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Regularity of a renewal process estimated from binary data.

ABSTRACT: Assessment of the regularity of a sequence of events over time is important for clinical decision-making as well as informing public health policy. Our motivating example involves determining the effect of an intervention on the regularity of HIV self-testing behavior among high-risk individuals when exact self-testing times are not recorded. Assuming that these unobserved testing times follow a renewal process, the goals of this work are to develop suitable methods for estimating its distributional parameters when only the presence or absence of at least one event per subject in each of several observation windows is recorded. We propose two approaches to estimation and inference: a likelihood-based discrete survival model using only time to first event; and a potentially more efficient quasi-likelihood approach based on the forward recurrence time distribution using all available data. Regularity is quantified and estimated by the coefficient of variation (CV) of the interevent time distribution. Focusing on the gamma renewal process, where the shape parameter of the corresponding interevent time distribution has a monotone relationship with its CV, we conduct simulation studies to evaluate the performance of the proposed methods. We then apply them to our motivating example, concluding that the use of text message reminders significantly improves the regularity of self-testing, but not its frequency. A discussion on interesting directions for further research is provided.

SUBMITTER: Rice JD

PROVIDER: S-EPMC7209979 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Regularity of a renewal process estimated from binary data.

Rice John D JD Strawderman Robert L RL Johnson Brent A BA

Biometrics 20171009 2

Assessment of the regularity of a sequence of events over time is important for clinical decision-making as well as informing public health policy. Our motivating example involves determining the effect of an intervention on the regularity of HIV self-testing behavior among high-risk individuals when exact self-testing times are not recorded. Assuming that these unobserved testing times follow a renewal process, the goals of this work are to develop suitable methods for estimating its distributi ...[more]

PMID: 28991366

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Project description:The "DOLPHINS" project started in 2018 under a collaboration between three partners: CNH Industrial Iveco (CHNi), RADA (an informatics company), and the Chemistry Department of the University of Turin. The project's main aim was to establish a predictive maintenance method in real-time at a pilot plant (CNHi Iveco, Brescia, Italy). This project currently allows maintenance technicians to intervene on machinery preventively, avoiding breakdowns or stops in the production process. For this purpose, several predictive maintenance models were tested starting from databases on programmable logic controllers (PLCs) already available, thus taking advantage of Machine Learning techniques without investing additional resources in purchasing or installing new sensors. The instrumentation and PLCs related to the truck sides' paneling phase were considered at the beginning of the project. The instrumentation under evaluation was equipped with sensors already connected to PLCs (only on/off switches, i.e., neither analog sensors nor continuous measurements are available, and the data are in sparse binary format) so that the data provided by PLCs were acquired in a binary way before being processed by multivariate data analysis (MDA) models. Several MDA approaches were tested (e.g., PCA, PLS-DA, SVM, XGBoost, and SIMCA) and validated in the plant (in terms of repeated double cross-validation strategies). The optimal approach currently used involves combining PCA and SIMCA models, whose performances are continuously monitored, and the various models are updated and tested weekly. Tuning the time range predictions enabled the shop floor and the maintenance operators to achieve sensitivity and specificity values higher than 90%, but the performance results are constantly improved since new data are collected daily. Furthermore, the information on where to carry out intervention is provided to the maintenance technicians between 30 min and 3 h before the breakdown.

Project description:ImportanceThe American Rescue Plan increases premium subsidies for health insurance marketplace enrollees, potentially leading to situations in which enrollees could switch to other health care plans with lower premiums and less cost sharing (ie, deductibles and copayments). Current policy defaults enrollees to their current health care plan if they automatically renew their coverage, which may cause them to stay in health care plans that, because of the American Rescue Plan, are now dominated in that they have higher premiums and cost sharing than other options.ObjectiveTo estimate the extent to which a smart default policy could reduce US health insurance marketplace enrollees' cost sharing and premiums.Design setting and participantsUsing 2018 individual enrollment data and 2021 premium data from California's marketplace and the American Rescue Plan premium tax credit subsidy schedule, this economic analysis estimated the characteristics of enrollees' default health care plans if they defaulted into 2021 health care plans under current and smart default policies. The analysis was conducted from March 20 to April 8, 2021.Main outcomes and measuresCharacteristics of enrollees' default health care plans under current and smart default policies, including net premiums, plan levels, and cost sharing.ResultsThe analytic sample consisted of 748 087 Covered California enrollees from 2018 (mean [SD] age, 44.80 [13.72] years; 408 410 [54.6%] women). Under current policy with the enhanced subsidies implemented under the American Rescue Plan, 5.8% of sample enrollees would default into dominated health plans. Of these enrollees, 98.0% would have incomes below 250% of the federal poverty level. A smart default policy would lead to a mean $102.47 decrease in monthly premiums (95% CI, $103.84-$101.10), a mean $1960 reduction in individual annual medical deductibles (95% CI, $1991-$1928), and a $49.56 reduction in specialty prescription copays (95% CI, $49.77-$49.34).Conclusions and relevanceThe findings of this economic analysis suggest that a smart default policy could avoid defaulting lower-income marketplace enrollees to objectively inferior health care insurance plans and may lead to large reductions in lower-income enrollees' deductibles, copayments, and maximum out-of-pocket amounts. Implementation of a smart default policy could enable marketplace administrators to reduce the prevalence of underinsurance among lower-income marketplace enrollees.

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Regularity of a renewal process estimated from binary data.

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Regularity of a renewal process estimated from binary data.

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