Fitting population dynamic models to timeseries data by gradient matching.

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Fitting population dynamic models to time-series data by gradient matching

We describe and test a method for fitting noisy differential equation models to a time series of population counts, motivated by stage-structured models of insect and zooplankton populations. We consider semimechanistic models, in which the model structure is derived from knowledge of the life cycle, but the rate equations are estimated nonparametrically from the time-series data. The method involves smoothing the population time series x(t) in order to estimate the gradient dx/dt, and then fitting rate equations using penalized regression splines. Computer-intensive methods are used to estimate and remove the biases that result from the data being discrete time samples with sampling errors from a continuous time process. Semimechanistic modeling makes it possible to test assumptions about the mechanisms behind population fluctuations without the results being confounded by possibly arbitrary choices of parametric forms for process-rate equations. To illustrate this application, we analyze time-series data on laboratory populations of blowflies Lucilia cuprina and Lucilia sericata. The models assume that the populations are limited by competition among adults affecting their current birth and death rates. The results correspond to the actual experimental conditions. For L. cuprina (where the model's structure is appropriate) a good fit can be obtained, while for L. sericata (where the model is inappropriate), the fitted model does not reproduce some major features of the observed cycles. A documented set of R functions for all steps in the model-fitting process is provided as a supplement to this article

Semiparametric Bayesian inference for regression models

This paper presents a method for Bayesian inference for the regression parameters in a linear model with independent and identically distributed errors that does not require the specification of a parametric family of densities for the error distribution. This method first selects a nonparametric kernel density estimate of the error distribution which is unimodal and based on the least-squares residuals. Once the error distribution is selected, the Metropolis algorithm is used to obtain the marginal posterior distribution of the regression parameters. The methodology is illustrated with data sets, and its performance relative to standard Bayesian techniques is evaluated using simulation results. R ESUM E Les auteurs presentent une methode d'inference bayesienne pour les parametres de regression d'un modele lineaire dans le cas ou les erreurs forment un echantillon aleatoire d'une loi non precisee. Cette technique consiste a trouver d'abord une estimation non parametrique unimodale de..

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Efficacy and safety of a ready-to-drink bowel preparation for colonoscopy: a randomized, controlled, non-inferiority trial

Background: We performed a randomized, controlled, assessor-blinded, multicenter, non-inferiority (NI) study to compare the safety and efficacy of a ready-to-drink formulation of sodium picosulfate, magnesium oxide, and citric acid (SPMC oral solution) with a powder formulation (P/MC powder) for oral solution. Methods: Eligible participants (adults undergoing elective colonoscopy) were randomized 1:1 to split-dose SPMC oral solution or P/MC powder. The primary efficacy endpoint assessed overall colon-cleansing quality with the Aronchick Scale (AS), and the key secondary efficacy endpoint rated quality of right colon cleansing with the Boston Bowel Preparation Scale (BBPS). Assessments were performed by a treatment-blinded endoscopist. Tolerability was assessed using the Mayo Clinic Bowel Prep Tolerability Questionnaire. Safety assessments included adverse events and laboratory evaluations. Results: The study included 901 participants: 448 for SPMC oral solution; 453 for P/MC powder. SPMC oral solution demonstrated non-inferiority to P/MC powder {87.7% (393/448) responders versus 81.5% (369/453) responders [difference (95% confidence interval): 6.3% (1.8, 10.9)]}. The key secondary efficacy objective assessing the right colon was also met. According to the prespecified hierarchical testing, after meeting the primary and key secondary objectives, SPMC oral solution was tested for superiority to P/MC powder for the primary endpoint ( p = 0.0067). SPMC oral solution was well tolerated. Most common adverse events were nausea (3.1% versus 2.9%), headache (2.7% versus 3.1%), hypermagnesemia (2.0% versus 5.1%), and vomiting (1.3% versus 0.7%) for SPMC oral solution and P/MC powder, respectively. Conclusions: Ready-to-drink SPMC oral solution showed superior efficacy of overall colon cleansing compared with P/MC powder, with similar safety and tolerability. [ClinicalTrials.gov identifier: NCT03017235.

The win ratio: Impact of censoring and follow-up time and use with nonproportional hazards

The win ratio has been studied methodologically and applied in data analysis and in designing clinical trials. Researchers have pointed out that the results depend on follow-up time and censoring time, which are sometimes used interchangeably. In this article, we distinguish between follow-up time and censoring time, show theoretically the impact of censoring on the win ratio, and illustrate the impact of follow-up time. We then point out that, if the treatment has long-term benefit from a more important but less frequent endpoint (eg, death), the win ratio can show that benefit by following patients longer, avoiding masking by more frequent but less important outcomes, which occurs in conventional time-to-first-event analyses. For the situation of nonproportional hazards, we demonstrate that the win ratio can be a good alternative to methods such as landmark survival rate, restricted mean survival time, and weighted log-rank tests