Using time series analysis to forecast the health-related quality of life of post-menopausal women with non-metastatic ER+ breast cancer: A tutorial and case study

Xiao, Hong; Jiang, Xinyi; Chen, Cheng; Wang, Wei; Wang, Ching‐Yu; Ali, Askal Ayalew; Berthe, Abdrahmane; Moussa, Richard K.; Diaby, Vakaramoko

doi:10.1016/j.sapharm.2019.11.009

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Overall, they found an acceptable forecasting value of 5.9%, meaning that their model over- or under-reported on average 5.9% (1.9% to 11.8%) of high symptom complexity, which is acceptable and within the typical 5% MAPE threshold [ 86 ]. Similar findings are reported on time series forecasts using common-day clustering for outpatient clinic visits, ARIMA or Bayesian methods for incidences of different cancer types as well as forecasts for health-related QoL in breast cancer [ 149 , 150 , 151 , 152 ]. These types of research methodologies can further be used to evaluate intervention programs.…”

Section: The Use Of Epro For Process Monitoring and Early Warning Sig...supporting

confidence: 79%

Emotion Regulation Flexibility and Electronic Patient-Reported Outcomes: A Framework for Understanding Symptoms and Affect Dynamics in Pediatric Psycho-Oncology

Mirzaie

Burns-Gebhart

Meyerheim

et al. 2022

Cancers

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Emotion dysregulation is regarded as a driving mechanism for the development of mental health problems and psychopathology. The role of emotion regulation (ER) in the management of cancer distress and quality of life (QoL) has recently been recognized in psycho-oncology. The latest technological advances afford ways to assess ER, affective experiences and QoL in child, adolescent and young adult (CAYA) cancer patients through electronic patient-reported outcomes (ePRO) in their daily environment in real-time. Such tools facilitate ways to study the dynamics of affect and the flexibility of ER. However, technological advancement is not risk-free. We critically review the literature on ePRO in cancer existing models of ER in pediatric psycho-oncology and analyze strength, weaknesses, opportunities and threats of ePRO with a focus on CAYA cancer research and care. Supported by personal study-based experiences, this narrative review serves as a foundation to propose a novel methodological and metatheoretical framework based on: (a) an extended notion of ER, which includes its dynamic, adaptive and flexible nature and focuses on processes and conditions rather than fixed categorical strategies; (b) ePRO as a means to measure emotion regulation flexibility and affect dynamics; (c) identifying early warning signals for symptom change via ePRO and building forecasting models using dynamical systems theory.

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Section: The Use Of Epro For Process Monitoring and Early Warning Sig...supporting

confidence: 79%

Emotion Regulation Flexibility and Electronic Patient-Reported Outcomes: A Framework for Understanding Symptoms and Affect Dynamics in Pediatric Psycho-Oncology

Mirzaie

Burns-Gebhart

Meyerheim

et al. 2022

Cancers

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“…A technique for analyzing a set of data points gathered over time is called time series analysis. [14] In time series analysis, analysts are required to collect data points at regular intervals throughout a predetermined length of time. This is in contrast to the more common practice of collecting data points infrequently or randomly.…”

Section: Time Seriesmentioning

confidence: 99%

Unraveling the Multifaceted Nature of Breast Cancer: Harnessing the Power of Machine Learning and Statistical Models for In-depth Investigation, Prognosis, and Treatment Optimization

Dafallah Mohamed Alhasan,

Sharma,

Ben Said

et al. 2024

Interciencia

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The most prevalent form of cancer in all of human history. Women typically have a shorter lifespan than men. The death toll from breast cancer is higher than that of any other type of cancer. After reaching puberty, women all around the world are at risk for developing breast cancer, however, the disease is more prevalent in elderly individuals. The death rate from breast cancer did not significantly change from the 1930s through the 1970s. In the 1980s, countries that were healthcare conscious and had early detection systems, as well as a variety of treatment modalities to eliminate invasive diseases, began to see an increase in their overall survival rates. This article employed decision tree models and time series models to anticipate breast cancer using time series data from 1930 to 2019. The breast cancer cases that occurred in the United States of America served as the basis for this study. The most precise prediction models will be produced as a result of this data analysis. In order to evaluate how well the predictions were made, we determined how accurate they were by calculating the symmetric mean absolute percentage errors (sMAPE), measured mean absolute percentage errors (MASE), and mean absolute percentage errors (MAPE).

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“…Many examples are found in the literature in several areas of knowledge. There are papers about the use of time series to forecast the daily average water level of a hydrological station (Wang & Lou, 2019); the effects of adjuvant endocrine therapy on the health-related quality of life (Xiao et al, 2020); supply chains (Mircetic et al, 2022) and econometric approaches (Matta et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A nonlinear time-series prediction methodology based on neural networks and tracking signals

Bianchesi

Matta

Streitenberger

et al. 2022

Prod.

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Paper aims: This paper presents a nonlinear time series prediction methodology using Neural Networks and Tracking Signals method to detect bias and their responsiveness to non-random changes in the time series.Originality: This study contributes with an innovative approach of nonlinear time series prediction methodology. Furthermore, the Design of Experiments was applied to simulate datasets and to analyze the results of Average Run Length, identifying in which conditions the methodology is efficient.Research method: Datasets were generated to simulate different nonlinear time series by changing the error of the series. The methodology was applied to the datasets and the Design of Experiments was implemented to evaluate the results. Lastly, a case study based on total oil and grease was performed. Main findings:The results showed that the proposed prediction methodology is an effective way to detect bias in the process when an error is introduced in the nonlinear time series because the mean and the standard deviation of the error have a significant impact on the Average Run Length. Implications for theory and practice:This study contributes to a discussion about time series prediction methodology since this new technique could be widely used in several areas to improve forecast accuracy.

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Using time series analysis to forecast the health-related quality of life of post-menopausal women with non-metastatic ER+ breast cancer: A tutorial and case study

Cited by 6 publications

References 16 publications

Emotion Regulation Flexibility and Electronic Patient-Reported Outcomes: A Framework for Understanding Symptoms and Affect Dynamics in Pediatric Psycho-Oncology

Emotion Regulation Flexibility and Electronic Patient-Reported Outcomes: A Framework for Understanding Symptoms and Affect Dynamics in Pediatric Psycho-Oncology

Unraveling the Multifaceted Nature of Breast Cancer: Harnessing the Power of Machine Learning and Statistical Models for In-depth Investigation, Prognosis, and Treatment Optimization

A nonlinear time-series prediction methodology based on neural networks and tracking signals

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