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Hicks B, Kaye JA, Azoulay L, Kristensen KB, Habel LA, Pottegård A. The Application of Lag Times in Cancer Pharmacoepidemiology: A Narrative Review. Ann Epidemiol 2023:S1047-2797(23)00090-X. [PMID: 37169040 DOI: 10.1016/j.annepidem.2023.05.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 04/27/2023] [Accepted: 05/05/2023] [Indexed: 05/13/2023]
Abstract
With the increasing utilization of medications worldwide, coupled with the increasing availability of long-term data, there is a growing opportunity and need for robust studies evaluating drug-cancer associations. One methodology of importance in such studies is the application of lag times. In this review, we discuss the main reasons for using lag times. Namely, we discuss the typically long latency period of cancer concerning both tumor promoter and initiator effects and outline why cancer latency is a key consideration when choosing a lag time. We also discuss how the use of lag times can help reduce protopathic and detection bias. Finally, we present practical advice for implementing lag periods. In general, we recommend that researchers consider the information that generated the hypothesis as well as clinical and biological knowledge to inform lag period selection. In addition, given that latency periods are usually unknown, we also advocate that researchers examine multiple lag periods in sensitivity analyses as well as duration analyses and flexible modeling approaches.
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Affiliation(s)
- Blánaid Hicks
- Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK; RTI Health Solutions, Waltham, Massachusetts; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec, Canada; Centre for Clinical Epidemiology, Lady Davis Institute, Montreal, Québec, Canada; Gerald Bronfman Department of Oncology, McGill University, Montreal, Québec, Canada; Clinical Pharmacology,Pharmacy and Environmental Medicine, Department of Public Health, University of Southern Denmark, Denmark; Division of Research, Kaiser Permanente Northern California, Oakland, California.
| | - James A Kaye
- Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK; RTI Health Solutions, Waltham, Massachusetts; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec, Canada; Centre for Clinical Epidemiology, Lady Davis Institute, Montreal, Québec, Canada; Gerald Bronfman Department of Oncology, McGill University, Montreal, Québec, Canada; Clinical Pharmacology,Pharmacy and Environmental Medicine, Department of Public Health, University of Southern Denmark, Denmark; Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Laurent Azoulay
- Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK; RTI Health Solutions, Waltham, Massachusetts; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec, Canada; Centre for Clinical Epidemiology, Lady Davis Institute, Montreal, Québec, Canada; Gerald Bronfman Department of Oncology, McGill University, Montreal, Québec, Canada; Clinical Pharmacology,Pharmacy and Environmental Medicine, Department of Public Health, University of Southern Denmark, Denmark; Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Kasper Bruun Kristensen
- Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK; RTI Health Solutions, Waltham, Massachusetts; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec, Canada; Centre for Clinical Epidemiology, Lady Davis Institute, Montreal, Québec, Canada; Gerald Bronfman Department of Oncology, McGill University, Montreal, Québec, Canada; Clinical Pharmacology,Pharmacy and Environmental Medicine, Department of Public Health, University of Southern Denmark, Denmark; Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Laurel A Habel
- Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK; RTI Health Solutions, Waltham, Massachusetts; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec, Canada; Centre for Clinical Epidemiology, Lady Davis Institute, Montreal, Québec, Canada; Gerald Bronfman Department of Oncology, McGill University, Montreal, Québec, Canada; Clinical Pharmacology,Pharmacy and Environmental Medicine, Department of Public Health, University of Southern Denmark, Denmark; Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Anton Pottegård
- Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK; RTI Health Solutions, Waltham, Massachusetts; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec, Canada; Centre for Clinical Epidemiology, Lady Davis Institute, Montreal, Québec, Canada; Gerald Bronfman Department of Oncology, McGill University, Montreal, Québec, Canada; Clinical Pharmacology,Pharmacy and Environmental Medicine, Department of Public Health, University of Southern Denmark, Denmark; Division of Research, Kaiser Permanente Northern California, Oakland, California
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Luijken K, Spekreijse JJ, van Smeden M, Gardarsdottir H, Groenwold RHH. New-user and prevalent-user designs and the definition of study time origin in pharmacoepidemiology: A review of reporting practices. Pharmacoepidemiol Drug Saf 2021; 30:960-974. [PMID: 33899305 PMCID: PMC8252086 DOI: 10.1002/pds.5258] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 04/01/2021] [Accepted: 04/20/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND Guidance reports for observational comparative effectiveness and drug safety research recommend implementing a new-user design whenever possible, since it reduces the risk of selection bias in exposure effect estimation compared to a prevalent-user design. The uptake of this guidance has not been studied extensively. METHODS We reviewed 89 observational effectiveness and safety cohort studies published in six pharmacoepidemiological journals in 2018 and 2019. We developed an extraction tool to assess how frequently new-user and prevalent-user designs were reported to be implemented. For studies that implemented a new-user design in both treatment arms, we extracted information about the extent to which the moment of meeting eligibility criteria, treatment initiation, and start of follow-up were reported to be aligned. RESULTS Of the 89 studies included, 40% reported implementing a new-user design for both the study exposure arm and the comparator arm, while 13% reported implementing a prevalent-user design in both arms. The moment of meeting eligibility criteria, treatment initiation, and start of follow-up were reported to be aligned in both treatment arms in 53% of studies that reported implementing a new-user design. We provided examples of studies that minimized the risk of introducing bias due to unclear definition of time origin in unexposed participants, immortal time, or a time lag. CONCLUSIONS Almost half of the included studies reported implementing a new-user design. Implications of misalignment of study design origin were difficult to assess because it would require explicit reporting of the target estimand in original studies. We recommend that the choice for a particular study time origin is explicitly motivated to enable assessment of validity of the study.
