Species abundance distributions (SADs) follow one of ecologyÕs oldest and most universal laws -every community shows a hollow curve or hyperbolic shape on a histogram with many rare species and just a few common species. Here, we review theoretical, empirical and statistical developments in the study of SADs. Several key points emerge. (i) Literally dozens of models have been proposed to explain the hollow curve. Unfortunately, very few models are ever rejected, primarily because few theories make any predictions beyond the hollow-curve SAD itself. (ii) Interesting work has been performed both empirically and theoretically, which goes beyond the hollow-curve prediction to provide a rich variety of information about how SADs behave. These include the study of SADs along environmental gradients and theories that integrate SADs with other biodiversity patterns. Central to this body of work is an effort to move beyond treating the SAD in isolation and to integrate the SAD into its ecological context to enable making many predictions. (iii) Moving forward will entail understanding how sampling and scale affect SADs and developing statistical tools for describing and comparing SADs. We are optimistic that SADs can provide significant insights into basic and applied ecological science.

A B S T R A C T PurposeLittle is known about change in quality of life (QOL) among long-term cancer survivors. We examined change over time in QOL among long-term survivors of non-Hodgkin lymphoma and identified demographic, clinical, and psychosocial risk factors for poor outcomes. MethodsSurveys were mailed to 682 lymphoma survivors who participated in a study 5 years earlier, when on average they were 10.4 years postdiagnosis. Standardized measures of QOL, perceptions of the impact of cancer, symptoms, medical history, and demographic variables were reported at both time points and examined using linear regression modeling to identify predictors of QOL over time. ResultsA total of 566 individuals participated (83% response rate) who were a mean of 15.3 years postdiagnosis; 52% were women, and 87% were white. One third of participants (32%) reported persistently high or improved QOL, yet a notable proportion (42%) reported persistently low or worsening QOL since the earlier survey. Participants who received only biologic systemic therapy reported improvement in physical health despite the passage of time. Older age, more comorbidity, and more or increasing negative and decreasing positive perceptions of cancer's impact were independent predictors of poor QOL. Lymphoma symptom burden, less social support, and having received a transplantation were related to negative perceptions of cancer's impact. ConclusionModerate to severe symptom burden, limited social support, or having received a transplantation should alert the clinician to potential need for supportive services. Perceptions of cancer's impact are associated with QOL cross-sectionally and longitudinally; modifying these perceptions may thus provide a strategy for improving QOL.

A B S T R A C T PurposeLittle is known about the trajectory of post-traumatic stress disorder (PTSD) symptoms in cancer survivors, despite the fact that such knowledge can guide treatment. Therefore, this study examined changes in PTSD symptoms among long-term survivors of non-Hodgkin's lymphoma (NHL) and identified demographic, clinical, and psychosocial predictors and correlates of PTSD symptomatology. Patients and MethodsSurveys were mailed to 682 NHL survivors who participated in an earlier survey and now were at least 7 years postdiagnosis. Information was obtained regarding PTSD symptoms, positive and negative perceptions of the cancer experience (ie, impact of cancer), and other potential correlates of PTSD. ResultsA total of 566 individuals participated (83% response rate) with a median of 12.9 years since diagnosis; respondents were 52% female and 87% white. Although half (51%) of the respondents reported no PTSD symptoms and 12% reported a resolution of symptoms, more than one-third (37%) reported persistence or worsening of symptoms over 5 years. Survivors who reported a low income, stage Ն 2 at diagnosis, aggressive lymphoma, having received chemotherapy, and greater impact of cancer (both positive and negative) at the initial survey had more PTSD symptoms at follow-up. In multivariable analysis, income and negative impacts of cancer were independent predictors of PTSD symptoms. ConclusionMore than one-third of long-term NHL survivors experience persisting or worsening PTSD symptoms. Providers should be aware of enduring risk; early identification of those at prolonged risk with standardized measures and treatments that target perceptions of the cancer experience might improve long-term outcomes.

