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Lee M, Gail MH. Absolute risk from double nested case-control designs: cause-specific proportional hazards models with and without augmented estimating equations. Biometrics 2024; 80:ujae062. [PMID: 38994640 DOI: 10.1093/biomtc/ujae062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 06/01/2024] [Accepted: 06/21/2024] [Indexed: 07/13/2024]
Abstract
We estimate relative hazards and absolute risks (or cumulative incidence or crude risk) under cause-specific proportional hazards models for competing risks from double nested case-control (DNCC) data. In the DNCC design, controls are time-matched not only to cases from the cause of primary interest, but also to cases from competing risks (the phase-two sample). Complete covariate data are available in the phase-two sample, but other cohort members only have information on survival outcomes and some covariates. Design-weighted estimators use inverse sampling probabilities computed from Samuelsen-type calculations for DNCC. To take advantage of additional information available on all cohort members, we augment the estimating equations with a term that is unbiased for zero but improves the efficiency of estimates from the cause-specific proportional hazards model. We establish the asymptotic properties of the proposed estimators, including the estimator of absolute risk, and derive consistent variance estimators. We show that augmented design-weighted estimators are more efficient than design-weighted estimators. Through simulations, we show that the proposed asymptotic methods yield nominal operating characteristics in practical sample sizes. We illustrate the methods using prostate cancer mortality data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Study of the National Cancer Institute.
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Affiliation(s)
- Minjung Lee
- Department of Statistics, Kangwon National University, Chuncheon, Gangwon 24341, South Korea
| | - Mitchell H Gail
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, United States
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Rentroia-Pacheco B, Bellomo D, Lakeman IMM, Wakkee M, Hollestein LM, van Klaveren D. Weighted metrics are required when evaluating the performance of prediction models in nested case-control studies. BMC Med Res Methodol 2024; 24:115. [PMID: 38760688 DOI: 10.1186/s12874-024-02213-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 04/04/2024] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND Nested case-control (NCC) designs are efficient for developing and validating prediction models that use expensive or difficult-to-obtain predictors, especially when the outcome is rare. Previous research has focused on how to develop prediction models in this sampling design, but little attention has been given to model validation in this context. We therefore aimed to systematically characterize the key elements for the correct evaluation of the performance of prediction models in NCC data. METHODS We proposed how to correctly evaluate prediction models in NCC data, by adjusting performance metrics with sampling weights to account for the NCC sampling. We included in this study the C-index, threshold-based metrics, Observed-to-expected events ratio (O/E ratio), calibration slope, and decision curve analysis. We illustrated the proposed metrics with a validation of the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA version 5) in data from the population-based Rotterdam study. We compared the metrics obtained in the full cohort with those obtained in NCC datasets sampled from the Rotterdam study, with and without a matched design. RESULTS Performance metrics without weight adjustment were biased: the unweighted C-index in NCC datasets was 0.61 (0.58-0.63) for the unmatched design, while the C-index in the full cohort and the weighted C-index in the NCC datasets were similar: 0.65 (0.62-0.69) and 0.65 (0.61-0.69), respectively. The unweighted O/E ratio was 18.38 (17.67-19.06) in the NCC datasets, while it was 1.69 (1.42-1.93) in the full cohort and its weighted version in the NCC datasets was 1.68 (1.53-1.84). Similarly, weighted adjustments of threshold-based metrics and net benefit for decision curves were unbiased estimates of the corresponding metrics in the full cohort, while the corresponding unweighted metrics were biased. In the matched design, the bias of the unweighted metrics was larger, but it could also be compensated by the weight adjustment. CONCLUSIONS Nested case-control studies are an efficient solution for evaluating the performance of prediction models that use expensive or difficult-to-obtain biomarkers, especially when the outcome is rare, but the performance metrics need to be adjusted to the sampling procedure.
