensitive to air pollutants. As Chapter 2 pointed out, the biologic nature of this presumed hypersusceptibility is not well understood, and much more information from all types of studies is needed for the characterization of sensitive populations.
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From page 214... ...
MULTIPLE ANALYSES Multiple tests and large data volume in air pollution studies also entail multiple statistical procedures and drawing of multiple conclusions. Two serious and related problems in data analysis and interpretation are multiple *
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By specifying in advance the major hypotheses that they wish to test, investigators can greatly add to the confidence in their positive results. The purpose of the foregoing discussion is not to discourage the thorough exploration of large data sets, nor the reanalysis of old ones, but rather to point out to those concerned with air pollution epidemiology that multiple analyses of large data sets carry certain perils
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From page 216... ...
Collinearity -- the correlation of important variables with each other -- leads to other problems. The presence of large correlations between variables lessens the opportunity to attribute effects to any particular predictor variable, including exposure to specific air pollutants.
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From page 217... ...
COMBINING RESULTS FROM INDEPENDENT STUDIES Epidemiologists have a special interest in the combination of results from independently conducted studies. Results of individual studies are not likely to be definitive on a particular research question and require interpretation within the context of similar work.
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From page 218... ...
That assumption might be reasonable for experiments repeated under very similar conditions, but it is rarely so for epidemiologic studies, in which extraneous factors are harder to control and nonrandom errors dominate the random ones. Weighting studies according to such features as data quality can also be treacherous.
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From page 219... ...
Previous methods, most commonly used in asthma studies, had relied on the panel attack rate as the outcome variable and had constructed models to predict this rate. The panel attack rate is defined as the number of panelists reporting an attack on a given day divided by the total number of panelists reporting.
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From page 220... ...
findings in either observational or experimental research are therefore statistical associations, which can always be explained in any of three ways: they reflect causality, they reflect a noncausal relation, or they reflect chance. Epidemiologic reasoning does not stop at the demonstration of a statistical association, but goes on to "the practical purpose of discovering (causal)
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From page 221... ...
Over the last 25 years, epidemiologists have developed a set of guidelines for judging whether statistical associations derived from observational studies truly reflect causality. These guidelines, which apply equally to inference from a single study and to inference from a set of similar studies, are as follows: • Strength (magnitude)
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From page 222... ...
The final step in the inferential process in epidemiology requires the extension of a study's results to persons, populations, or settings not specifically included in the study. The confidence with which this is done for positive results is usually based implicitly on how successful the investigators have been in identifying and handling the factors that produce or influence the pollution-effect association they have observed, including sampling variation.
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From page 223... ...
Inman. The Hamilton study: Distribution of factors confounding the relationship between air quality and respiratory health.
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From page 224... ...
Armstrong. The problem of multiple inference in studies designed to generate hypotheses.
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