First do no harm: extending the debate on the provision of preventive tamoxifen

Will, B P; Nobrega, K.M; Berthelot, J.M.; Flanagan, W. Michael; Wolfson, Michael; Logan, Diane; Evans, William K.

doi:10.1054/bjoc.2001.2125

Cited by 15 publications

(12 citation statements)

References 32 publications

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“…However, patients should be informed of this rare side effect so that they may seek prompt ophthalmic evaluation for ocular A djuvant chemotherapy and tamoxifen clearly reduce recurrence and death rates after surgery for operable breast cancer, 1,2 and tamoxifen reduces contralateral breast cancer 2 and breast cancer incidence in high-risk women. 3 However, concern about possible side effects is an important issue, particularly when prescribing adjuvant or chemopreventive treatments to healthy 4 or possibly cured patients after surgery. Certain side effects of chemotherapy and hormone therapy are well recognized, 5 whereas ocular toxicity is less frequently described in reports from adjuvant treatment clinical trials and is probably underestimated.…”

Section: Resultsmentioning

confidence: 99%

Ocular toxicity during adjuvant chemoendocrine therapy for early breast cancer

Giannì

Panzini

et al. 2005

Cancer

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BACKGROUND Others have reported ocular toxicity after adjuvant chemoendocrine therapy, but this study looked at ocular toxicity in similarly treated patients from large randomized clinical trials. METHODS Information was retrieved on incidence and timing of ocular toxicity from the International Breast Cancer Study Group (IBCSG) database of 4948 eligible patients randomized to receive tamoxifen or toremifene alone or in combination with chemotherapy (either concurrently or sequentially). Case reports of patients with ocular toxicity were evaluated to determine whether ocular toxicity occurred during chemotherapy and/or hormonal therapy. Additional information was obtained from participating institutions for patients in whom ocular toxicity occurred after chemotherapy but during administration of tamoxifen or toremifene. RESULTS Ocular toxicity was reported in 538 of 4948 (10.9%) patients during adjuvant treatment, mainly during chemotherapy. Forty‐five of 4948 (0.9%) patients had ocular toxicity during hormone therapy alone, but only 30 (0.6%) patients had ocular toxicity reported either without receiving any chemotherapy or beyond 3 months after completing chemotherapy and, thus, possibly related to tamoxifen or toremifene. In 3 cases, retinal alterations, without typical aspects of tamoxifen toxicity, were reported; 4 patients had cataract (2 bilateral), 12 impaired visual acuity, 10 ocular irritation, 1 optical neuritis, and the rest had other symptoms. CONCLUSION Ocular toxicity during adjuvant therapy is a common side effect mainly represented by irritative symptoms due to chemotherapy. By contrast, ocular toxicity during hormonal therapy is rare and does not appear to justify a regular program of ocular examination. However, patients should be informed of this rare side effect so that they may seek prompt ophthalmic evaluation for ocular complaints. Cancer 2006. © 2005 American Cancer Society.

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Section: Resultsmentioning

confidence: 99%

Ocular toxicity during adjuvant chemoendocrine therapy for early breast cancer

Giannì

Panzini

et al. 2005

Cancer

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“…It is worth noting that the vast majority of microsimulation model-based analyses fail to account for such variation. (An exception is Will et al 2001). The underlying joint distribution of life lengths (LLs) and health-adjusted life lengths (HALLs) is shown in Figure 6.…”

Section: Base Case Scenariomentioning

confidence: 99%

HealthPaths: Using functional health trajectories to quantify the relative importance of selected health determinants

