Efficient parameter estimation in longitudinal data analysis using a hybrid GEE method

The method of generalized estimating equations (GEEs) provides consistent estimates of the regression parameters in a marginal regression model for longitudinal data, even when the working correlation model is misspecified (Liang and Zeger, 1986). However, the efficiency of a GEE estimate can be seriously affected by the choice of the working correlation model. This study addresses this problem by proposing a hybrid method that combines multiple GEEs based on different working correlation models, using the empirical likelihood method (Qin and Lawless, 1994). Analyses show that this hybrid method is more efficient than a GEE using a misspecified working correlation model. Furthermore, if one of the working correlation structures correctly models the within-subject correlations, then this hybrid method provides the most efficient parameter estimates. In simulations, the hybrid method's finite-sample performance is superior to a GEE under any of the commonly used working correlation models and is almost fully efficient in all scenarios studied. The hybrid method is illustrated using data from a longitudinal study of the respiratory infection rates in 275 Indonesian children.

Efficient regression analysis with ranked-set sampling

This article is motivated by a lung cancer study where a regression model is involved and the response variable is too expensive to measure but the predictor variable can be measured easily with relatively negligible cost. This situation occurs quite often in medical studies, quantitative genetics, and ecological and environmental studies. In this article, by using the idea of ranked-set sampling (RSS), we develop sampling strategies that can reduce cost and increase efficiency of the regression analysis for the above-mentioned situation. The developed method is applied retrospectively to a lung cancer study. In the lung cancer study, the interest is to investigate the association between smoking status and three biomarkers: polyphenol DNA adducts, micronuclei, and sister chromatic exchanges. Optimal sampling schemes with different optimality criteria such as A-, D-, and integrated mean square error (IMSE)-optimality are considered in the application. With set size 10 in RSS, the improvement of the optimal schemes over simple random sampling (SRS) is great. For instance, by using the optimal scheme with IMSE-optimality, the IMSEs of the estimated regression functions for the three biomarkers are reduced to about half of those incurred by using SRS.

Estimating equations for removal data analysis

We consider the problem of estimating a population size from successive catches taken during a removal experiment and propose two estimating functions approaches, the traditional quasi-likelihood (TQL) approach for dependent observations and the conditional quasi-likelihood (CQL) approach using the conditional mean and conditional variance of the catch given previous catches. Asymptotic covariance of the estimates and the relationship between the two methods are derived. Simulation results and application to the catch data from smallmouth bass show that the proposed estimating functions perform better than other existing methods, especially in the presence of overdispersion.

Effect of individual variability on estimation of population parameters from length-frequency data

We consider estimation of mortality rates and growth parameters from length-frequency data of a fish stock when there is individual variability in the von Bertalanffy growth parameter L-infinity and investigate the possible bias in the estimates when the individual variability is ignored. Three methods are examined: (i) the regression method based on the Beverton and Holt's (1956, Rapp. P.V. Reun. Cons. Int. Explor. Mer, 140: 67-83) equation; (ii) the moment method of Powell (1979, Rapp. PV. Reun. Int. Explor. Mer, 175: 167-169); and (iii) a generalization of Powell's method that estimates the individual variability to be incorporated into the estimation. It is found that the biases in the estimates from the existing methods are, in general, substantial, even when individual variability in growth is small and recruitment is uniform, and the generalized method performs better in terms of bias but is subject to a larger variation. There is a need to develop robust and flexible methods to deal with individual variability in the analysis of length-frequency data.

