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Article ; Online: What can hospital emergency admissions prior to cancer diagnosis tell us about socio-economic inequalities in cancer diagnosis? Evidence from population-based data in England.

Exarchakou, Aimilia / Rachet, Bernard / Lyratzopoulos, Georgios / Maringe, Camille / Rubio, Francisco Javier

2024

Abstract: Background: More deprived cancer patients are at higher risk of Emergency Presentation (EP) with most studies pointing to lower symptom awareness and increased comorbidities to explain those patterns. With the example of colon cancer, we examine ... ...

Abstract	Background: More deprived cancer patients are at higher risk of Emergency Presentation (EP) with most studies pointing to lower symptom awareness and increased comorbidities to explain those patterns. With the example of colon cancer, we examine patterns of hospital emergency admissions (HEAs) history in the most and least deprived patients as a potential precursor of EP. Methods: We analysed the rates of hospital admissions and their admission codes (retrieved from Hospital Episode Statistics) in the two years preceding cancer diagnosis by sex, deprivation and route to diagnosis (EP, non-EP). To select the conditions (grouped admission codes) that best predict emergency admission, we adapted the purposeful variable selection to mixed-effects logistic regression. Results: Colon cancer patients diagnosed through EP had the highest number of HEAs than all the other routes to diagnosis, especially in the last 7 months before diagnosis. Most deprived patients had an overall higher rate and higher probability of HEA but fewer conditions associated with it. Conclusions: Our findings point to higher use of emergency services for non-specific symptoms and conditions in the most deprived patients, preceding colon cancer diagnosis. Health system barriers may be a shared factor of socio-economic inequalities in EP and HEAs.
Language	English
Publishing date	2024-04-26
Publishing country	England
Document type	Journal Article
ZDB-ID	80075-2
ISSN	1532-1827 ; 0007-0920
ISSN (online)	1532-1827
ISSN	0007-0920
DOI	10.1038/s41416-024-02688-6
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Article ; Online: Immortal-time bias in older vs younger age groups: a simulation study with application to a population-based cohort of patients with colon cancer.

Pilleron, Sophie / Maringe, Camille / Morris, Eva J A / Leyrat, Clémence

British journal of cancer

2023 Volume 128, Issue 8, Page(s) 1521–1528

Abstract: Background: In observational studies, the risk of immortal-time bias (ITB) increases with the likelihood of early death, itself increasing with age. We investigated how age impacts the magnitude of ITB when estimating the effect of surgery on 1-year ... ...

Abstract	Background: In observational studies, the risk of immortal-time bias (ITB) increases with the likelihood of early death, itself increasing with age. We investigated how age impacts the magnitude of ITB when estimating the effect of surgery on 1-year overall survival (OS) in patients with Stage IV colon cancer aged 50-74 and 75-84 in England. Methods: Using simulations, we compared estimates from a time-fixed exposure model to three statistical methods addressing ITB: time-varying exposure, delayed entry and landmark methods. We then estimated the effect of surgery on OS using a population-based cohort of patients from the CORECT-R resource and conducted the analysis using the emulated target trial framework. Results: In simulations, the magnitude of ITB was larger among older patients when their probability of early death increased or treatment was delayed. The bias was corrected using the methods addressing ITB. When applied to CORECT-R data, these methods yielded a smaller effect of surgery than the time-fixed exposure approach but effects were similar in both age groups. Conclusion: ITB must be addressed in all longitudinal studies, particularly, when investigating the effect of exposure on an outcome in different groups of people (e.g., age groups) with different distributions of exposure and outcomes.
MeSH term(s)	Aged ; Humans ; Bias ; Colonic Neoplasms/surgery ; England/epidemiology ; Probability ; Time Factors
Language	English
Publishing date	2023-02-09
Publishing country	England
Document type	Comparative Study ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	80075-2
ISSN	1532-1827 ; 0007-0920
ISSN (online)	1532-1827
ISSN	0007-0920
DOI	10.1038/s41416-023-02187-0
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Article ; Online: Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review.