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Affiliation(s)
- Barbara Rentroia-Pacheco
- Department of Dermatology, Erasmus Medical Center Cancer Institute, Erasmus University Medical Center, Dr. Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands.
| | | | - Inge M M Lakeman
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Marlies Wakkee
- Department of Dermatology, Erasmus Medical Center Cancer Institute, Erasmus University Medical Center, Dr. Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - Loes M Hollestein
- Department of Dermatology, Erasmus Medical Center Cancer Institute, Erasmus University Medical Center, Dr. Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
- Department of Research, Netherlands Comprehensive Cancer Organization (IKNL), Utrecht, The Netherlands
| | - David van Klaveren
- Department of Public Health, Center for Medical Decision Making, Erasmus University Medical Center, Rotterdam, The Netherlands
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Destefanis N, Fiano V, Milani L, Vasapolli P, Fiorentino M, Giunchi F, Lianas L, Del Rio M, Frexia F, Pireddu L, Molinaro L, Cassoni P, Papotti MG, Gontero P, Calleris G, Oderda M, Ricardi U, Iorio GC, Fariselli P, Isaevska E, Akre O, Zelic R, Pettersson A, Zugna D, Richiardi L. Cohort profile: the Turin prostate cancer prognostication (TPCP) cohort. Front Oncol 2023; 13:1242639. [PMID: 37869094 PMCID: PMC10587560 DOI: 10.3389/fonc.2023.1242639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 09/18/2023] [Indexed: 10/24/2023] Open
Abstract
Introduction Prostate cancer (PCa) is the most frequent tumor among men in Europe and has both indolent and aggressive forms. There are several treatment options, the choice of which depends on multiple factors. To further improve current prognostication models, we established the Turin Prostate Cancer Prognostication (TPCP) cohort, an Italian retrospective biopsy cohort of patients with PCa and long-term follow-up. This work presents this new cohort with its main characteristics and the distributions of some of its core variables, along with its potential contributions to PCa research. Methods The TPCP cohort includes consecutive non-metastatic patients with first positive biopsy for PCa performed between 2008 and 2013 at the main hospital in Turin, Italy. The follow-up ended on December 31st 2021. The primary outcome is the occurrence of metastasis; death from PCa and overall mortality are the secondary outcomes. In addition to numerous clinical variables, the study's prognostic variables include histopathologic information assigned by a centralized uropathology review using a digital pathology software system specialized for the study of PCa, tumor DNA methylation in candidate genes, and features extracted from digitized slide images via Deep Neural Networks. Results The cohort includes 891 patients followed-up for a median time of 10 years. During this period, 97 patients had progression to metastatic disease and 301 died; of these, 56 died from PCa. In total, 65.3% of the cohort has a Gleason score less than or equal to 3 + 4, and 44.5% has a clinical stage cT1. Consistent with previous studies, age and clinical stage at diagnosis are important prognostic factors: the crude cumulative incidence of metastatic disease during the 14-years of follow-up increases from 9.1% among patients younger than 64 to 16.2% for patients in the age group of 75-84, and from 6.1% for cT1 stage to 27.9% in cT3 stage. Discussion This study stands to be an important resource for updating existing prognostic models for PCa on an Italian cohort. In addition, the integrated collection of multi-modal data will allow development and/or validation of new models including new histopathological, digital, and molecular markers, with the goal of better directing clinical decisions to manage patients with PCa.