Online grocery shopping: The impact of shopping frequency on perceived risk

Online grocery shopping has enjoyed strong growth and it is predicted this channel will continue to grow exponentially in the coming years. While online shopping has attracted an abundance of research interest, examinations of online grocery shopping behaviour are only now emerging. Shopping online for groceries differs considerably from general online shopping due to the perishability and variability of the product, and frequency of the shopping activity. Two salient gaps underpin this research into online grocery shopping. This study responds to calls to investigate the online shoppers’ experience in the context of online purchasing frequency. Second, this study examines the mediating effect of perceived risk between trust and online repurchase intention of groceries. An online survey was employed to collect data from shoppers who were recruited from a multi-channel grocery e-retailer’s database. The online survey, comprising 16 reflective validated scale items, was sent to 555 frequent and infrequent online grocery shoppers. Results find that while customer satisfaction predicts trust for both infrequent and frequent online grocery shoppers, perceived risk fully mediates the effect of trust on repurchase intentions for infrequent online grocery shoppers. Furthermore path analysis reveals that the developed behavioural model is variant across both groups of shoppers. Theoretically, we provide a deeper understanding of the online customer experience, while gaining insight into two shopper segments identified as being important to grocery e-retailers. For managers, this study tests an online customer behavioural model with actual purchasing behaviour and identifies the continued presence of perceived risk in grocery e-retailing regardless of purchase frequency or experience.

Spectral analysis of pair-correlation bandwidth: Application to cell biology images

Images from cell biology experiments often indicate the presence of cell clustering, which can provide insight into the mechanisms driving the collective cell behaviour. Pair-correlation functions provide quantitative information about the presence, or absence, of clustering in a spatial distribution of cells. This is because the pair-correlation function describes the ratio of the abundance of pairs of cells, separated by a particular distance, relative to a randomly distributed reference population. Pair-correlation functions are often presented as a kernel density estimate where the frequency of pairs of objects are grouped using a particular bandwidth (or bin width), Δ>0. The choice of bandwidth has a dramatic impact: choosing Δ too large produces a pair-correlation function that contains insufficient information, whereas choosing Δ too small produces a pair-correlation signal dominated by fluctuations. Presently, there is little guidance available regarding how to make an objective choice of Δ. We present a new technique to choose Δ by analysing the power spectrum of the discrete Fourier transform of the pair-correlation function. Using synthetic simulation data, we confirm that our approach allows us to objectively choose Δ such that the appropriately binned pair-correlation function captures known features in uniform and clustered synthetic images. We also apply our technique to images from two different cell biology assays. The first assay corresponds to an approximately uniform distribution of cells, while the second assay involves a time series of images of a cell population which forms aggregates over time. The appropriately binned pair-correlation function allows us to make quantitative inferences about the average aggregate size, as well as quantifying how the average aggregate size changes with time.

Genome-wide linkage and association analyses implicate FASN in predisposition to Uterine Leiomyomata

Uterine leiomyomata (UL), the most prevalent pelvic tumors in women of reproductive age, pose a major public health problem given their high frequency, associated morbidities, and most common indication for hysterectomies. A genetic component to UL predisposition is supported by analyses of ethnic predisposition, twin studies, and familial aggregation. A genome-wide SNP linkage panel was genotyped and analyzed in 261 white UL-affected sister-pair families from the Finding Genes for Fibroids study. Two significant linkage regions were detected in 10p11 (LOD = 4.15) and 3p21 (LOD = 3.73), and five additional linkage regions were identified with LOD scores > 2.00 in 2q37, 5p13, 11p15, 12q14, and 17q25. Genome-wide association studies were performed in two independent cohorts of white women, and a meta-analysis was conducted. One SNP (rs4247357) was identified with a p value (p = 3.05 x 10(-8)) that reached genome-wide significance (odds ratio = 1.299). The candidate SNP is under a linkage peak and in a block of linkage disequilibrium in 17q25.3, which spans fatty acid synthase (FASN), coiled-coil-domain-containing 57 (CCDC57), and solute-carrier family 16, member 3 (SLC16A3). By tissue microarray immunohistochemistry, we found elevated (3-fold) FAS levels in UL-affected tissue compared to matched myometrial tissue. FAS transcripts and/or protein levels are upregulated in various neoplasms and implicated in tumor cell survival. FASN represents the initial UL risk allele identified in white women by a genome-wide, unbiased approach and opens a path to management and potential therapeutic intervention.

