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- Abrahamsson, L, et al.
(författare)
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Continuous tumour growth models, lead time estimation and length bias in breast cancer screening studies
- 2020
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Ingår i: Statistical methods in medical research. - : SAGE Publications. - 1477-0334 .- 0962-2802. ; 29:2, s. 374-395
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
- Isheden, G, et al.
(författare)
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Joint models of tumour size and lymph node spread for incident breast cancer cases in the presence of screening
- 2019
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Ingår i: Statistical methods in medical research. - : SAGE Publications. - 1477-0334 .- 0962-2802. ; 28:12, s. 3822-3842
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Tidskriftsartikel (refereegranskat)abstract
- Continuous growth models show great potential for analysing cancer screening data. We recently described such a model for studying breast cancer tumour growth based on modelling tumour size at diagnosis, as a function of screening history, detection mode, and relevant patient characteristics. In this article, we describe how the approach can be extended to jointly model tumour size and number of lymph node metastases at diagnosis. We propose a new class of lymph node spread models which are biologically motivated and describe how they can be extended to incorporate random effects to allow for heterogeneity in underlying rates of spread. Our final model provides a dramatically better fit to empirical data on 1860 incident breast cancer cases than models in current use. We validate our lymph node spread model on an independent data set consisting of 3961 women diagnosed with invasive breast cancer.
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| 8. |
- Isheden, G, et al.
(författare)
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Modelling breast cancer tumour growth for a stable disease population
- 2019
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Ingår i: Statistical methods in medical research. - : SAGE Publications. - 1477-0334 .- 0962-2802. ; 28:3, s. 681-702
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Tidskriftsartikel (refereegranskat)abstract
- Statistical models of breast cancer tumour progression have been used to further our knowledge of the natural history of breast cancer, to evaluate mammography screening in terms of mortality, to estimate overdiagnosis, and to estimate the impact of lead-time bias when comparing survival times between screen detected cancers and cancers found outside of screening programs. Multi-state Markov models have been widely used, but several research groups have proposed other modelling frameworks based on specifying an underlying biological continuous tumour growth process. These continuous models offer some advantages over multi-state models and have been used, for example, to quantify screening sensitivity in terms of mammographic density, and to quantify the effect of body size covariates on tumour growth and time to symptomatic detection. As of yet, however, the continuous tumour growth models are not sufficiently developed and require extensive computing to obtain parameter estimates. In this article, we provide a detailed description of the underlying assumptions of the continuous tumour growth model, derive new theoretical results for the model, and show how these results may help the development of this modelling framework. In illustrating the approach, we develop a model for mammography screening sensitivity, using a sample of 1901 post-menopausal women diagnosed with invasive breast cancer.
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| 10. |
- Isheden, G., et al.
(författare)
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SWEDISH NATIONWIDE REGISTER DATA AS A LOW-COST RESOURCE TO DETECT DRUG-REPURPOSING SIGNALS : A STUDY ON DE NOVO METASTATIC BREAST CANCER PATIENTS
- 2022
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Ingår i: Value in Health. - : Elsevier. - 1098-3015 .- 1524-4733. ; 25:12 Suppl., s. S375-S375
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Objectives: Electronic health records have recently been highlighted as a low-cost resource to accelerate cancer therapeutics by drug repurposing discovery (Wu et al., JCO Clinical Cancer Informatics 2019:3, 1-9). The aim of this study was to test this approach on Swedish nationwide register data focusing on breast cancer cases with distant metastasis at initial diagnosis (de novo mBC). To demonstrate the feasibility of this methodology we i) evaluated the nine drug candidates identified by Wu et al. on our dataset, ii) generated drug repurposing hypotheses based on prescription drugs given to patients during metastatic breast cancer diagnosis/treatment.Methods: Patients diagnosed with de novo mBC between 2010 and 2020 were identified in the Swedish Cancer Register. Data on prescription drug use was collected from the National Prescribed Drug Register and survival data was collected from the National Cause of Death Register. Based on a 6-month window from diagnosis, drug repurposing candidates were evaluated using Cox proportional hazards models.Results: A total of 2,106 de novo mBC patients were included. The nine drug candidates found by Wu et al. (Rosuvastatin, Simvastatin, Amlodipine, Tamsulosin, Metformin, Omeprazole, Warfarin, Lisinoprol and Metroprolol) were not found significant in our data. However, a total of seven other drug repurposing hy-potheses were generated, with a plausible biological rationale for at least five of them (Calcium + Vitamin D, Morphine, Furosemide, Salbutamol and Ipratropium bromide, and Fentanyl). The other two were vaginal gel and Fluoride mouthwash.Conclusions: This study shows that the Swedish National Health Data Registers may be leveraged as a low-cost data source to detect drug repurposing signals. While results need to be interpreted with caution to not confuse causal relationships, the hypotheses generated in our study show a model for discovering noncancer drug effects on overall survival.
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