Staff Profile

@article{pmid38738534,

title = {The Impact of Model Assumptions on Personalized Lung Cancer Screening Recommendations},

author = {Kevin Ten Haaf and Koen de Nijs and Giulia Simoni and Andres Alban and Pianpian Cao and Zhuolu Sun and Jean Yong and Jihyoun Jeon and Iakovos Toumazis and Summer S Han and G Scott Gazelle and Chung Ying Kong and Sylvia K Plevritis and Rafael Meza and Harry J de Koning},

doi = {10.1177/0272989X241249182},

issn = {1552-681X},

year  = {2024},

date = {2024-07-01},

urldate = {2024-05-01},

journal = {Med Decis Making},

volume = {44},

number = {5},

pages = {497-511},

abstract = {BACKGROUND: Recommendations regarding personalized lung cancer screening are being informed by natural-history modeling. Therefore, understanding how differences in model assumptions affect model-based personalized screening recommendations is essential.nnDESIGN: Five Cancer Intervention and Surveillance Modeling Network (CISNET) models were evaluated. Lung cancer incidence, mortality, and stage distributions were compared across 4 theoretical scenarios to assess model assumptions regarding 1) sojourn times, 2) stage-specific sensitivities, and 3) screening-induced lung cancer mortality reductions. Analyses were stratified by sex and smoking behavior.nnRESULTS: Most cancers had sojourn times \<5 y (model range [MR]; lowest to highest value across models: 83.5%-98.7% of cancers). However, cancer aggressiveness still varied across models, as demonstrated by differences in proportions of cancers with sojourn times \<2 y (MR: 42.5%-64.6%) and 2 to 4 y (MR: 28.8%-43.6%). Stage-specific sensitivity varied, particularly for stage I (MR: 31.3%-91.5%). Screening reduced stage IV incidence in most models for 1 y postscreening; increased sensitivity prolonged this period to 2 to 5 y. Screening-induced lung cancer mortality reductions among lung cancers detected at screening ranged widely (MR: 14.6%-48.9%), demonstrating variations in modeled treatment effectiveness of screen-detected cases. All models assumed longer sojourn times and greater screening-induced lung cancer mortality reductions for women. Models assuming differences in cancer epidemiology by smoking behaviors assumed shorter sojourn times and lower screening-induced lung cancer mortality reductions for heavy smokers.nnCONCLUSIONS: Model-based personalized screening recommendations are primarily driven by assumptions regarding sojourn times (favoring longer intervals for groups more likely to develop less aggressive cancers), sensitivity (higher sensitivities favoring longer intervals), and screening-induced mortality reductions (greater reductions favoring shorter intervals).nnIMPLICATIONS: Models suggest longer screening intervals may be feasible and benefits may be greater for women and light smokers.nnHIGHLIGHTS: Natural-history models are increasingly used to inform lung cancer screening, but causes for variations between models are difficult to assess.This is the first evaluation of these causes and their impact on personalized screening recommendations through easily interpretable metrics.Models vary regarding sojourn times, stage-specific sensitivities, and screening-induced lung cancer mortality reductions.Model outcomes were similar in predicting greater screening benefits for women and potentially light smokers. Longer screening intervals may be feasible for women and light smokers.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid37991182,

title = {Unspoken Truths: Mental Health Among Academic Surgeons},

author = {Reagan A Collins and Tianna Herman and Rebecca A Snyder and Krista L Haines and Anne Stey and Tania K Arora and Sunil K Geevarghese and Joseph D Phillips and Diego Vicente and Cornelia L Griggs and Imani E McElroy and Anji E Wall and Tasha M Hughes and Srijan Sen and Jaber Valinejad and Andres Alban and J Shannon Swan and Nathaniel Mercaldo and Mohammad S Jalali and Jagpreet Chhatwal and G Scott Gazelle and Erika Rangel and Chi-Fu Jeffrey Yang and Karen Donelan and Jessica A Gold and Colin P West and Carrie Cunningham},

doi = {10.1097/SLA.0000000000006159},

issn = {1528-1140},

year  = {2024},

date = {2024-03-01},

urldate = {2024-03-01},

journal = {Ann Surg},

volume = {279},

issue = {3},

pages = {429-436},

abstract = {OBJECTIVE: To characterize the current state of mental health within the surgical workforce in the United States (US).nnSUMMARY BACKGROUND DATA: Mental illness and suicide is a growing concern in the medical community; however, the current state is largely unknown.nnMETHODS: Cross-sectional survey of the academic surgery community assessing mental health, medical error, and suicidal ideation. The odds of suicidal ideation adjusting for sex, prior mental health diagnosis, and validated scales screening for depression, anxiety, post-traumatic stress disorder (PTSD), and alcohol use disorder were assessed.nnRESULTS: Of 622 participating medical students, trainees, and surgeons (estimated response rate=11.4-14.0%), 26.1% (141/539) reported a previous mental health diagnosis. 15.9% (83/523) of respondents screened positive for current depression, 18.4% (98/533) for anxiety, 11.0% (56/510) for alcohol use disorder, and 17.3% (36/208) for PTSD. Medical error was associated with depression (30.7% vs. 13.3%, P\<0.001), anxiety (31.6% vs. 16.2%, P=0.001), PTSD (12.8% vs. 5.6%, P=0.018), and hazardous alcohol consumption (18.7% vs. 9.7%, P=0.022). 13.2% (73/551) of respondents reported suicidal ideation in the past year and 9.6% (51/533) in the past two weeks. On adjusted analysis, a previous history of a mental health disorder (aOR: 1.97, 95% CI: 1.04-3.65, P=0.033), and screening positive for depression (aOR: 4.30, 95% CI: 2.21-8.29, P\<0.001) or PTSD (aOR: 3.93, 95% CI: 1.61-9.44, P=0.002) were associated with increased odds of suicidal ideation over the past 12 months.nnCONCLUSIONS: Nearly 1 in 7 respondents reported suicidal ideation in the past year. Mental illness and suicidal ideation are significant problems among the surgical workforce in the US.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Forster2021,

