Length-time bias is the most likely explanation for the positive outcome of the study. The apparent benefits of early diagnosis and intervention on screen-detected cases compared with cases detected by disease symptoms and signs are almost always more favorable than the true effects seen when the outcomes of an overall screened population are compared with an overall population that is not screened for the disease. This difference is caused by several types of bias that tend to occur when examining the effectiveness of screening studies, one of which is length-time bias. Because indolent disease has a longer latent period than more aggressive forms of disease (which are more likely to be detected with onset of symptoms), indolent disease is more likely to be detected by screening. Length-time bias occurs when there is overrepresentation of indolent (low-grade) disease in the screen-detected cohort and overrepresentation of aggressive disease in the symptom-detected (non-screened) cohort, as was the case in this hypothetical study. This makes the screen-detected cohort, with more indolent disease, falsely appear to have a better prognosis than the patients who present with symptoms and signs in the non-screened cohort. A drastic type of length-time bias is termed overdiagnosis and occurs when a disease that is so indolent that it would not otherwise have been clinically significant during a patient's lifespan is detected through screening.

Contamination bias occurs when the control group is unintentionally exposed to the intervention, which biases the estimate toward the null hypothesis. Contamination bias was unlikely in this case as there was little crossover between groups.

Observer bias occurs when knowledge of the hypothesis or intervention received influences data recording, which would not be expected to be an influence in this study in which the researchers were blinded.

Selection bias refers to systematic error in a study resulting from the manner in which the subjects are selected for the study. It can influence the results when the characteristics of the subjects selected for a study differ systematically from those in the target population or when the study and comparison groups are selected from different populations. An example is volunteer bias, in which patients who seek participation in a screening study are often healthier than those who do not undergo screening. Because the patients in this study were randomly selected from the general population, this would not be a likely cause of significant bias.