Thierry Duchesne

WOLVES INFLUENCE ELK MOVEMENTS: BEHAVIOR SHAPES A TROPHIC CASCADE IN YELLOWSTONE NATIONAL PARK

A trophic cascade recently has been reported among wolves, elk, and aspen on the northern winter range of Yellowstone National Park, Wyoming, USA, but the mechanisms of indirect interactions within this food chain have yet to be established. We investigated whether the observed trophic cascade might have a behavioral basis by exploring environmental factors influencing the movements of 13 female elk equipped with GPS radio collars. We developed a simple statistical approach that can unveil the concurrent influence of several environmental features on animal movements. Paths of elk traveling on their winter range were broken down into steps, which correspond to the straight-line segment between successive locations at 5-hour intervals. Each observed step was paired with 200 random steps having the same starting point, but differing in length and/or direction. Comparisons between the characteristics of observed and random steps using conditional logistic regression were used to model environmental features influencing movement patterns. We found that elk movements were influenced by multiple factors, such as the distance from roads, the presence of a steep slope along the step, and the cover type in which they ended. The influence of cover type on elk movements depended on the spatial distribution of wolves across the northern winter range of the park. In low wolf-use areas, the relative preference for end point locations of steps followed: aspen stands > open areas > conifer forests. As the risks of wolf encounter increased, the preference of elk for aspen stands gradually decreased, and selection became strongest for steps ending in conifer forests in high wolf-use areas. Our study clarifies the behavioral mechanisms involved in the trophic cascade of Yellowstones wolf-elk-aspen system: elk respond to wolves on their winter range by a shift in habitat selection, which leads to local reductions in the use of aspen by elk.

Mixed conditional logistic regression for habitat selection studies

1. Resource selection functions (RSFs) are becoming a dominant tool in habitat selection studies. RSF coefficients can be estimated with unconditional (standard) and conditional logistic regressions. While the advantage of mixed-effects models is recognized for standard logistic regression, mixed conditional logistic regression remains largely overlooked in ecological studies. 2. We demonstrate the significance of mixed conditional logistic regression for habitat selection studies. First, we use spatially explicit models to illustrate how mixed-effects RSFs can be useful in the presence of inter-individual heterogeneity in selection and when the assumption of independence from irrelevant alternatives (IIA) is violated. The IIA hypothesis states that the strength of preference for habitat type A over habitat type B does not depend on the other habitat types also available. Secondly, we demonstrate the significance of mixed-effects models to evaluate habitat selection of free-ranging bison Bison bison. 3. When movement rules were homogeneous among individuals and the IIA assumption was respected, fixed-effects RSFs adequately described habitat selection by simulated animals. In situations violating the inter-individual homogeneity and IIA assumptions, however, RSFs were best estimated with mixed-effects regressions, and fixed-effects models could even provide faulty conclusions. 4. Mixed-effects models indicate that bison did not select farmlands, but exhibited strong inter-individual variations in their response to farmlands. Less than half of the bison preferred farmlands over forests. Conversely, the fixed-effect model simply suggested an overall selection for farmlands. 5. Conditional logistic regression is recognized as a powerful approach to evaluate habitat selection when resource availability changes. This regression is increasingly used in ecological studies, but almost exclusively in the context of fixed-effects models. Fitness maximization can imply differences in trade-offs among individuals, which can yield inter-individual differences in selection and lead to departure from IIA. These situations are best modelled with mixed-effects models. Mixed-effects conditional logistic regression should become a valuable tool for ecological research.

Serum Prognostic Markers in Head and Neck Cancer

Purpose: Recognized prognostic factors do not adequately predict outcomes of head and neck cancer (HNC) patients after their initial treatment. We identified from the literature nine potential serum prognostic markers and assessed whether they improve outcome prediction. Experimental Design: A pretreatment serum sample was obtained from 527 of the 540 HNC patients who participated in a randomized controlled trial. During follow-up, 115 had a HNC recurrence, 110 had a second primary cancer (SPC), and 216 died. We measured nine potential serum prognostic markers: prolactin, soluble interleukin-2 (IL-2) receptor-α, vascular endothelial growth factor, IL-6, squamous cell carcinoma antigen, free β-human choriogonadotropin, insulin-like growth factor-I, insulin-like growth factor binding protein-3, and soluble epidermal growth factor receptor. Cox regression was used to identify a reference predictive model for (a) HNC recurrence, (b) SPC incidence, and (c) overall mortality. Each serum marker was added in turn to these reference models to determine by the likelihood ratio test whether it significantly improved outcome prediction. We controlled for the false discovery rate that results from multiple testing. Results: IL-6 was the only serum marker that significantly improved outcome prediction. Higher levels of IL-6 were associated with a higher SPC incidence. The hazard ratio comparing the uppermost quartile to the lowest quartile of IL-6 was 2.68 (95% confidence interval, 1.49-4.08). IL-6 was also associated with SPC-specific mortality but not with mortality due to other causes. No marker improved outcome prediction for cancer recurrence or overall mortality. Conclusions: IL-6 significantly improves outcome prediction for SPC in HNC patients. Clin Cancer Res; 16(3); 1008–15

