Background We present probabilistic and Bayesian techniques to correct for bias in categorical and numerical measures and empirically apply them to a recent survey of female sex workers (FSW) conducted in Iran.

Methods We used bias parameters from a previous validation study to correct estimates of behaviours reported by FSW. Monte-Carlo Sensitivity Analysis and Bayesian bias analysis produced point and simulation intervals (SI).

Results The apparent and corrected prevalence differed by a minimum of 1% for the number of ‘non-condom use sexual acts’ (36.8% vs 35.8%) to a maximum of 33% for ‘ever associated with a venue to sell sex’ (35.5% vs 68.0%). The negative predictive value of the questionnaire for ‘history of STI’ and ‘ever associated with a venue to sell sex’ was 36.3% (95% SI 4.2% to 69.1%) and 46.9% (95% SI 6.3% to 79.1%), respectively. Bias-adjusted numerical measures of behaviours increased by 0.1 year for ‘age at first sex act for money’ to 1.5 for ‘number of sexual contacts in last 7 days’.

Conclusions The ‘true’ estimates of most behaviours are considerably higher than those reported and the related SIs are wider than conventional CIs. Our analysis indicates the need for and applicability of bias analysis in surveys, particularly in stigmatised settings.