Table 2.

Summary of probabilistic bias analysis when simulating unmeasured confounding, sorted by study design.

Study	Unmeasured confounder simulated	Source of bias parameter estimates	Type of probability distribution assigned to bias parameters*	Bias parameters for which assigned range of values were reported†	Simulated iterations
Cohort studies
Albert et al. (2012)30	Use of endocrine therapy (Binary)	Not reported	Not reported	Prevalence of confounder in exposed and unexposed	Not reported
Bannister-Tyrell et al. (2014)31	Severe labor pain intensity (Binary)	Expert judgement (prevalence); prior studies (association)	Trapezoidal	Prevalence of confounder in exposed and unexposed; association between confounder and outcome	10,000
Corrao et al. (2011)32	Depression (Binary)	Prior studies	Not reported	Not reported	Not reported
Corrao et al. (2014)33	Obesity (Binary)	Prior studies	Not reported	Not reported	Not reported
Corrao et al. (2014)34	Severity of hypercholesterolemia (Binary)	Prior study	Not reported	Not reported	Not reported
Case-control studies
Corrao et al. (2011)29	Severity of hypertension; CDS; BMI (Multinomial)	External dataset (prevalence); expert judgement	Normal	Association between confounder and outcome	5,000
Corrao et al. (2013)35	Severe hypercholest- erolemia (Binary)	Prior studies	Not reported	Not reported	Not reported
Ghirardi et al. (2014)36	Severity of osteoporosis (Binary)	Not reported	Not reported	Not reported	Not reported
Schmidt et al. (2010)37	Smoking (Binary)	Prior studies	Not reported	Association between confounder and outcome	Not reported

^*See eFigure 1 in the online supplementary appendix for further description of probability distribution types.

^†Bias parameters for unmeasured confounding include confounder prevalence in exposed and unexposed and strength of association between the confounder and outcome. We report whether investigators discussed the range of plausible values assigned to bias parameters