Bootstrap approaches for estimation and confidence intervals of long term memory processes

In this work, we investigate an alternative bootstrap approach based on a result of Ramsey [F.L. Ramsey, Characterization of the partial autocorrelation function, Ann. Statist. 2 (1974), pp. 1296-1301] and on the Durbin-Levinson algorithm to obtain a surrogate series from linear Gaussian processes with long range dependence. We compare this bootstrap method with other existing procedures in a wide Monte Carlo experiment by estimating, parametrically and semi-parametrically, the memory parameter d. We consider Gaussian and non-Gaussian processes to prove the robustness of the method to deviations from normality. The approach is also useful to estimate confidence intervals for the memory parameter d by improving the coverage level of the interval.

Exact parametric confidence intervals for Bland-Altman limits of agreement

Purpose The previous literature on Bland-Altman analysis only describes approximate methods for calculating confidence intervals for 95% Limits of Agreement (LoAs). This paper describes exact methods for calculating such confidence intervals, based on the assumption that differences in measurement pairs are normally distributed. Methods Two basic situations are considered for calculating LoA confidence intervals: the first where LoAs are considered individually (i.e. using one-sided tolerance factors for a normal distribution); and the second, where LoAs are considered as a pair (i.e. using two-sided tolerance factors for a normal distribution). Equations underlying the calculation of exact confidence limits are briefly outlined. Results To assist in determining confidence intervals for LoAs (considered individually and as a pair) tables of coefficients have been included for degrees of freedom between 1 and 1000. Numerical examples, showing the use of the tables for calculating confidence limits for Bland-Altman LoAs, have been provided. Conclusions Exact confidence intervals for LoAs can differ considerably from Bland and Altman’s approximate method, especially for sample sizes that are not large. There are better, more precise methods for calculating confidence intervals for LoAs than Bland and Altman’s approximate method, although even an approximate calculation of confidence intervals for LoAs is likely to be better than none at all. Reporting confidence limits for LoAs considered as a pair is appropriate for most situations, however there may be circumstances where it is appropriate to report confidence limits for LoAs considered individually.

An extravariation model for improving confidence intervals of population size estimates from removal data

We propose a new model for estimating the size of a population from successive catches taken during a removal experiment. The data from these experiments often have excessive variation, known as overdispersion, as compared with that predicted by the multinomial model. The new model allows catchability to vary randomly among samplings, which accounts for overdispersion. When the catchability is assumed to have a beta distribution, the likelihood function, which is refered to as beta-multinomial, is derived, and hence the maximum likelihood estimates can be evaluated. Simulations show that in the presence of extravariation in the data, the confidence intervals have been substantially underestimated in previous models (Leslie-DeLury, Moran) and that the new model provides more reliable confidence intervals. The performance of these methods was also demonstrated using two real data sets: one with overdispersion, from smallmouth bass (Micropterus dolomieu), and the other without overdispersion, from rat (Rattus rattus).

Does psychological stress mediate social deprivation in tooth loss?

It is unclear which theoretical dimension of psychological stress affects health status. We hypothesized that both distress and coping mediate the relationship between socio-economic position and tooth loss. Cross-sectional data from 2915 middle-aged adults evaluated retention of < 20 teeth, behaviors, psychological stress, and sociodemographic characteristics. Principal components analysis of the Perceived Stress Scale (PSS) extracted 'distress' (a = 0.85) and 'coping' (a =0.83) factors, consistent with theory. Hierarchical entry of explanatory variables into age- and sex-adjusted logistic regression models estimated odds ratios (OR) and 95% confidence intervals [95% CI] for retention of < 20 teeth. Analysis of the separate contributions of distress and coping revealed a significant main effect of coping (OR = 0.7 [95% CI = 0.7-0.8]), but no effect for distress (OR = 1.0 [95% CI = 0.9-1.1]) or for the interaction of coping and distress. Behavior and psychological stress only modestly attenuated socio-economic inequality in retention of < 20 teeth, providing evidence to support a mediating role of coping.

Bayesian versus frequentist statistical inference for investi gating a one-off cancer cluster reported to a health department

Background The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods. Methods This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution. Results Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective "guesstimate". The Bayesian approach is also subjective: whether there is an apparent statistical excess depends on the specified prior. Conclusion In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones), rather than objective reality. Bayesian analysis is (arguably) a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit.