Genome-wide meta-analysis of common variant differences between men and women

The male-to-female sex ratio at birth is constant across world populations with an average of 1.06 (106 male to 100 female live births) for populations of European descent. The sex ratio is considered to be affected by numerous biological and environmental factors and to have a heritable component. The aim of this study was to investigate the presence of common allele modest effects at autosomal and chromosome X variants that could explain the observed sex ratio at birth. We conducted a large-scale genome-wide association scan (GWAS) meta-analysis across 51 studies, comprising overall 114 863 individuals (61 094 women and 53 769 men) of European ancestry and 2 623 828 common (minor allele frequency >0.05) single-nucleotide polymorphisms (SNPs). Allele frequencies were compared between men and women for directly-typed and imputed variants within each study. Forward-time simulations for unlinked, neutral, autosomal, common loci were performed under the demographic model for European populations with a fixed sex ratio and a random mating scheme to assess the probability of detecting significant allele frequency differences. We do not detect any genome-wide significant (P < 5 x 10(-8)) common SNP differences between men and women in this well-powered meta-analysis. The simulated data provided results entirely consistent with these findings. This large-scale investigation across ~115 000 individuals shows no detectable contribution from common genetic variants to the observed skew in the sex ratio. The absence of sex-specific differences is useful in guiding genetic association study design, for example when using mixed controls for sex-biased traits.

Analysis of the distribution of the number of bidders in construction contract auctions

The number of bidders, N, involved in a construction procurement auction is known to have an important effect on the value of the lowest bid and the mark up applied by bidders. In practice, for example, it is important for a bidder to have a good estimate of N when bidding for a current contract. One approach, instigated by Friedman in 1956, is to make such an estimate by statistical analysis and modelling. Since then, however, finding a suitable model for N has been an enduring problem for researchers and, despite intensive research activity in the subsequent thirty years little progress has been made - due principally to the absence of new ideas and perspectives. This paper resumes the debate by checking old assumptions, providing new evidence relating to concomitant variables and proposing a new model. In doing this and in order to assure universality, a novel approach is developed and tested by using a unique set of twelve construction tender databases from four continents. This shows the new model provides a significant advancement on previous versions. Several new research questions are also posed and other approaches identified for future study.

The effectiveness of SMS reminders on appointment attendance: A meta-analysis

Objective To identify the efficacy of short message service (SMS) reminders in health care appointment attendance. Materials and Methods A systematic review was undertaken to identify studies published between 2005 and 2015 that compared the attendance rates of patients receiving SMS reminders compared to patients not receiving a reminder. Each article was examined for information regarding the study design, sample size, population demographics and intervention methods. A meta-analysis was used to calculate a pooled estimate odds ratio. Results Twenty-eight (28) studies were included in the review, including 13 (46%) randomized controlled trials. The pooled odds ratio of the randomized control trials was 1.62 (1.35 – 1.94). Half of the studies reviewed sent the reminder within 48 hour prior to the appointment time, yet no significant subgroups differences with respect to participant age, SMS timing, rate or type, setting or specialty was detectable. Discussion All studies, except one with a small sample size, demonstrated a positive OR, indicating SMS reminders were an effective means of improving appointment attendance. There was no significant difference in OR when controlling for when the SMS was sent, the frequency of the reminders or the content of the reminder. Conclusion SMS appointment reminders are an effective and operative method in improving appointment attendance in a health care setting and this effectiveness has improved over the past five years. Further research is required to identify the optimal SMS reminder timing and frequency, specifically in relation to the length of time since the appointment.

