Several measures have been used to judge the effectiveness of screening. As Goldie and Kuntz discussed, biologic characteristics of cancers and their precursor lesions may affect test efficacy. In addition, we make assumptions about the heterogeneity of risk factors in screened and unscreened populations; measures of effectiveness tend to minimize true differences within population. The most definitive measure of efficacy and least subject to bias is breast cancer mortality as determined by the comparison of screened and unscreened groups in a randomized clinical trial (RCT). Other outcome measures have been proposed, including case finding and survival. Each has limitations.
Another potential outcome is improved quality of life. Unfortunately, few trials have collected such data. These data are being collected as part of the NCI’s Prostate, Lung, Colorectal, Ovarian (PLCO) cancer screening trial and the newer NCI-funded trial to compare special computed tomography (CT) for lung cance3r screening versus chest x-rays.
Increasingly, reports include the number needed to treat to convey the size of the population that do and do not benefit. The number is usually very small for cancer screening-often in the range of five or fewer people benefiting per 5000 people screened.
In assessing effectiveness of screening technologies, the RCT has been the gold standard. It is the most powerful methodology for demonstrating the value of screening in comparison to an unscreened group. RCTs minimize biases inherent in other designs; especially lead time, length bias, selection bias, and over diagnosis. Case-control studies also can provide useful information, and at less cost than RCTs. In addition, increasingly sophisticated statistical modeling techniques may be helpful in assessing the impact of screening, especially in situations in which large RCTs cannot be conducted. The U.S. Preventive Services Task Force PD! And others have rated levels of evidence; the RCT is uniformly regarded as the highest level of evidence.
Another potential outcome is improved quality of life. Unfortunately, few trials have collected such data. These data are being collected as part of the NCI’s Prostate, Lung, Colorectal, Ovarian (PLCO) cancer screening trial and the newer NCI-funded trial to compare special computed tomography (CT) for lung cance3r screening versus chest x-rays.
Increasingly, reports include the number needed to treat to convey the size of the population that do and do not benefit. The number is usually very small for cancer screening-often in the range of five or fewer people benefiting per 5000 people screened.
In assessing effectiveness of screening technologies, the RCT has been the gold standard. It is the most powerful methodology for demonstrating the value of screening in comparison to an unscreened group. RCTs minimize biases inherent in other designs; especially lead time, length bias, selection bias, and over diagnosis. Case-control studies also can provide useful information, and at less cost than RCTs. In addition, increasingly sophisticated statistical modeling techniques may be helpful in assessing the impact of screening, especially in situations in which large RCTs cannot be conducted. The U.S. Preventive Services Task Force PD! And others have rated levels of evidence; the RCT is uniformly regarded as the highest level of evidence.
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