Smith, Matthew J / Phillips, Rachael V / Luque-Fernandez, Miguel Angel / Maringe, Camille

Annals of epidemiology

2023 Volume 86, Page(s) 34–48.e28

Abstract: Purpose: The targeted maximum likelihood estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least biased, efficient, and robust strategy for estimation and ... ...

Abstract	Purpose: The targeted maximum likelihood estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least biased, efficient, and robust strategy for estimation and inference of a variety of statistical and causal parameters. We describe and evaluate the epidemiological applications that have benefited from recent methodological developments. Methods: We conducted a systematic literature review in PubMed for articles that applied any form of TMLE in observational studies. We summarized the epidemiological discipline, geographical location, expertize of the authors, and TMLE methods over time. We used the Roadmap of Targeted Learning and Causal Inference to extract key methodological aspects of the publications. We showcase the contributions to the literature of these TMLE results. Results: Of the 89 publications included, 33% originated from the University of California at Berkeley, where the framework was first developed by Professor Mark van der Laan. By 2022, 59% of the publications originated from outside the United States and explored up to seven different epidemiological disciplines in 2021-2022. Double-robustness, bias reduction, and model misspecification were the main motivations that drew researchers toward the TMLE framework. Through time, a wide variety of methodological, tutorial, and software-specific articles were cited, owing to the constant growth of methodological developments around TMLE. Conclusions: There is a clear dissemination trend of the TMLE framework to various epidemiological disciplines and to increasing numbers of geographical areas. The availability of R packages, publication of tutorial papers, and involvement of methodological experts in applied publications have contributed to an exponential increase in the number of studies that understood the benefits and adoption of TMLE.
MeSH term(s)	Humans ; Likelihood Functions ; Public Health ; Models, Statistical ; Bias ; Epidemiologic Studies
Language	English
Publishing date	2023-06-19
Publishing country	United States
Document type	Systematic Review ; Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	1074355-8
ISSN	1873-2585 ; 1047-2797
ISSN (online)	1873-2585
ISSN	1047-2797
DOI	10.1016/j.annepidem.2023.06.004
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Article ; Online: Prediction of cancer survival for cohorts of patients most recently diagnosed using multi-model inference.

Maringe, Camille / Belot, Aurélien / Rachet, Bernard

Statistical methods in medical research

2020 Volume 29, Issue 12, Page(s) 3605–3622

Abstract: Despite a large choice of models, functional forms and types of effects, the selection of excess hazard models for prediction of population cancer survival is not widespread in the literature. We propose multi-model inference based on excess hazard model( ...

Abstract	Despite a large choice of models, functional forms and types of effects, the selection of excess hazard models for prediction of population cancer survival is not widespread in the literature. We propose multi-model inference based on excess hazard model(s) selected using Akaike information criteria or Bayesian information criteria for prediction and projection of cancer survival. We evaluate the properties of this approach using empirical data of patients diagnosed with breast, colon or lung cancer in 1990-2011. We artificially censor the data on 31 December 2010 and predict five-year survival for the 2010 and 2011 cohorts. We compare these predictions to the observed five-year cohort estimates of cancer survival and contrast them to predictions from an a priori selected simple model, and from the period approach. We illustrate the approach by replicating it for cohorts of patients for which stage at diagnosis and other important prognosis factors are available. We find that model-averaged predictions and projections of survival have close to minimal differences with the Pohar-Perme estimation of survival in many instances, particularly in subgroups of the population. Advantages of information-criterion based model selection include (i) transparent model-building strategy, (ii) accounting for model selection uncertainty, (iii) no a priori assumption for effects, and (iv) projections for patients outside of the sample.
MeSH term(s)	Bayes Theorem ; Cohort Studies ; Humans ; Neoplasms ; Proportional Hazards Models ; Survival Analysis
Language	English
Publishing date	2020-10-05
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1136948-6
ISSN	1477-0334 ; 0962-2802
ISSN (online)	1477-0334
ISSN	0962-2802
DOI	10.1177/0962280220934501
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Article ; Online: Target trial emulation with multi-state model analysis to assess treatment effectiveness using clinical COVID-19 data.

