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Purpose Chemotherapy-induced peripheral neuropathy (CIPN) may necessitate chemotherapy dose reduction, delay, or discontinuation. This pilot study tested feasibility of patient enrollment, CIPN screening, and data collection in cancer patients for a future clinical study that will assess the safety and efficacy of an intervention that may prevent CIPN. Methods This prospective, observational, single-center, pilot study included adults with newly diagnosed lymphoma or multiple myeloma receiving neurotoxic chemotherapy. Patients were enrolled between September 2016 and February 2017. The Functional Assessment of Cancer Therapy/Gynecologic Oncology Group-Neurotoxicity (FACT/GOG-Ntx) questionnaire was completed by patients at 3 time points: baseline, week 6, and week 12. The primary outcome was change in the neurotoxicity score between these time points. Results Of 33 patients approached for consent, 28 (85%) provided consent and were enrolled. The FACT/GOG-Ntx questionnaire was completed by 28 (100%) at baseline, 25 (89%) at week 6, and 24 (86%) at week 12. Average (standard deviation) neurotoxicity scores were 36.5 (6.6) at baseline, 34.0 (8.3) at week 6, and 30.6 (7.6) at week 12. Neurotoxicity scores changed from baseline by − 2.7 points (95% CI − 5.5 to 0.1; p  = 0.061) at week 6 and − 6.0 points (95% CI − 5.6 to − 0.8; p  = 0.012) at week 12. Clinically meaningful declines (decrease of > 10% from baseline) in neurotoxicity score were detected in 36% (9 of 25) at week 6 and in 67% (16 of 24) at week 12. Conclusion Sixty-seven percent of patients experienced clinically significant CIPN within 12 weeks of starting chemotherapy. Feasibility metrics for enrollment, consent, CIPN assessment, and follow-up were met.

Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated-Gaussian reference function [AKDE], Silverman's rule of thumb, and least squares cross-validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half-sample cross-validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation (N̂area) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID-based estimates by a mean factor of 2. The median number of cross-validated locations included in the hold-out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing N̂area. To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small N̂area. While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an N̂area >1,000, where 30% had an N̂area <30. In this frequently encountered scenario of small N̂area, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.

Fil: Alberts, Susan C.. University of Duke; Estados Unidos

Quantifying animal movements is necessary for answering a wide array of research questions in ecology and conservation biology. Consequently, ecologists have made considerable efforts to identify the best way to estimate an animal's home range, and many methods of estimating home ranges have arisen over the past half century. Most of these methods fall into two distinct categories of estimators that have only recently been described in statistical detail: those that measure range distributions (methods such as Kernel Density Estimation that quantify the long-run behavior of a movement process that features restricted space use) and those that measure occurrence distributions (methods such as Brownian Bridge Movement Models and the Correlated Random Walk Library that quantify uncertainty in an animal movement path during a specific period of observation). In this paper, we use theory, simulations, and empirical analysis to demonstrate the importance of applying these two classes of space use estimators appropriately and distinctly. Conflating range and occurrence distributions can have serious consequences for ecological inference and conservation practice. For example, in most situations, home-range estimates quantified using occurrence estimators are too small, and this problem is exacerbated by ongoing improvements in tracking technology that enable more frequent and more accurate data on animal movements. We encourage researchers to use range estimators to estimate the area of home ranges and occurrence estimators to answer other questions in movement ecology, such as when and where an animal crosses a linear feature, visits a location of interest, or interacts with other animals. Competing Interest Statement The authors have declared no competing interest.