Table 3.

Critical Evaluation of the Quality and Limitations of the Decision-Analytic Models Evaluating Benefits and Harms of Screening Mammography According to Comorbidity

Source	Were important strategies included?	Was the potential impact of uncertainty in the evidence determined?	How strong is the evidence?	Do the probabilities fit the U.S. population?	Do the utilities* reflect the values of older women in the U.S.?
Mandelblatt, 1992	Yes - compared screening for women ≥65 years with no screening	Conducted sensitivity analyses by varying quality of life, breast cancer incidence rates, perioperative death rate, sensitivity and specificity of mammography test, stage distribution of detected breast cancer	The evidence is strong, as the model assumes U.S. breast cancer stage distribution and stage-specific survival data	All measures used in models were based on U.S. population estimates	Yes
Messecar, 2000	Yes - compared 1 mammography screening in women ≥75 years with and without cognitive impairment, who (a) underwent regular screening, or (b) had no prior screening	Conducted sensitivity analyses by varying prior probabilities, quality of life, costs of recurrence, sensitivity and specificity of mammography test	The evidence is strong, as the model assumes U.S. breast cancer stage distribution and stage-specific survival data	All measures used in models were based on U.S. population estimates	Yes
Lansdorp-Vogelaar, 2014	Yes - compared biennial mammography screening from age 50 to a range of cessation ages from 66 to 90	Assessed the robustness of choice of metric by considering other harms (false-positive tests, over-diagnosed cancers) and benefits (cancer deaths prevented). Also varied method of extrapolating comorbidity-specific life tables	The evidence is strong, as the models assume U.S. breast cancer stage distribution and stage-specific survival data	All measures used in models were based on U.S. population estimates	Yes

* Weights used to adjust life expectancy gains for impact on quality of life