Martinuka, Oksana / Hazard, Derek / Marateb, Hamid Reza / Maringe, Camille / Mansourian, Marjan / Rubio-Rivas, Manuel / Wolkewitz, Martin

BMC medical research methodology

2023 Volume 23, Issue 1, Page(s) 197

Abstract: Background: Real-world observational data are an important source of evidence on the treatment effectiveness for patients hospitalized with coronavirus disease 2019 (COVID-19). However, observational studies evaluating treatment effectiveness based on ... ...

Abstract	Background: Real-world observational data are an important source of evidence on the treatment effectiveness for patients hospitalized with coronavirus disease 2019 (COVID-19). However, observational studies evaluating treatment effectiveness based on longitudinal data are often prone to methodological biases such as immortal time bias, confounding bias, and competing risks. Methods: For exemplary target trial emulation, we used a cohort of patients hospitalized with COVID-19 (n = 501) in a single centre. We described the methodology for evaluating the effectiveness of a single-dose treatment, emulated a trial using real-world data, and drafted a hypothetical study protocol describing the main components. To avoid immortal time and time-fixed confounding biases, we applied the clone-censor-weight technique. We set a 5-day grace period as a period of time when treatment could be initiated. We used the inverse probability of censoring weights to account for the selection bias introduced by artificial censoring. To estimate the treatment effects, we took the multi-state model approach. We considered a multi-state model with five states. The primary endpoint was defined as clinical severity status, assessed by a 5-point ordinal scale on day 30. Differences between the treatment group and standard of care treatment group were calculated using a proportional odds model and shown as odds ratios. Additionally, the weighted cause-specific hazards and transition probabilities for each treatment arm were presented. Results: Our study demonstrates that trial emulation with a multi-state model analysis is a suitable approach to address observational data limitations, evaluate treatment effects on clinically heterogeneous in-hospital death and discharge alive endpoints, and consider the intermediate state of admission to ICU. The multi-state model analysis allows us to summarize results using stacked probability plots that make it easier to interpret results. Conclusions: Extending the emulated target trial approach to multi-state model analysis complements treatment effectiveness analysis by gaining information on competing events. Combining two methodologies offers an option to address immortal time bias, confounding bias, and competing risk events. This methodological approach can provide additional insight for decision-making, particularly when data from randomized controlled trials (RCTs) are unavailable.
MeSH term(s)	Humans ; COVID-19 ; Treatment Outcome ; Selection Bias ; Hospitalization ; Odds Ratio
Language	English
Publishing date	2023-09-02
Publishing country	England
Document type	Observational Study ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2041362-2
ISSN	1471-2288 ; 1471-2288
ISSN (online)	1471-2288
ISSN	1471-2288
DOI	10.1186/s12874-023-02001-8
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Book ; Online: Application of targeted maximum likelihood estimation in public health and epidemiological studies

Smith, Matthew J. / Phillips, Rachael V. / Luque-Fernandez, Miguel Angel / Maringe, Camille

a systematic review

2023

Abstract: The Targeted Maximum Likelihood Estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least biased, efficient and robust strategy for estimation and inference of a ... ...

Abstract	The Targeted Maximum Likelihood Estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least biased, efficient and robust strategy for estimation and inference of a variety of statistical and causal parameters. We describe and evaluate the epidemiological applications that have benefited from recent methodological developments. We conducted a systematic literature review in PubMed for articles that applied any form of TMLE in observational studies. We summarised the epidemiological discipline, geographical location, expertise of the authors, and TMLE methods over time. We used the Roadmap of Targeted Learning and Causal Inference to extract key methodological aspects of the publications. We showcase the contributions to the literature of these TMLE results. Of the 81 publications included, 25% originated from the University of California at Berkeley, where the framework was first developed by Professor Mark van der Laan. By the first half of 2022, 70% of the publications originated from outside the United States and explored up to 7 different epidemiological disciplines in 2021-22. Double-robustness, bias reduction and model misspecification were the main motivations that drew researchers towards the TMLE framework. Through time, a wide variety of methodological, tutorial and software-specific articles were cited, owing to the constant growth of methodological developments around TMLE. There is a clear dissemination trend of the TMLE framework to various epidemiological disciplines and to increasing numbers of geographical areas. The availability of R packages, publication of tutorial papers, and involvement of methodological experts in applied publications have contributed to an exponential increase in the number of studies that understood the benefits, and adoption, of TMLE. Comment: 42 pages, 2 figures, 2 tables
Keywords	Statistics - Applications ; Statistics - Methodology ; Statistics - Machine Learning
Subject code	306
Publishing date	2023-03-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Reply to: Versatility of the clone-censor-weight approach: response to ''trial emulation in the presence of immortal-time bias''.

