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Novel robust time series analysis for long-term and short-term prediction.

ABSTRACT: Nonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner-recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

SUBMITTER: Okamura H

PROVIDER: S-EPMC8184922 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundTime-series analysis with case-only data is a prominent method for the effect of environmental determinants on disease events in environmental epidemiology. In this analysis, adjustment for seasonality and long-term time-trend is crucial to obtain valid findings. When applying this analysis for long-term exposure (e.g., months, years) of which effects are usually studied via survival analysis with individual-level longitudinal data, unlike its application for short-term exposure (e.g., days, weeks), a standard adjustment method for seasonality and long-term time-trend can extremely inflate standard error of coefficient estimates of the effects. Given that individual-level longitudinal data are difficult to construct and often available to limited populations, if this inflation of standard error can be solved, rich case-only data over regions and countries would be very useful to test a variety of research hypotheses considering unique local contexts.MethodsWe discuss adjustment methods for seasonality and time-trend used in time-series analysis in environmental epidemiology and explain why standard errors can be inflated. We suggest alternative methods to solve this problem. We conduct simulation analyses based on real data for Seoul, South Korea, 2002-2013, and time-series analysis using real data for seven major South Korean cities, 2006-2013 to identify whether the association between long-term exposure and health outcomes can be estimated via time-series analysis with alternative adjustment methods.ResultsSimulation analyses and real-data analysis confirmed that frequently used adjustment methods such as a spline function of a variable representing time extremely inflate standard errors of estimates for associations between long-term exposure and health outcomes. Instead, alternative methods such as a combination of functions of variables representing time can make sufficient adjustment with efficiency.ConclusionsOur findings suggest that time-series analysis with case-only data can be applied for estimating long-term exposure effects. Rich case-only data such as death certificates and hospitalization records combined with repeated measurements of environmental determinants across countries would have high potentials for investigating the effects of long-term exposure on health outcomes allowing for unique contexts of local populations.

Project description:BackgroundMacrolides are recommended as an adjunctive treatment for patients with moderate to severe chronic obstructive pulmonary disease (COPD) who experience recurrent exacerbations. The objective of this study was to examine temporal trends in the provision of long-term macrolide therapy, specifically before and after publication of the landmark MACRO trial in August 2011 showing efficacy of macrolides for this indication.MethodsWe performed an interrupted time series analysis using population-level health administrative data. The study cohort consisted of all Ontario residents who had COPD, were using at least 1 long-acting inhaler, and were aged 65 years and older between Apr. 1, 2004, and Mar. 31, 2018. We compared the baseline characteristics of eligible patients before and after publication of the MACRO trial. Our primary outcome was overall prevalence of long-term macrolide therapy; secondary outcomes were incidence of COPD-related hospitalizations, emergency department visits and outpatient exacerbations requiring high-dose steroids in each quarter. We performed an interrupted time series analysis to assess for changes in the incidence of macrolide prophylaxis by quarter-year over the study period.ResultsThe rate of long-term macrolide use increased from 0.8 per 1000 people in 2004 to 13.8 per 1000 people in 2018 (in the severe COPD group, the rate increased from 1.3 to 32.3 per 1000 people). The interrupted time series analysis showed that, before 2011, the prevalence of macrolide prophylaxis increased at a rate of 0.44 (95% confidence interval [CI] 0.39-0.50) per 1000 people per year; after 2011, the rate of increase grew by 1.18 (95% CI 1.07-1.29) per 1000 people to 1.63 (95% CI 1.56-1.69) per 1000 people per year. The seasonal pattern of COPD-related health care visits remained stable over the study period, and there was no detectable reduction in hospitalizations or emergency department visits at the population level.InterpretationIn the past decade, there has been a significant rise in the use of long-term macrolide therapy for patients with COPD. As this practice becomes increasingly common, it will be important to monitor its potential benefits on COPD exacerbations but also its potential effects on adverse events and antimicrobial resistance patterns.

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Novel robust time series analysis for long-term and short-term prediction.

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