@article{Orsini:120927,
      recid = {120927},
      author = {Orsini, Nicola and Bellocco, Rino and Bottai, Matteo and  Wolk, Alicja and Greenland, Sander},
      title = {A tool for deterministic and probabilistic sensitivity  analysis of epidemiologic studies},
      journal = {Stata Journal},
      address = {2008},
      number = {199-2016-2510},
      pages = {20},
      year = {2008},
      abstract = {Classification errors, selection bias, and uncontrolled  confounders are likely to be present in most epidemiologic  studies, but the uncertainty introduced by these types of  biases is seldom quantified. The authors present a simple  yet easy-to-use Stata command to adjust the relative risk  for exposure misclassification, selection bias, and an  unmeasured confounder. This command implements both  deterministic and probabilistic sensitivity analysis. It  allows the user to specify a variety of probability  distributions for the bias parameters, which are used to  simulate distributions for the bias-adjusted  exposure–disease relative risk. We illustrate the command  by applying it to a case–control study of occupational  resin exposure and lung-cancer deaths. By using plausible  probability distributions for the bias parameters,  investigators can report results that incorporate their  uncertainties regarding systematic errors and thus avoid  overstating their certainty about the effect under study.  These results can supplement conventional results and can  help pinpoint major sources of conflict in study  interpretations.},
      url = {http://ageconsearch.umn.edu/record/120927},
      doi = {https://doi.org/10.22004/ag.econ.120927},
}