Maringe, Camille / Benitez Majano, Sara / Exarchakou, Aimilia / Smith, Matthew / Rachet, Bernard / Belot, Aurélien / Leyrat, Clémence

International journal of epidemiology

2021 Volume 50, Issue 2, Page(s) 696

MeSH term(s)	Bias ; Clone Cells ; Humans ; Time Factors
Language	English
Publishing date	2021-01-09
Publishing country	England
Document type	Letter ; Research Support, Non-U.S. Gov't ; Comment
ZDB-ID	187909-1
ISSN	1464-3685 ; 0300-5771
ISSN (online)	1464-3685
ISSN	0300-5771
DOI	10.1093/ije/dyaa225
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Article ; Online: Age disparities in lung cancer survival in New Zealand: The role of patient and clinical factors.

Pilleron, Sophie / Maringe, Camille / Charvat, Hadrien / Atkinson, June / Morris, Eva / Sarfati, Diana

Lung cancer (Amsterdam, Netherlands)

2021 Volume 157, Page(s) 92–99

Abstract: Objective: Age is an important prognostic factor for lung cancer. However, no studies have investigated the age difference in lung cancer survival per se. We, therefore, described the role of patient-related and clinical factors on the age pattern in ... ...

Abstract	Objective: Age is an important prognostic factor for lung cancer. However, no studies have investigated the age difference in lung cancer survival per se. We, therefore, described the role of patient-related and clinical factors on the age pattern in lung cancer excess mortality hazard by stage at diagnosis in New Zealand. Materials and methods: We extracted 22 487 new lung cancer cases aged 50-99 (median age = 71, 47.1 % females) diagnosed between 1 January 2006 and 31 July 2017 from the New Zealand population-based cancer registry and followed up to December 2019. We modelled the effect of age at diagnosis, sex, ethnicity, deprivation, comorbidity, and emergency presentation on the excess mortality hazard by stage at diagnosis, and we derived corresponding lung cancer net survival. Results: The age difference in net survival was particularly marked for localised and regional lung cancers, with a sharp decline in survival from the age of 70. No identified factors influenced age disparities in patients with localised cancer. However, for other stages, females had a greater difference in survival between middle-age and older-age than males. Comorbidity and emergency presentation played a minor role. Ethnicity and deprivation did not influence age disparities in lung cancer survival. Conclusion: Sex and stage at diagnosis were the most important factors of age disparities in lung cancer survival in New Zealand.
MeSH term(s)	Aged ; Comorbidity ; Ethnicity ; Female ; Humans ; Lung Neoplasms/epidemiology ; Male ; Middle Aged ; New Zealand/epidemiology ; Registries
Language	English
Publishing date	2021-05-13
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	632771-0
ISSN	1872-8332 ; 0169-5002
ISSN (online)	1872-8332
ISSN	0169-5002
DOI	10.1016/j.lungcan.2021.05.015
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Article ; Online: The impact of timely cancer diagnosis on age disparities in colon cancer survival.

Pilleron, Sophie / Maringe, Camille / Charvat, Hadrien / Atkinson, June / Morris, Eva J A / Sarfati, Diana

Journal of geriatric oncology

2021 Volume 12, Issue 7, Page(s) 1044–1051

Abstract: Objective: We described the role of patient-related and clinical factors on age disparities in colon cancer survival among patients aged 50-99 using New Zealand population-based cancer registry data linked to hospitalisation data.: Method: We ... ...