Background The objective of this study was to determine whether non-Hodgkin’s lymphoma survivors are meeting select American Cancer Society (ACS) health-related guidelines for cancer survivors, as well as to examine relationships between these lifestyle factors and health- related quality of life (HRQoL) and post-traumatic stress (PTS). Methods A cross-sectional sample of 566 NHL survivors was identified from the tumor registries of two large academic medical centers. Respondents were surveyed about physical activity, fruit and vegetable intake, body weight, tobacco use, HRQoL using the Medical Outcomes Study Short Form-36 and post-traumatic stress using the Post-traumatic Stress Disorder Checklist-Civilian form. Lifestyle cluster scores were generated based on whether individuals met health guidelines and multiple linear regression was used to evaluate relationships between lifestyle behaviors and HRQoL scores and PTS scores. Results 11% of participants met all four ACS health recommendations. Meeting all four healthy recommendations was related to better physical and mental quality of life (β = 0.57, p <0.0001; β = 0.47, p = 0.002) and to lower PTS scores (β = −0.41, p = 0.01). Conclusions NHL survivors who met more ACS health-related guidelines appeared to have better HRQoL and less PTS. Unfortunately many survivors are not meeting these guidelines, which could impact their overall well-being and longevity.

AimThe goal of this study was to identify progressing periodontal sites by applying linear mixed models (LMM) to longitudinal measurements of clinical attachment loss (CAL).MethodsNinety‐three periodontally healthy and 236 periodontitis subjects had their CAL measured bi‐monthly for 12 months. The proportions of sites demonstrating increases in CAL from baseline above specified thresholds were calculated for each visit. The proportions of sites reversing from the progressing state were also computed. LMM were fitted for each tooth site and the predicted CAL levels used to categorize sites regarding progression or regression. The threshold for progression was established based on the model‐estimated error in predictions.ResultsOver 12 months, 21.2%, 2.8% and 0.3% of sites progressed, according to thresholds of 1, 2 and 3 mm of CAL increase. However, on average, 42.0%, 64.4% and 77.7% of progressing sites for the different thresholds reversed in subsequent visits. Conversely, 97.1%, 76.9% and 23.1% of sites classified as progressing using LMM had observed CAL increases above 1, 2 and 3 mm after 12 months, whereas mean rates of reversal were 10.6%, 30.2% and 53.0% respectively.Conclusion LMM accounted for several sources of error in longitudinal CAL measurement, providing an improved method for classifying progressing sites.

Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren.

Purpose People living in rural areas experience greater health disparities than their nonrural counterparts, but little is known about the association between rural status and quality of life (QOL) in non‐Hodgkin's lymphoma (NHL) survivors. We compared self‐reported quality of life and impact of cancer in rural and nonrural NHL survivors. Methods This study is a secondary analysis of 566 NHL cancer survivors recruited from cancer registries at 2 large academic medical centers in 1 state. Standardized measures collected information on demographics and clinical characteristics, quality of life (QOL; SF‐36), and the Impact of Cancer (IOCv2). Rural residence was determined by Rural‐Urban Commuting Area (RUCA) codes designated as nonmetropolitan. Multiple linear regression analysis, adjusted for demographic and clinical covariates, was used to evaluate the relationship between rural residence and QOL and impact of cancer. Findings Among the 566 participants (83% response rate), rural residence was independently associated with lower SF‐36 physical component summary scores and the physical function subscale (all P < .05). Rural residence was also associated with higher IOCv2 positive impact scores and the subscales of altruism/empathy and meaning of cancer scores in the adjusted models (all P < .05). Conclusions Given documented rural cancer disparities and the lack of resources in rural communities, study findings support the continued need to provide supportive care to rural cancer survivors to improve their QOL. Consistent with previous research, rural residence status is associated with increased positive impact following cancer diagnosis.

Mixture distributions provide flexibility in modeling data collected from populations having unexplained heterogeneity. While interpretations of regression parameters from traditional finite mixture models are specific to unobserved subpopulations or latent classes, investigators are often interested in making inferences about the marginal mean of a count variable in the overall population. Recently, marginal mean regression modeling procedures for zero-inflated count outcomes have been introduced within the framework of maximum likelihood estimation of zero-inflated Poisson and negative binomial regression models. In this article, we propose marginalized mixture regression models based on two-component mixtures of non-degenerate count data distributions that provide directly interpretable estimates of exposure effects on the overall population mean of a count outcome. The models are examined using simulations and applied to two datasets, one from a double-blind dental caries incidence trial, and the other from a horticultural experiment. The finite sample performance of the proposed models are compared with each other and with marginalized zero-inflated count models, as well as ordinary Poisson and negative binomial regression.Electronic supplementary materialThe online version of this article (doi:10.1186/s40488-017-0057-4) contains supplementary material, which is available to authorized users.