Frequency of dressing changes for central venous access devices on catheter-related infections

Background People admitted to intensive care units and those with chronic health care problems often require long-term vascular access. Central venous access devices (CVADs) are used for administering intravenous medications and blood sampling. CVADs are covered with a dressing and secured with an adhesive or adhesive tape to protect them from infection and reduce movement. Dressings are changed when they become soiled with blood or start to come away from the skin. Repeated removal and application of dressings can cause damage to the skin. The skin is an important barrier that protects the body against infection. Less frequent dressing changes may reduce skin damage, but it is unclear whether this practice affects the frequency of catheter-related infections. Objectives To assess the effect of the frequency of CVAD dressing changes on the incidence of catheter-related infections and other outcomes including pain and skin damage. Search methods In June 2015 we searched: The Cochrane Wounds Specialised Register; The Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library); Ovid MEDLINE; Ovid MEDLINE (In-Process & Other Non-Indexed Citations); Ovid EMBASE and EBSCO CINAHL. We also searched clinical trials registries for registered trials. There were no restrictions with respect to language, date of publication or study setting. Selection criteria All randomised controlled trials (RCTs) evaluating the effect of the frequency of CVAD dressing changes on the incidence of catheter-related infections on all patients in any healthcare setting. Data collection and analysis We used standard Cochrane review methodology. Two review authors independently assessed studies for inclusion, performed risk of bias assessment and data extraction. We undertook meta-analysis where appropriate or otherwise synthesised data descriptively when heterogeneous. Main results We included five RCTs (2277 participants) that compared different frequencies of CVAD dressing changes. The studies were all conducted in Europe and published between 1995 and 2009. Participants were recruited from the intensive care and cancer care departments of one children's and four adult hospitals. The studies used a variety of transparent dressings and compared a longer interval between dressing changes (5 to15 days; intervention) with a shorter interval between changes (2 to 5 days; control). In each study participants were followed up until the CVAD was removed or until discharge from ICU or hospital. - Confirmed catheter-related bloodstream infection (CRBSI) One trial randomised 995 people receiving central venous catheters to a longer or shorter interval between dressing changes and measured CRBSI. It is unclear whether there is a difference in the risk of CRBSI between people having long or short intervals between dressing changes (RR 1.42, 95% confidence interval (CI) 0.40 to 4.98) (low quality evidence). - Suspected catheter-related bloodstream infection Two trials randomised a total of 151 participants to longer or shorter dressing intervals and measured suspected CRBSI. It is unclear whether there is a difference in the risk of suspected CRBSI between people having long or short intervals between dressing changes (RR 0.70, 95% CI 0.23 to 2.10) (low quality evidence). - All cause mortality Three trials randomised a total of 896 participants to longer or shorter dressing intervals and measured all cause mortality. It is unclear whether there is a difference in the risk of death from any cause between people having long or short intervals between dressing changes (RR 1.06, 95% CI 0.90 to 1.25) (low quality evidence). - Catheter-site infection Two trials randomised a total of 371 participants to longer or shorter dressing intervals and measured catheter-site infection. It is unclear whether there is a difference in risk of catheter-site infection between people having long or short intervals between dressing changes (RR 1.07, 95% CI 0.71 to 1.63) (low quality evidence). - Skin damage One small trial (112 children) and three trials (1475 adults) measured skin damage. There was very low quality evidence for the effect of long intervals between dressing changes on skin damage compared with short intervals (children: RR of scoring ≥ 2 on the skin damage scale 0.33, 95% CI 0.16 to 0.68; data for adults not pooled). - Pain Two studies involving 193 participants measured pain. It is unclear if there is a difference between long and short interval dressing changes on pain during dressing removal (RR 0.80, 95% CI 0.46 to 1.38) (low quality evidence). Authors' conclusions The best available evidence is currently inconclusive regarding whether longer intervals between CVAD dressing changes are associated with more or less catheter-related infection, mortality or pain than shorter intervals.

