A new biomarker has been shown to allow for the early detection of non-small cell lung carcinoma. A preliminary analysis on a cohort study of this new test demonstrates that its use prolongs survival of lung cancer patients by 3 months when compared to the survival of patients diagnosed by conventional methods. A secondary analysis reveals no difference in 6-month mortality rates between the 2 groups. Which of the following factors most likely explains the study results?

Question

A new biomarker has been shown to allow for the early detection of non-small cell lung carcinoma.  A preliminary analysis on a cohort study of this new test demonstrates that its use prolongs survival of lung cancer patients by 3 months when compared to the survival of patients diagnosed by conventional methods.  A secondary analysis reveals no difference in 6-month mortality rates between the 2 groups.  Which of the following factors most likely explains the study results?

Quizplus · Accepted Answer

11ec160b_36fb_1a6b_9249_e53505540b96_MD0001_00 Lead-time bias should always be considered when evaluating any screening test. Lead-time bias is an apparent increase in survival time among patients undergoing screening when they actually have an unchanged prognosis. Patients screened with more sensitive tests may appear to live longer only because the disease was detected earlier than had it been diagnosed clinically. The overall length of time from disease onset to death actually remains the same with or without the earlier screening. To determine the actual effectiveness of the screening program, it is necessary to follow up patients for periods that are longer than the apparent increase in survival time, and then to estimate and compare mortality rates among patients who have undergone the additional screening and those who have not. In this case, patients screened by the new test appeared to have increased their survival time by 3 months when compared to those diagnosed by conventional methods. However, there was no difference in 6-month mortality rate between the 2 groups, which indicates that there is no actual benefit to the screening program. (Choice A) Confounding distorts the relationship between risk factors and the disease of interest, and can wholly or partially account for the observed effect. Although the results of this study could be potentially confounded, there is no information on how potential confounders were treated during the design or analysis stage of the study. (Choice C) Length-time bias occurs when subjects with a rapidly progressive form of disease are less likely to be detected by screening compared to those with slowly progressive disease. This bias tends to overstate the beneficial effects of screening on length of survival and mortality. In this case, there is no indication that the patients identified by the new test have more benign forms of lung cancer. (Choices D and E) Measurement bias and observer bias refer to the misclassification of outcome and/or exposure (eg, labeling diseased as non-diseased and vice versa) and are related to poor study design. The scenario described does not mention how the study was conducted. (Choice F) Rare disease assumption refers to the practice of approximating the odds ratio and relative risk when conducting a case-control study for rare diseases. According to the assumption, the odds ratio approximates relative risk when disease incidence is low (eg, <10%). Educational objective: Lead-time bias occurs when a new test diagnoses a condition earlier than conventional studies, causing an apparent increase in survival time despite no improvement in overall mortality. Long-term mortality rates, not survival times, should be considered for measuring the effect of early screening and treatment.

A New Biomarker Has Been Shown to Allow for the Early