title = {Cost-effective clinical trial design: Application of a Bayesian sequential model to the ProFHER pragmatic trial.},

author = {Martin Forster and Stephen Brealey and Stephen Chick and Ada Keding and Belen Corbacho and Andres Alban and Paolo Pertile and Amar Rangan},

url = {https://pubmed.ncbi.nlm.nih.gov/34407641/},

doi = {10.1177/17407745211032909},

issn = {1740-7753},

year  = {2021},

date = {2021-08-01},

journal = {Clinical trials (London, England)},

pages = {17407745211032909},

abstract = {There is growing interest in the use of adaptive designs to improve the efficiency of clinical trials. We apply a Bayesian decision-theoretic model of a sequential experiment using cost and outcome data from the ProFHER pragmatic trial. We assess the model's potential for delivering value-based research. Using parameter values estimated from the ProFHER pragmatic trial, including the costs of carrying out the trial, we establish when the trial could have stopped, had the model's value-based stopping rule been used. We use a bootstrap analysis and simulation study to assess a range of operating characteristics, which we compare with a fixed sample size design which does not allow for early stopping. We estimate that application of the model could have stopped the ProFHER trial early, reducing the sample size by about 14%, saving about 5% of the research budget and resulting in a technology recommendation which was the same as that of the trial. The bootstrap analysis suggests that the expected sample size would have been 38% lower, saving around 13% of the research budget, with a probability of 0.92 of making the same technology recommendation decision. It also shows a large degree of variability in the trial's sample size. Benefits to trial cost stewardship may be achieved by monitoring trial data as they accumulate and using a stopping rule which balances the benefit of obtaining more information through continued recruitment with the cost of obtaining that information. We present recommendations for further research investigating the application of value-based sequential designs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alban2021,

title = {Resource Allocation with Sigmoidal Demands: Mobile Healthcare Units and Service Adoption},

author = {Philippe Blaettchen and Harwin Vries and Luk N. Van Wassenhove and Andres Alban},

url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3906146},

year  = {2021},

date = {2021-01-01},

journal = {Manufacturing \& Service Operations Management},

abstract = {Achieving broad access to health services (a target within the sustainable development goals) requires reaching rural populations. Mobile healthcare units (MHUs) visit remote sites to offer health services to these populations. However, limited exposure, health literacy, and trust can lead to sigmoidal (S-shaped) adoption dynamics, presenting a difficult obstacle in allocating limited MHU resources. It is tempting to allocate resources in line with current demand, as seen in practice. However, to maximize access in the long term, this may be far from optimal, and insights into allocation decisions are limited. 

 

We present a formal model of the allocation of MHU resources, i.e., the frequency of visits to each site, to maximize long-term uptake of preventative health services. We formulate the problem as the optimization of a sum of sigmoidal functions. While the problem is NP-hard, we provide closed-form solutions to particular cases of the model that elucidate insights into the optimal allocation. For example, more visits should generally be allocated to sites where the cumulative demand potential is higher and, counterintuitively, often those where demand is currently lower. To apply our insights in practice, we propose a practical method for estimating our model's parameters from pre-existing data. Our estimation approach achieves better predictions than standard methods. Finally, we demonstrate the potential of our approach by applying our methods to family planning MHUs in Uganda. In particular, we show that operationalizable heuristic allocations, grounded in our insights, outperform allocations based on current demand.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alban2020,

title = {ICU capacity management during the COVID-19 pandemic using a process simulation.},

author = {Stephen E. Chick and Dave A. Dongelmans and Alexander P. J. Vlaar and Danielle Sent and Study Group and Andres Alban},

url = {https://pubmed.ncbi.nlm.nih.gov/32383060/},

doi = {10.1007/s00134-020-06066-7},

issn = {1432-1238},

year  = {2020},

date = {2020-08-01},

journal = {Intensive care medicine},

volume = {46},

pages = {1624--1626},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alban2017,

title = {Efficient Monte Carlo methods for estimating failure probabilities},

author = {Hardik A. Darji and Atsuki Imamura and Marvin K. Nakayama and Andres Alban},

url = {https://www.sciencedirect.com/science/article/pii/S0951832017304325},

doi = {https://doi.org/10.1016/j.ress.2017.04.001},

issn = {0951-8320},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Reliability Engineering \& System Safety},

volume = {165},

pages = {376-394},

abstract = {We develop efficient Monte Carlo methods for estimating the failure probability of a system. An example of the problem comes from an approach for probabilistic safety assessment of nuclear power plants known as risk-informed safety-margin characterization, but it also arises in other contexts, e.g., structural reliability, catastrophe modeling, and finance. We estimate the failure probability using different combinations of simulation methodologies, including stratified sampling (SS), (replicated) Latin hypercube sampling (LHS), and conditional Monte Carlo (CMC). We prove theorems establishing that the combination SS+LHS (resp., SS+CMC+LHS) has smaller asymptotic variance than SS (resp., SS+LHS). We also devise asymptotically valid (as the overall sample size grows large) upper confidence bounds for the failure probability for the methods considered. The confidence bounds may be employed to perform an asymptotically valid probabilistic safety assessment. We present numerical results demonstrating that the combination SS+CMC+LHS can result in substantial variance reductions compared to stratified sampling alone.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}