Incomplete pregnancy and risk of ovarian cancer : results from two Australian case-control studies and systematic review

Although full-term pregnancies reduce the risk of ovarian cancer, it has not been conclusively established whether incomplete pregnancies also influence risk. We investigated the relationship between a history of incomplete pregnancy and incident epithelial ovarian cancer among over 4,500 women who participated in two large Australian population-based case-control studies in 1990-1993 and 2002-2005. They provided responses to detailed questions about their reproductive histories and other personal factors. Summary odds ratios (OR) and confidence intervals (CI) derived from each study using the same covariates were aggregated. We found no significant associations between the number of incomplete pregnancies and ovarian cancer, for parous (OR = 0.98, 95% CI: 0.89, 1.08) or nulliparous (OR = 1.06, 95% CI: 0.75, 1.48) women, nor for the number of spontaneous or induced abortions and ovarian cancer for parous women (OR = 0.95, 95% CI 0.82, 1.09; OR = 1.08, 95% CI: 0.86, 1.36) or nulliparous women (OR = 1.2, 95% CI: 0.6, 2.4; OR = 0.8, 95% CI: 0.47, 1.38), respectively. A systematic review of 37 previous studies of the topic confirmed our findings that a history of incomplete pregnancy does not influence a woman’s risk of epithelial ovarian cancer.

The prognostic significance of quality of life following breast cancer [Conference Abstract]

Introduction: Evidence suggests a positive association between quality of life (QOL). and overall survival(OS). among metastatic breast cancer (BC). patients, although the relationship in early-stage BC is unclear. This work examines the association between QOL and OS following a diagnosis of early-stage BC. ----- Methods: A population-based sample of Queensland women (n=287). with early-stage, invasive, unilateral BC, were prospectively observed for a median of 6.6 years. QOL was assessed at six and 18 months post-diagnosis using the Functional Assessment of Cancer Therapy, Breast FACT-B+4. questionnaire. Raw scores for the FACT-B+4 scales were computed and individuals were categorised according to whether QOL declined, remained stable or improved over time. OS was measured from the date of diagnosis to the date of death or was censored at the date of last follow-up. Risk ratios (RR) and 95% confidence intervals (CI). for the association between QOL and OS were obtained using Cox proportional hazards survival models adjusted for confounding characteristics. ----- Results: A total of 27 (9.4%). women died during the follow-up period. Three baseline QOL scales (emotional, general and overall QOL) were significantly associated with OS, with RRs ranging between 0.89 95% CI: 0.81, 0.98; P=0.01. and 0.98 (95% CI: 0.96, 0.99; P=0.03),indicating a 2%-11% reduced risk of death for every one unit increase in QOL. When QOL was categorised according to changes between six and 18 months post-diagnosis, analyses showed that for those who experienced declines in functional and physical QOL, risk of death increased by two- (95% CI: 1.43, 12.52; P<0.01) and four-fold (95% CI: 1.15, 7.19; P=0.02), respectively. Conclusions: This work indicates that specific QOL scales at six months post-diagnosis, and changes in certain QOL scales over the subsequent 12-month period (as measured by the FACT-B+4), are associated with overall survival in women with early-stage breast cancer.

Patterns of reporting to child protection services in Canada by healthcare and nonhealthcare professionals

Background: All Canadian jurisdictions require certain professionals to report suspected or observed child maltreatment. This study examined the types of maltreatment, level of harm and child functioning issues, controlling for family socioeconomic status, age and gender of the child reported by healthcare and non-healthcare professionals. Methods: We conducted chi-square analyses and logistic regression on a national child welfare sample from the 2003 Canadian Incidence Study of Reported Child Abuse and Neglect (CIS-2003) and compared the differences in professional reporting with its previous cycle (CIS-1998) using Bonferroni-corrected confidence intervals. Results: Our analysis of CIS-2003 data revealed that the majority of substantiated child maltreatment is reported to service agencies by non-healthcare professionals (57%), followed by non-professionals (33%) and healthcare professionals (10%). The number of professional reports increased 2.5 times between CIS-1998 and CIS-2003, while non-professionals’ increased 1.7 times. Of the total investigations, professional reports represented 59% in CIS-1998 and 67% in CIS-2003 (p<0.001). Compared to non-healthcare professionals, healthcare professionals more often reported younger children, children who experienced neglect and emotional maltreatment and those assessed as suffering harm and child functioning issues, but less often exposure to domestic violence. Conclusion: The results indicate that healthcare professionals played an important role in identifying children in need of protection considering harm and other child functioning issues. The authors discuss the reasons why underreporting is likely to remain an issue.

Using samples to estimate the sensitivity and specificity of a surveillance process

Determining sensitivity and specificity of a postoperative infection surveillance process is a difficult undertaking. Because postoperative infections are rare, vast numbers of negative results exist, and it is often not reasonable to assess them all. This study gives a methodological framework for estimating sensitivity and specificity by taking only a small sample of the number of patients who test negative and comparing their findings to the reference or “gold standard” rather than comparing the findings of all patients to the gold standard. It provides a formula for deriving confidence intervals for these estimates and a guide to minimum requirements for sampling results.