Wolfson¹,

Rowe²

2014

DemRes

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BACKGROUNDIt is widely known that that there is a significant dispersion in health status, as well as a strong correlation between health status and socio-economic status. But considerable uncertainty remains as to the quantitative importance of the various explanatory factors typically cited in this context. As health status is intrinsically a reflection of co-evolving dynamic processes, it is important to take a lifetime perspective when seeking to understand its determinants. The -bottom line‖ measure of overall population health is, though, health-adjusted life expectancy (HALE), which is an aggregation of individuals' health-adjusted life lengths (HALLs). OBJECTIVEIn an exploratory mode, we intend to provide a realistic assessment of the relative importance of selected health determinants of HALE. METHODSThis paper first draws on very detailed estimates of the covariates of vector-valued functional health trajectories, using the National Population Health Survey (Statistics Canada). We then use longitudinal microsimulation to draw out their implications by synthesising first a realistic base case -specifically, a representative longitudinal population sample -and then a series of exploratory counterfactual populations. Comparisons between and among counterfactuals and the base case are then used to estimate the quantitative importance of various factors in accounting for HALE. RESULTSSeveral surprising results emerged. Of the four risk factors explicitly examined, obesity had the smallest impacts on HALE: moving from the fifth to the 95 th percentiles of BMI increased HALE 1.5 and 2.5 years for men and women, respectively. Eliminating smoking increased HALE by five and four years, while moving from the lowest to the http://www.demographic-research.org highest levels of education had similar effects of about five years for both men and women. Perhaps surprisingly, moving from the fifth to the 95 th percentiles of the psycho-social factor, Antonovsky's sense of coherence, had the greatest impact on estimated HALE, with increases of 5.5 to six years. CONCLUSIONSWhile the analysis is exploratory, the results show the analytical potential of tightly coupling detailed empirical analysis of complex longitudinal data with microsimulation modelling. Substantively, the results of the analysis contribute to the evidence that the widespread public health preoccupation with the -obesity epidemic‖ is over-stated, while psycho-social and mental health problems continue to be seriously underemphasised.

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“…There are several sampling approaches for the MC method [45]. One of the most effective is the Latin hypercube sampling (LHS) [31,43]. LHS is a particular type of stratified sampling, which samples from the equally probable segments of the parameters distribution.…”

Section: Monte Carlomentioning

confidence: 99%

“…The major limitation of these techniques is that the assumptions of a specific response surface function may not Epidemiology Research International 3 hold in all types of MS models or even for different outcomes within one model [42]. Another approach is to perform "guided" sampling, for example Latin hypercube sampling (LHS) [31,43] or orthogonal sampling [44]. However, these sampling approaches do not reduce the model execution time, which is the "bottleneck" of the total computational time in UA [45].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis in Population-Based Disease Microsimulation Models

Sharif

Kopeć

Wong

et al. 2012

Epidemiology Research International

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Objective. Uncertainty analysis (UA) is an important part of simulation model validation. However, literature is imprecise as to how UA should be performed in the context of population-based microsimulation (PMS) models. In this expository paper, we discuss a practical approach to UA for such models. Methods. By adapting common concepts from published UA guidelines, we developed a comprehensive, step-by-step approach to UA in PMS models, including sample size calculation to reduce the computational time.As an illustration, we performed UA for POHEM-OA, a microsimulation model of osteoarthritis (OA) in Canada. Results. The resulting sample size of the simulated population was 500,000 and the number of Monte Carlo (MC) runs was 785 for 12-hour computational time. The estimated 95% uncertainty intervals for the prevalence of OA in Canada in 2021 were 0.09 to 0.18 for men and 0.15 to 0.23 for women. The uncertainty surrounding the sex-specific prevalence of OA increased over time. Conclusion. The proposed approach to UA considers the challenges specific to PMS models, such as selection of parameters and calculation of MC runs and population size to reduce computational burden. Our example of UA shows that the proposed approach is feasible. Estimation of uncertainty intervals should become a standard practice in the reporting of results from PMS models.

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First do no harm: extending the debate on the provision of preventive tamoxifen

Cited by 15 publications

References 32 publications

Ocular toxicity during adjuvant chemoendocrine therapy for early breast cancer

Ocular toxicity during adjuvant chemoendocrine therapy for early breast cancer

HealthPaths: Using functional health trajectories to quantify the relative importance of selected health determinants

Uncertainty Analysis in Population-Based Disease Microsimulation Models

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