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Affiliation(s)
- Nicolas Destefanis
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Valentina Fiano
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Lorenzo Milani
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Paolo Vasapolli
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Michelangelo Fiorentino
- DIMEC Department of Medicine and Surgery, Alma Mater Studiorum, University of Bologna, Bologna, Italy
| | - Francesca Giunchi
- Department of Pathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Luca Lianas
- Visual and Data-intensive Computing, CRS4 (Center for Advanced Studies, Research and Development in Sardinia), Pula, Italy
| | - Mauro Del Rio
- Visual and Data-intensive Computing, CRS4 (Center for Advanced Studies, Research and Development in Sardinia), Pula, Italy
| | - Francesca Frexia
- Visual and Data-intensive Computing, CRS4 (Center for Advanced Studies, Research and Development in Sardinia), Pula, Italy
| | - Luca Pireddu
- Visual and Data-intensive Computing, CRS4 (Center for Advanced Studies, Research and Development in Sardinia), Pula, Italy
| | - Luca Molinaro
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Paola Cassoni
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | | | - Paolo Gontero
- Urology Unit, Department of Surgical Sciences, University of Turin, Molinette Hospital, Turin, Italy
| | - Giorgio Calleris
- Urology Unit, Department of Surgical Sciences, University of Turin, Molinette Hospital, Turin, Italy
| | - Marco Oderda
- Urology Unit, Department of Surgical Sciences, University of Turin, Molinette Hospital, Turin, Italy
| | | | | | - Piero Fariselli
- Computational Biomedicine Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Elena Isaevska
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Olof Akre
- Department of Molecular Medicine and Surgery, Section of Urology, Karolinska Institutet, Stockholm, Sweden
| | - Renata Zelic
- Department of Molecular Medicine and Surgery, Karolinska Institutet and Department of Pelvic Cancer, Karolinska University Hospital, Stockholm, Sweden
| | - Andreas Pettersson
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | - Daniela Zugna
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Lorenzo Richiardi
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
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Zelic R, Giunchi F, Lianas L, Mascia C, Zanetti G, Andrén O, Fridfeldt J, Carlsson J, Davidsson S, Molinaro L, Vincent PH, Richiardi L, Akre O, Fiorentino M, Pettersson A. Interchangeability of light and virtual microscopy for histopathological evaluation of prostate cancer. Sci Rep 2021; 11:3257. [PMID: 33547336 PMCID: PMC7864946 DOI: 10.1038/s41598-021-82911-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 12/29/2020] [Indexed: 01/01/2023] Open
Abstract
Virtual microscopy (VM) holds promise to reduce subjectivity as well as intra- and inter-observer variability for the histopathological evaluation of prostate cancer. We evaluated (i) the repeatability (intra-observer agreement) and reproducibility (inter-observer agreement) of the 2014 Gleason grading system and other selected features using standard light microscopy (LM) and an internally developed VM system, and (ii) the interchangeability of LM and VM. Two uro-pathologists reviewed 413 cores from 60 Swedish men diagnosed with non-metastatic prostate cancer 1998–2014. Reviewer 1 performed two reviews using both LM and VM. Reviewer 2 performed one review using both methods. The intra- and inter-observer agreement within and between LM and VM were assessed using Cohen’s kappa and Bland and Altman’s limits of agreement. We found good repeatability and reproducibility for both LM and VM, as well as interchangeability between LM and VM, for primary and secondary Gleason pattern, Gleason Grade Groups, poorly formed glands, cribriform pattern and comedonecrosis but not for the percentage of Gleason pattern 4. Our findings confirm the non-inferiority of VM compared to LM. The repeatability and reproducibility of percentage of Gleason pattern 4 was poor regardless of method used warranting further investigation and improvement before it is used in clinical practice.
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Affiliation(s)
- Renata Zelic
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
| | | | - Luca Lianas
- Data-Intensive Computing Division, Center for Advanced Studies, Research and Development in Sardinia (CRS4), Pula, Italy
| | - Cecilia Mascia
- Data-Intensive Computing Division, Center for Advanced Studies, Research and Development in Sardinia (CRS4), Pula, Italy
| | - Gianluigi Zanetti
- Data-Intensive Computing Division, Center for Advanced Studies, Research and Development in Sardinia (CRS4), Pula, Italy
| | - Ove Andrén
- Department of Urology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Jonna Fridfeldt
- Department of Urology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Jessica Carlsson
- Department of Urology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Sabina Davidsson
- Department of Urology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Luca Molinaro
- Division of Pathology, A.O. Città Della Salute e Della Scienza Hospital, Turin, Italy
| | - Per Henrik Vincent
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Department of Urology, Karolinska University Hospital, Stockholm, Sweden
| | - Lorenzo Richiardi
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, and CPO-Piemonte, Turin, Italy
| | - Olof Akre
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Department of Urology, Karolinska University Hospital, Stockholm, Sweden
| | | | - Andreas Pettersson
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
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