Open AccessEffectiveness of Population-Based Cancer Screening Only Based on Survival Data Considering Latent Lead-Time and Truncation Biases
Author(s) -
Wei-Jung Chang
Publication year - 2018
Publication title -
journal of global oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.002
H-Index - 17
ISSN - 2378-9506
DOI - 10.1200/jgo.18.84800
Subject(s) - truncation (statistics) , hazard ratio , medicine , statistics , population , bayesian probability , survival analysis , parametric statistics , econometrics , proportional hazards model , confidence interval , mathematics , environmental health
Background: When evaluating the effectiveness of population-based screening program (comparison of survival between screen-detected and clinically detected cases), latent lead time and truncation are both inherent biases from screen-detected cases. Following the disease natural history, the time spent in preclinical screen-detectable cases (PCDP) called sojourn time is an important factor to determine these 2 biases, lead-time bias and length bias. The first is the time from diagnosis at screen to hypothetical diagnosis (entering clinical phase) depending on the sojourn time should be subtracted from the overall survival time to achieve a fair comparison. The second is pertaining to oversampling cases with long sojourn time at prevalent screen. The survival benefit is often overestimated without considering these 2 biases. Aim: To unbiasedly estimate the effectiveness of population-based screening in terms of hazard ratio between screen-detected and clinically detected cases. Methods: Walter and Stitt (1985) has already proposed a parametric approach to adjust lead-time bias. However, lead-time bias and truncation biases are correlated as both have shared information on sojourn time in common, and therefore we aim to consider these 2 biases at the same time. Both parametric and semiparametric approaches are proposed to address the hazard ratio of risk of cancer death adjusting for lead-time and truncation biases based on data from the Swedish W-county trial. The estimation of parameters computer algorithms was also adopted with non-Bayesian and Bayesian approaches using SAS and R. Results: By using parametric approach, the hazard ratio was inflated from 0.26 (0.18-0.36) without adjustment to 0.38 (0.25-0.55) with lead-time adjustment and 0.43 (0.27-0.60) with both lead-time and truncation adjustments. The use of semiparametric model led to 0.59 (0.49-0.70) of adjusted hazard ratio. After considering tumor attributes, the hazard ratio was inflated to 0.76 (0.45-1.11) and 0.87 (0.71-1.04) separately under parametric and semiparametric approaches. Conclusion: Unbiased evaluation of effectiveness of population-based screen with adjustment for lead-time and truncation biases based on survival data are efficient and feasible.