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Affiliation(s)
- Kim Luijken
- Department of Clinical EpidemiologyLeiden University Medical CenterLeidenThe Netherlands
| | | | - Maarten van Smeden
- Department of Clinical EpidemiologyLeiden University Medical CenterLeidenThe Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Helga Gardarsdottir
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical SciencesUtrecht UniversityUtrechtThe Netherlands
- Department of Clinical Pharmacy, Division Laboratories, Pharmacy and Biomedical Genetics, University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
- Faculty of Pharmaceutical SciencesUniversity of IcelandReykjavikIceland
| | - Rolf H. H. Groenwold
- Department of Clinical EpidemiologyLeiden University Medical CenterLeidenThe Netherlands
- Department of Biomedical Data SciencesLeiden University Medical CenterLeidenThe Netherlands
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Danieli C, Cohen S, Liu A, Pilote L, Guo L, Beauchamp ME, Marelli AJ, Abrahamowicz M. Flexible Modeling of the Association Between Cumulative Exposure to Low-Dose Ionizing Radiation From Cardiac Procedures and Risk of Cancer in Adults With Congenital Heart Disease. Am J Epidemiol 2019; 188:1552-1562. [PMID: 31107497 DOI: 10.1093/aje/kwz114] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 04/24/2019] [Accepted: 04/30/2019] [Indexed: 12/26/2022] Open
Abstract
Adults with congenital heart disease are increasingly being exposed to low-dose ionizing radiation (LDIR) from cardiac procedures. In a recent study, Cohen et al. (Circulation. 2018;137(13):1334-1345) reported an association between increased LDIR exposure and cancer incidence but did not explore temporal relationships. Yet, the impact of past exposures probably accumulates over years, and its strength may depend on the amount of time elapsed since exposure. Furthermore, LDIR procedures performed shortly before a cancer diagnosis may have been ordered because of early symptoms of cancer, raising concerns about reversal causality bias. To address these challenges, we combined flexible modeling of cumulative exposures with competing-risks methodology to estimate separate associations of time-varying LDIR exposure with cancer incidence and all-cause mortality. Among 24,833 patients from the Quebec Congenital Heart Disease Database, 602 had incident cancer and 500 died during a follow-up period of up to 15 years (1995-2010). Initial results suggested a strong association of cancer incidence with very recent LDIR exposures, likely reflecting reverse causality bias. When exposure was lagged by 2 years, an increased cumulative LDIR dose from the previous 2-6 years was associated with increased cancer incidence, with a stronger association for women. These results illustrate the importance of accurate modeling of temporal relationships between time-varying exposures and health outcomes.
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Affiliation(s)
- Coraline Danieli
- Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine, McGill University, Montréal, Quebec, Canada
- Center for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montréal, Quebec, Canada
| | - Sarah Cohen
- Center for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montréal, Quebec, Canada
- McGill Adult Unit for Congenital Heart Disease Excellence, McGill University Health Centre, Montréal, Quebec, Canada
| | - Aihua Liu
- Center for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montréal, Quebec, Canada
- McGill Adult Unit for Congenital Heart Disease Excellence, McGill University Health Centre, Montréal, Quebec, Canada
| | - Louise Pilote
- Center for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montréal, Quebec, Canada
- Department of Medicine, Faculty of Medicine, McGill University, Montréal, Quebec, Canada
| | - Liming Guo
- Center for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montréal, Quebec, Canada
- McGill Adult Unit for Congenital Heart Disease Excellence, McGill University Health Centre, Montréal, Quebec, Canada
| | - Marie-Eve Beauchamp
- Center for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montréal, Quebec, Canada
| | - Ariane J Marelli
- Center for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montréal, Quebec, Canada
- McGill Adult Unit for Congenital Heart Disease Excellence, McGill University Health Centre, Montréal, Quebec, Canada
| | - Michal Abrahamowicz
- Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine, McGill University, Montréal, Quebec, Canada
- Center for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montréal, Quebec, Canada
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Gagnon B, Abrahamowicz M, Xiao Y, Beauchamp ME, MacDonald N, Kasymjanova G, Kreisman H, Small D. Flexible modeling improves assessment of prognostic value of C-reactive protein in advanced non-small cell lung cancer. Br J Cancer 2010; 102:1113-22. [PMID: 20234363 PMCID: PMC2853092 DOI: 10.1038/sj.bjc.6605603] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Background: C-reactive protein (CRP) is gaining credibility as a prognostic factor in different cancers. Cox's proportional hazard (PH) model is usually used to assess prognostic factors. However, this model imposes a priori assumptions, which are rarely tested, that (1) the hazard ratio associated with each prognostic factor remains constant across the follow-up (PH assumption) and (2) the relationship between a continuous predictor and the logarithm of the mortality hazard is linear (linearity assumption). Methods: We tested these two assumptions of the Cox's PH model for CRP, using a flexible statistical model, while adjusting for other known prognostic factors, in a cohort of 269 patients newly diagnosed with non-small cell lung cancer (NSCLC). Results: In the Cox's PH model, high CRP increased the risk of death (HR=1.11 per each doubling of CRP value, 95% CI: 1.03–1.20, P=0.008). However, both the PH assumption (P=0.033) and the linearity assumption (P=0.015) were rejected for CRP, measured at the initiation of chemotherapy, which kept its prognostic value for approximately 18 months. Conclusion: Our analysis shows that flexible modeling provides new insights regarding the value of CRP as a prognostic factor in NSCLC and that Cox's PH model underestimates early risks associated with high CRP.
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Affiliation(s)
- B Gagnon
- Department of Medicine and Oncology, McGill University, 687 Pine Avenue West, R4.29, Montreal, Quebec, H3A 1A1, Canada.
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