Identification of Cancer Care and Protocol Characteristics Associated With Recruitment in Breast Cancer Clinical Trials

PURPOSE It is estimated that only 5% of patients with cancer participate in a clinical trial. Barriers to participation may relate to available protocols, physicians, and patients, but few data exist on barriers related to cancer care environments and protocol characteristics. METHODS The primary objective was to identify characteristics of cancer care environments and clinical trial protocols associated with a low recruitment into breast cancer clinical trials. Secondary objectives were to determine yearly recruitment fraction onto clinical trials from 1997 to 2002 in Ontario, Canada, and to compare recruitment fraction among years. Questionnaires were sent to hospitals requesting characteristics of cancer care environments and to cooperative groups/pharmaceutical companies for information on protocols and the number of patients recruited per hospital/year. Poisson regression was used to estimate the recruitment fraction. RESULTS Questionnaire completion rate varied between 69% and 100%. Recruitment fraction varied between 5.4% and 8.5% according to year. More than 30% of patients were diagnosed in hospitals with no available trials. In multivariate analysis, the following characteristics were associated with recruitment: use of placebo versus not (relative risk [RR] = 0.80; P = .05), nonmetastatic versus metastatic trial (RR = 2.80; P < .01), and for nonmetastatic trials, protocol allowing an interval of 12 weeks or longer versus less than 12 weeks (from diagnosis, surgery, or end of therapy) before enrollment (RR = 1.36; P < .01). CONCLUSION Allowable interval of 12 weeks or longer to randomly assign patients in clinical trials could help recruitment. In our study, absence of an available clinical trial represented the largest barrier to recruitment.

Criminal Trajectories and Risk Factors in a Canadian Sample of Offenders

This study contributed to the criminal trajectory literature using a Canadian-based sample of offenders and examined childhood and adolescent predictors of trajectory group membership. The sample comprised 378 males who had been sentenced as youth, between 1986 and 1996, to one of two open custody facilities in Toronto, Canada. Official criminal records were obtained from late childhood and early adolescence into adulthood for an average follow-up of 12.1 years. Childhood and adolescent predictors reflecting individual, family, peer, and school domains were extracted from client files. Trajectory analysis yielded four groups, labeled moderate rate (MR); low rate (LR); high-rate, adult peaked (HRADL); and high-rate, adolescence peaked (HRADOL). Multinomial regression analyses indicated that risk factors representing the family and peer domains differentiated the MR, HRADL, and HRADOL groups from the LR group. Moreover, whereas both child and adolescent risk factors were associated with the MR, HRADL, and HRADOL groups, only adolescent risk factors were associated with the LR group.

Redefining the maximum sustainable yield for the Schaefer population model including multiplicative environmental noise

The focus of this article is to investigate the biological reference points, such as the maximum sustainable yield (MSY), in a common Schaefer (logistic) surplus production model in the presence of a multiplicative environmental noise. This type of model is used in fisheries stock assessment as a first-hand tool for biomass modelling. Under the assumption that catches are proportional to the biomass, we derive new conditions on the environmental noise distribution such that stationarity exists and extinction is avoided. We then get new explicit results about the stationary behavior of the biomass distribution for a particular specification of the noise, namely the biomass distribution itself and a redefinition of the MSY and related quantities that now depend on the value of the variance of the noise. Consequently, we obtain a more precise vision of how less optimistic the stochastic version of the MSY can be than the traditionally used (deterministic) MSY. In addition, we give empirical conditions on the error variance to approximate our specific noise by a lognormal noise, the latter being more natural and leading to easier inference in this context. These conditions are mild enough to make the explicit results of this paper valid in a number of practical applications. The outcomes of two case-studies about northwest Atlantic haddock [Spencer, P.D., Collie, J.S., 1997. Effect of nonlinear predation rates on rebuilding the Georges Bank haddock (Melanogrammus aeglefinus) stock. Can. J. Fish. Aquat. Sci. 54, 2920-2929] and South Atlantic albacore tuna [Millar, R.B., Meyer, R., 2000. Non-linear state space modelling of fisheries biomass dynamics by using Metropolis-Hastings within-Gibbs sampling. Appl. Stat. 49, 327-342] are used to illustrate the impact of our results in bioeconomic terms.

