Application of NSGA-II to Obtain the Charging Current-Time Tradeoff Curve in Battery Based Underwater Wireless Sensor Nodes

In this paper, a novel application of the Nondominated Sorting Genetic Algorithm II (NSGA II) is presented for obtaining the charging current–time tradeoff curve in battery based underwater wireless sensor nodes. The selection of the optimal charging current and times is a common optimization problem. A high charging current ensures a fast charging time. However, it increases the maximum power consumption and also the cost and complexity of the power supply sources. This research studies the tradeoff curve between charging currents and times in detail. The design exploration methodology is based on a two nested loop search strategy. The external loop determines the optimal design solutions which fulfill the designers’ requirements using parameters like the sensor node measurement period, power consumption, and battery voltages. The inner loop executes a local search within working ranges using an evolutionary multi-objective strategy. The experiments proposed are used to obtain the charging current–time tradeoff curve and to exhibit the accuracy of the optimal design solutions. The exploration methodology presented is compared with a bisection search strategy. From the results, it can be concluded that our approach is at least four times better in terms of computational effort than a bisection search strategy. In terms of power consumption, the presented methodology reduced the required power at least 3.3 dB in worst case scenarios tested.

Pilot Study of Patients’ Preferences for Immediate Resection Versus a Watch and Wait Approach After Neoadjuvant Chemoradiation for Locally Advanced Rectal Cancer

PURPOSE: In patients with rectal cancer who achieve a clinical complete response to neoadjuvant chemoradiation, it may be reasonable to adopt a watch-and-wait (W&W) strategy rather than proceed to immediate resection of the rectum. Patient preferences for this strategy are unknown. The primary aim of the current study was to determine the feasibility of assessing hypothetical recurrence and survival differences that relevant patients would tolerate to avoid immediate resection of the rectum. A secondary aim included estimating patients’ tolerance thresholds and the factors that might predict them. METHODS: We developed a study-specific written questionnaire based on a previously validated instrument. Hypothetical time tradeoff tasks were used to determine the recurrence rate patients would accept to adopt a W&W strategy and the survival benefit that would be needed to justify choosing immediate resection over W&W. Feasibility was measured on the basis of response rate, the stated ease of completion and the satisfaction of task, and time used. RESULTS: Twenty of 31 potentially eligible patients completed the study-specific questionnaire. The majority of respondents felt that questions were clear (70%) and not hard to understand (65%). The median acceptable recurrence risk to adopt a W&W strategy was 20% (interquartile range [IQR], 10%-35%). Patients required a median of 2.0 extra years of survival (IQR, 1.0-3.0 years) over a baseline 7.0 years, and they required a median extra 10% (IQR, 4%-19%) over baseline 70% survival rates to justify immediate resection. CONCLUSION: Measuring the preferences of patients with rectal cancer using time tradeoff methods seemed to be feasible. Larger studies are needed to confirm how acceptable a W&W strategy would be for relevant patients.

Using Both Time Tradeoff and Discrete Choice Experiments in Valuing the EQ-5D: Impact of Model Misspecification on Value Sets

Introduction. The EQ-5D-5L valuation protocol contains both time tradeoff (TTO) tasks and discrete choice experiments (DCE), raising the question of how to best use these in creating a value set. The hybrid model, which combines TTO and DCE data, has emerged as a commonly used approach. However, this model assumes independence among responses from the same individual, a linear relationship between TTO and DCE utilities, and, in many implementations, homoscedastic residuals. The aims of this study are to examine alternatives to these assumptions and determine the impact of misspecification on value sets. Methods. We performed a simulation study, parameterized using the US EQ-5D-5L valuation study, to assess the impact of model misspecification. We simulated TTO and DCE data with nonlinear relationships between TTO and DCE utilities, heteroscedastic errors, and correlated responses. Simulated data were analyzed using hybrid models with and without heteroscedasticity, Tobit models with and without heteroscedasticity, a latent class model, and a mixed model. Results. Mean absolute errors (MAEs) for correctly specified models were <0.05, whereas models that incorrectly assumed a linear relationship between TTO and DCE utilities or homoscedasticity of TTO responses featured states with an MAE >0.1. When a linear relationship between TTO and DCE utilities held, using both TTO and DCE data under correct specification yielded smaller MAEs compared with using TTO data alone but yielded larger MAEs when a linear relationship did not hold. Mistakenly assuming homoscedasticity led to increased MAEs, whereas ignoring dependence did not. Conclusions. Because heteroscedasticity in TTO utilities and nonlinear associations between DCE and TTO utilities have been noted, we recommend careful assessment of scedasticity and linearity to ascertain the suitability of a hybrid model.

Comparison of Rating Scale, Time Tradeoff, and Conjoint Analysis Methods for Assessment of Preferences in Prostate Cancer

Background. Conjoint analysis is widely used in studies of consumer preference but has only recently been applied to measure patient utilities for health outcomes. We compared the reliability, feasibility, and internal and predictive validity of conjoint scaling methods against better established rating scale and time tradeoff methods for assessing prostate cancer utilities in men at risk for prostate cancer. Methods. In total, 194 men who were biopsy negative for prostate cancer were randomly assigned to complete 2 preference assessment modules, either conjoint analysis and a rating scale module or conjoint analysis and a time tradeoff module. Each participant’s most important attribute was identified and evaluated in relation to age group (age <65, age 65 and older), education (high school, some college, college graduate), race/ethnicity (white, black, Latino), and relationship status (in significant relationship v. not). The methods were also evaluated in terms of ease of use and satisfaction. Results. Rating scales were rated as easiest to use and respondents were more satisfied with rating scales and conjoint in comparison to time tradeoffs. Rating scales and conjoint measures demonstrated significantly higher internal validity compared to time tradeoff when evaluated through R2 of the fitted utility function. The 3 methods were similar in terms of predictive validity, but conjoint analysis outperformed the rating scale method when patients were presented with novel combinations of attribute levels (68% correct v. 43%, P = 0.003). Conclusions. Rating scales and conjoint analysis exercises offer greater ease of use and higher satisfaction when measuring patient preferences in men biopsied for prostate cancer in comparison to time tradeoff exercises. Conjoint analysis may be a more robust approach to preference measurement for men at risk for prostate cancer.