Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Stat Methods Med Res. 2020 Feb;29(2):374-395. doi: 10.1177/0962280219832901. Epub 2019 Mar 10.
Comparisons of survival times between screen-detected and symptomatically detected breast cancer cases are subject to lead time and length biases. Whilst the existence of these biases is well known, correction procedures for these are not always clear, as are not the interpretation of these biases. In this paper we derive, based on a recently developed continuous tumour growth model, conditional lead time distributions, using information on each individual's tumour size, screening history and percent mammographic density. We show how these distributions can be used to obtain an individual-based (conditional) procedure for correcting survival comparisons. In stratified analyses, our correction procedure works markedly better than a previously used unconditional lead time correction, based on multi-state Markov modelling. In a study of postmenopausal invasive breast cancer patients, we estimate that, in large (>12 mm) tumours, the multi-state Markov model correction over-corrects five-year survival by 2-3 percentage points. The traditional view of length bias is that tumours being present in a woman's breast for a long time, due to being slow-growing, have a greater chance of being screen-detected. This gives a survival advantage for screening cases which is not due to the earlier detection by screening. We use simulated data to share the new insight that, not only the tumour growth rate but also the symptomatic tumour size will affect the sampling procedure, and thus be a part of the length bias through any link between tumour size and survival. We explain how this has a bearing on how observable breast cancer-specific survival curves should be interpreted. We also propose an approach for correcting survival comparisons for the length bias.
筛查检出和症状检出乳腺癌病例的生存时间比较受到领先时间和长度偏倚的影响。虽然这些偏差的存在是众所周知的，但对于这些偏差的校正程序并不总是清楚的，对于这些偏差的解释也不是。在本文中，我们基于最近开发的连续肿瘤生长模型，利用个体的肿瘤大小、筛查史和乳腺密度百分比的信息，推导出条件性领先时间分布。我们展示了如何使用这些分布来获得基于个体的（条件）生存比较校正程序。在分层分析中，我们的校正程序明显优于以前基于多状态马尔可夫模型的无条件领先时间校正。在一项绝经后浸润性乳腺癌患者的研究中，我们估计，在大（>12 毫米）肿瘤中，多状态马尔可夫模型校正会使五年生存率过度校正 2-3 个百分点。关于长度偏倚的传统观点是，由于肿瘤生长缓慢，在女性乳房中存在较长时间的肿瘤有更大的机会被筛查检出。这为筛查病例提供了生存优势，而这种优势不是由于筛查更早地发现了肿瘤。我们使用模拟数据来分享一个新的观点，即不仅肿瘤生长速度，而且症状性肿瘤大小也会影响采样过程，并且通过肿瘤大小与生存之间的任何联系，成为长度偏倚的一部分。我们解释了这对如何解释可观察的乳腺癌特异性生存曲线有何影响。我们还提出了一种校正生存比较的长度偏倚的方法。