Equivalence between Step Selection Functions and Biased Correlated Random Walks for Statistical Inference on Animal Movement.

Animal movement has a fundamental impact on population and community structure and dynamics. Biased correlated random walks (BCRW) and step selection functions (SSF) are commonly used to study movements. Because no studies have contrasted the parameters and the statistical properties of their estimators for models constructed under these two Lagrangian approaches, it remains unclear whether or not they allow for similar inference. First, we used the Weak Law of Large Numbers to demonstrate that the log-likelihood function for estimating the parameters of BCRW models can be approximated by the log-likelihood of SSFs. Second, we illustrated the link between the two approaches by fitting BCRW with maximum likelihood and with SSF to simulated movement data in virtual environments and to the trajectory of bison (Bison bison L.) trails in natural landscapes. Using simulated and empirical data, we found that the parameters of a BCRW estimated directly from maximum likelihood and by fitting an SSF were remarkably similar. Movement analysis is increasingly used as a tool for understanding the influence of landscape properties on animal distribution. In the rapidly developing field of movement ecology, management and conservation biologists must decide which method they should implement to accurately assess the determinants of animal movement. We showed that BCRW and SSF can provide similar insights into the environmental features influencing animal movements. Both techniques have advantages. BCRW has already been extended to allow for multi-state modeling. Unlike BCRW, however, SSF can be estimated using most statistical packages, it can simultaneously evaluate habitat selection and movement biases, and can easily integrate a large number of movement taxes at multiple scales. SSF thus offers a simple, yet effective, statistical technique to identify movement taxis.

A two-stage, single-arm, phase II study of EGCG-enriched green tea drink as a maintenance therapy in women with advanced stage ovarian cancer

OBJECTIVES A two-stage, single-arm, phase II study was conducted to assess the effectiveness and safety of an epigallocatechin gallate (EGCG)-enriched tea drink, the double-brewed green tea (DBGT), as a maintenance treatment in women with advanced stage serous or endometrioid ovarian cancer (clinicaltrials.gov, NCT00721890). METHODS Eligible women had FIGO stage III-IV serous or endometrioid ovarian cancer. They had to undergo complete response after debulking surgery followed by 6 to 8 cycles of platinum/taxane chemotherapy at the Centre Hospitalier Universitaire de Québec. They all had to drink the DBGT, 500 mL daily until recurrence or during a follow-up of 18 months. The primary endpoint was the absence of recurrence at 18 months. Statistical analyses were done according to the principle of intention to treat. Using a two-stage design, the first stage consisted of 16 enrolled patients. At the end of the follow-up, if 7 or fewer patients were free of recurrence, the trial stopped. Otherwise, accrual would continue to a total of 46 patients. RESULTS During the first stage of the study, only 5 of the 16 women remained free of recurrence 18 months after complete response. Accordingly, the clinical trial was terminated. Womens adherence to DBGT was high (median daily intake during intervention, 98.1%, interquartile range: 89.7-100%), but 6 women discontinued the intervention before the end of their follow-up. No severe toxicity was reported. CONCLUSIONS DBGT supplementation does not appear to be a promising maintenance intervention in women with advanced stage ovarian cancer after standard treatment.

Semiparametric Inference Methods for General Time Scale Models

In this paper we consider semiparametric inference methods for the time scale parameters in general time scale models (Oakes, 1995, Duchesne and Lawless, 2000). We use the results of Robins and Tsiatis (1992) and Lin and Ying (1995) to derive a rank-based estimator that is more efficient and robust than the traditional minimum coefficient of variation (min CV) estimator of Kordonsky and Gerstbakh (1993) for many underlying models. Moreover, our estimator can readily handle censored samples, which is not the case with the min CV method.

On the collapsibility of lifetime regression models

In this paper we derive conditions on the internal wear process under which the resulting time to failure model will be of the simple collapsible form when the usage accumulation history is available. We suppose that failure occurs when internal wear crosses a certain threshold or a traumatic event causes the item to fail. We model the infinitesimal increment in internal wear as a function of time, accumulated internal wear, and usage history, and we derive conditions on this function to get a collapsible model for the distribution of time to failure given the usage history. We reach the conclusion that collapsible models form the subset of accelerated failure time models with time-varying covariates for which the time transformation function satisfies certain simple properties.