Multiple indicators of ambient and personal ultraviolet radiation exposure and risk of non-Hodgkin lymphoma (United States)

Recent epidemiologic studies have suggested that ultraviolet radiation (UV) may protect against non-Hodgkin lymphoma (NHL), but few, if any, have assessed multiple indicators of ambient and personal UV exposure. Using the US Radiologic Technologists study, we examined the association between NHL and self-reported time outdoors in summer, as well as average year-round and seasonal ambient exposures based on satellite estimates for different age periods, and sun susceptibility in participants who had responded to two questionnaires (1994–1998, 2003–2005) and who were cancer-free as of the earlier questionnaire. Using unconditional logistic regression, we estimated the odds ratio (OR) and 95% confidence intervals for 64,103 participants with 137 NHL cases. Self-reported time outdoors in summer was unrelated to risk. Lower risk was somewhat related to higher average year-round and winter ambient exposure for the period closest in time, and prior to, diagnosis (ages 20–39). Relative to 1.0 for the lowest quartile of average year-round ambient UV, the estimated OR for successively higher quartiles was 0.68 (0.42–1.10); 0.82 (0.52–1.29); and 0.64 (0.40–1.03), p-trend = 0.06), for this age period. The lower NHL risk associated with higher year-round average and winter ambient UV provides modest additional support for a protective relationship between UV and NHL.

Derivation of motor vehicle tailpipe particle emission factors suitable for modelling urban fleet emissions and air quality assessments

Background, aim, and scope Urban motor vehicle fleets are a major source of particulate matter pollution, especially of ultrafine particles (diameters < 0.1 µm), and exposure to particulate matter has known serious health effects. A considerable body of literature is available on vehicle particle emission factors derived using a wide range of different measurement methods for different particle sizes, conducted in different parts of the world. Therefore the choice as to which are the most suitable particle emission factors to use in transport modelling and health impact assessments presented as a very difficult task. The aim of this study was to derive a comprehensive set of tailpipe particle emission factors for different vehicle and road type combinations, covering the full size range of particles emitted, which are suitable for modelling urban fleet emissions. Materials and methods A large body of data available in the international literature on particle emission factors for motor vehicles derived from measurement studies was compiled and subjected to advanced statistical analysis, to determine the most suitable emission factors to use in modelling urban fleet emissions. Results This analysis resulted in the development of five statistical models which explained 86%, 93%, 87%, 65% and 47% of the variation in published emission factors for particle number, particle volume, PM1, PM2.5 and PM10 respectively. A sixth model for total particle mass was proposed but no significant explanatory variables were identified in the analysis. From the outputs of these statistical models, the most suitable particle emission factors were selected. This selection was based on examination of the statistical robustness of the statistical model outputs, including consideration of conservative average particle emission factors with the lowest standard errors, narrowest 95% confidence intervals and largest sample sizes, and the explanatory model variables, which were Vehicle Type (all particle metrics), Instrumentation (particle number and PM2.5), Road Type (PM10) and Size Range Measured and Speed Limit on the Road (particle volume). Discussion A multiplicity of factors need to be considered in determining emission factors that are suitable for modelling motor vehicle emissions, and this study derived a set of average emission factors suitable for quantifying motor vehicle tailpipe particle emissions in developed countries. Conclusions The comprehensive set of tailpipe particle emission factors presented in this study for different vehicle and road type combinations enable the full size range of particles generated by fleets to be quantified, including ultrafine particles (measured in terms of particle number). These emission factors have particular application for regions which may have a lack of funding to undertake measurements, or insufficient measurement data upon which to derive emission factors for their region. Recommendations and perspectives In urban areas motor vehicles continue to be a major source of particulate matter pollution and of ultrafine particles. It is critical that in order to manage this major pollution source methods are available to quantify the full size range of particles emitted for traffic modelling and health impact assessments.

Lifestyle factors associated concurrently and prospectively with co-morbid cardiovascular disease in a population-based cohort of colorectal cancer survivors

Aims To assess self-reported lifetime prevalence of cardiovascular disease (CVD) among colorectal cancer survivors, and examine the cross-sectional and prospective associations of lifestyle factors with co-morbid CVD. Methods Colorectal cancer survivors were recruited (n = 1966). Data were collected at approximately 5, 12, 24 and 36 months post-diagnosis. Cross-sectional findings included six CVD categories (hypercholesterolaemia, hypertension, diabetes, heart failure, kidney disease and ischaemic heart disease (IHD)) at 5 months post-diagnosis. Longitudinal outcomes included the probability of developing (de novo) co-morbid CVD by 36 months post-diagnosis. Lifestyle factors included body mass index, physical activity, television (TV) viewing, alcohol consumption and smoking. Results Co-morbid CVD prevalence at 5 months post-diagnosis was 59%, and 16% of participants with no known CVD at the baseline reported de novo CVD by 36 months. Obesity at the baseline predicted de novo hypertension (odds ratio [OR] = 2.20, 95% confidence intervals [CI] = 1.09, 4.45) and de novo diabetes (OR = 6.55, 95% CI = 2.19, 19.53). Participants watching >4 h of TV/d at the baseline (compared with <2 h/d) were more likely to develop ischaemic heart disease by 36 months (OR = 5.51, 95% CI = 1.86, 16.34). Conclusion Overweight colorectal cancer survivors were more likely to suffer from co-morbid CVD. Interventions focusing on weight management and other modifiable lifestyle factors may reduce functional decline and improve survival.