Abstract	Objective: We described the role of patient-related and clinical factors on age disparities in colon cancer survival among patients aged 50-99 using New Zealand population-based cancer registry data linked to hospitalisation data. Method: We included 21,270 new colon cancer cases diagnosed between 1 January 2006 and 31 July 2017, followed up to end 2019. We modelled the effect of age at diagnosis, sex, ethnicity, deprivation, comorbidity, and emergency presentation on colon cancer survival by stage at diagnosis using flexible excess hazard regression models. Results: The excess mortality in older patients was minimal for localised cancers, maximal during the first six months for regional cancers, the first eighteen months for distant cancers, and over the three years for missing stages. The age pattern of the excess mortality hazard varied according to sex for distant cancers, emergency presentation for regional and distant cancers, and comorbidity for cancer with missing stages. Ethnicity and deprivation did not influence age disparities in colon cancer survival. Conclusion: Factors reflecting timeliness of cancer diagnosis most affected age-related disparities in colon cancer survival, probably by impacting treatment strategy. Because of the high risk of poor outcomes related to treatment in older patients, efforts made to improve earlier diagnosis in older patients are likely to help reduce age disparities in colon cancer survival in New Zealand.
MeSH term(s)	Aged ; Colonic Neoplasms/diagnosis ; Colonic Neoplasms/pathology ; Comorbidity ; Humans ; Neoplasm Staging ; New Zealand/epidemiology ; Proportional Hazards Models ; Time Factors
Language	English
Publishing date	2021-04-15
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2556813-9
ISSN	1879-4076 ; 1879-4068
ISSN (online)	1879-4076
ISSN	1879-4068
DOI	10.1016/j.jgo.2021.04.003
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Article ; Online: Comparison of model-building strategies for excess hazard regression models in the context of cancer epidemiology.

Maringe, Camille / Belot, Aurélien / Rubio, Francisco Javier / Rachet, Bernard

BMC medical research methodology

2019 Volume 19, Issue 1, Page(s) 210

Abstract: Background: Large and complex population-based cancer data are becoming broadly available, thanks to purposeful linkage between cancer registry data and health electronic records. Aiming at understanding the explanatory power of factors on cancer ... ...

Abstract	Background: Large and complex population-based cancer data are becoming broadly available, thanks to purposeful linkage between cancer registry data and health electronic records. Aiming at understanding the explanatory power of factors on cancer survival, the modelling and selection of variables need to be understood and exploited properly for improving model-based estimates of cancer survival. Method: We assess the performances of well-known model selection strategies developed by Royston and Sauerbrei and Wynant and Abrahamowicz that we adapt to the relative survival data setting and to test for interaction terms. Results: We apply these to all male patients diagnosed with lung cancer in England in 2012 (N = 15,688), and followed-up until 31/12/2015. We model the effects of age at diagnosis, tumour stage, deprivation, comorbidity and emergency presentation, as well as interactions between age and all of the above. Given the size of the dataset, all model selection strategies favoured virtually the same model, except for a non-linear effect of age at diagnosis selected by the backward-based selection strategies (versus a linear effect selected otherwise). Conclusion: The results from extensive simulations evaluating varying model complexity and sample sizes provide guidelines on a model selection strategy in the context of excess hazard modelling.
MeSH term(s)	Age Factors ; Aged ; Aged, 80 and over ; Algorithms ; England/epidemiology ; Humans ; Linear Models ; Lung Neoplasms/mortality ; Lung Neoplasms/pathology ; Male ; Middle Aged ; Neoplasm Staging ; Nonlinear Dynamics ; Proportional Hazards Models ; Survival Rate
Language	English
Publishing date	2019-11-20
Publishing country	England
Document type	Comparative Study ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2041362-2
ISSN	1471-2288 ; 1471-2288
ISSN (online)	1471-2288
ISSN	1471-2288
DOI	10.1186/s12874-019-0830-9
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