Basic methods for sensitivity analysis of biases.
BACKGROUND
Most discussions of statistical methods focus on accounting for measured confounders and random errors in the data-generating process. In observational epidemiology, however, controllable confounding and random error are sometimes only a fraction of the total error, and are rarely if ever the only important source of uncertainty. Potential biases due to unmeasured confounders, classification errors, and selection bias need to be addressed in any thorough discussion of study results.


METHODS
This paper reviews basic methods for examining the sensitivity of study results to biases, with a focus on methods that can be implemented without computer programming.


CONCLUSIONS
Sensitivity analysis is helpful in obtaining a realistic picture of the potential impact of biases.
Greenland
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Department of Epidemiology, UCLA School of Public Health 90095-1772, USA.
D015982:Bias; D016022:Case-Control Studies; D004812:Epidemiologic Methods; D006801:Humans; D008175:Lung Neoplasms / Q000209:etiology / Q000401:mortality*; D009784:Occupational Diseases / Q000209:etiology / Q000401:mortality*; D016273:Occupational Exposure / Q000009:adverse effects*; D016017:Odds Ratio; D015203:Reproducibility of Results; D012116:Resins, Plant / Q000009:adverse effects*; D012307:Risk Factors; D012680:Sensitivity and Specificity
D012116:Resins, Plant