Towards automated content analysis of discussion transcripts: A cognitive presence case

In this paper, we present the results of an exploratory study that examined the problem of automating content analysis of student online discussion transcripts. We looked at the problem of coding discussion transcripts for the levels of cognitive presence, one of the three main constructs in the Community of Inquiry (CoI) model of distance education. Using Coh-Metrix and LIWC features, together with a set of custom features developed to capture discussion context, we developed a random forest classification system that achieved 70.3% classification accuracy and 0.63 Cohen's kappa, which is significantly higher than values reported in the previous studies. Besides improvement in classification accuracy, the developed system is also less sensitive to overfitting as it uses only 205 classification features, which is around 100 times less features than in similar systems based on bag-of-words features. We also provide an overview of the classification features most indicative of the different phases of cognitive presence that gives an additional insights into the nature of cognitive presence learning cycle. Overall, our results show great potential of the proposed approach, with an added benefit of providing further characterization of the cognitive presence coding scheme.

Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis

Early identification of diabetic foot complications and their precursors is essential in preventing their devastating consequences, such as foot infection and amputation. Frequent, automatic risk assessment by an intelligent telemedicine system might be feasible and cost effective. Infrared thermography is a promising modality for such a system. The temperature differences between corresponding areas on contralateral feet are the clinically significant parameters. This asymmetric analysis is hindered by (1) foot segmentation errors, especially when the foot temperature and the ambient temperature are comparable, and by (2) different shapes and sizes between contralateral feet due to deformities or minor amputations. To circumvent the first problem, we used a color image and a thermal image acquired synchronously. Foot regions, detected in the color image, were rigidly registered to the thermal image. This resulted in 97.8% ± 1.1% sensitivity and 98.4% ± 0.5% specificity over 76 high-risk diabetic patients with manual annotation as a reference. Nonrigid landmark-based registration with Bsplines solved the second problem. Corresponding points in the two feet could be found regardless of the shapes and sizes of the feet. With that, the temperature difference of the left and right feet could be obtained.

Statistical analysis of spectral data: A methodology for designing an intelligent monitoring system for the diabetic foot

Early detection of (pre-)signs of ulceration on a diabetic foot is valuable for clinical practice. Hyperspectral imaging is a promising technique for detection and classification of such (pre-)signs. However, the number of the spectral bands should be limited to avoid overfitting, which is critical for pixel classification with hyperspectral image data. The goal was to design a detector/classifier based on spectral imaging (SI) with a small number of optical bandpass filters. The performance and stability of the design were also investigated. The selection of the bandpass filters boils down to a feature selection problem. A dataset was built, containing reflectance spectra of 227 skin spots from 64 patients, measured with a spectrometer. Each skin spot was annotated manually by clinicians as "healthy" or a specific (pre-)sign of ulceration. Statistical analysis on the data set showed the number of required filters is between 3 and 7, depending on additional constraints on the filter set. The stability analysis revealed that shot noise was the most critical factor affecting the classification performance. It indicated that this impact could be avoided in future SI systems with a camera sensor whose saturation level is higher than 106, or by postimage processing.

The problem of simplification: Think-tanks, recipes, equity and ‘Turning around low-performing schools’

Non-government actors such as think-tanks are playing an important role in Australian policy work. As governments increasingly outsource policy work previously done by education departments and academics to these new policy actors, more think-tanks have emerged that represent a wide range of political views and ideological positions. This paper looks at the emergence of the Grattan Institute as one significant player in Australian education policy with a particular emphasis on Grattan’s report ‘Turning around low-performing schools’. Grattan exemplifies many of the facets of Barber’s ‘deliverology’, as they produce reports designed to be easily digested, simply actioned and provide reassurance that there is an answer, often through focusing on ‘what works’ recipes. ‘Turning around low-performing schools’ is a perfect example of this deliverology. However, a close analysis of the Report suggests that it contains four major problems which seriously impact its usefulness for schools and policymakers: it ignores data that may be more important in explaining the turn-around of schools, the Report is overly reliant on NAPLAN data, there are reasons to be suspicious about the evidence assembled, and finally the Report falls into a classic trap of logic—the post hoc fallacy.