Basic methods for sensitivity analysis of biases
S Greenland - International journal of epidemiology, 1996 - academic.oup.com
International journal of epidemiology, 1996•academic.oup.com
Abstract Greenland S (Department of Epidemiology, UCLA School of Public Health, Los
Angeles, CA 90095-1772, USA). Basic methods for sensitivity analysis of biases.
International Journal of Epidemiology 1996; 25: 1107–1116 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 …
Angeles, CA 90095-1772, USA). Basic methods for sensitivity analysis of biases.
International Journal of Epidemiology 1996; 25: 1107–1116 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 …
Abstract
Greenland S (Department of Epidemiology, UCLA School of Public Health, Los Angeles, CA 90095-1772, USA). Basic methods for sensitivity analysis of biases. International Journal of Epidemiology 1996; 25: 1107–1116
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.
Conclusion
Sensitivity analysis is helpful in obtaining a realistic picture of the potential impact of biases.
Oxford University Press