A Characterization of the Negative Multinomial Distribution

This paper deals with a characterization of the negative multinomial distribution. It is based on the assumption that the conditional distribution of two random vectors is multivariate inverse hypergeometric. It makes use essentially of a multivariate analogue of a condition known in the literature as the Rao-Rubin condition. The result is extended to include characterizations of truncated forms of the negative multinomial distribution. Comparison with previous results in the field is made and an example is included to demonstrate a possible use of the characterizatio

Three Ways of Combining Genotyping and Resequencing in Case-Control Association Studies

We describe three statistical results that we have found to be useful in case-control genetic association testing. All three involve combining the discovery of novel genetic variants, usually by sequencing, with genotyping methods that recognize previously discovered variants. We first consider expanding the list of known variants by concentrating variant-discovery in cases. Although the naive inclusion of cases-only sequencing data would create a bias, we show that some sequencing data may be retained, even if controls are not sequenced. Furthermore, for alleles of intermediate frequency, cases-only sequencing with bias-correction entails little if any loss of power, compared to dividing the same sequencing effort among cases and controls. Secondly, we investigate more strongly focused variant discovery to obtain a greater enrichment for disease-related variants. We show how case status, family history, and marker sharing enrich the discovery set by increments that are multiplicative with penetrance, enabling the preferential discovery of high-penetrance variants. A third result applies when sequencing is the primary means of counting alleles in both cases and controls, but a supplementary pooled genotyping sample is used to identify the variants that are very rare. We show that this raises no validity issues, and we evaluate a less expensive and more adaptive approach to judging rarity, based on group-specific variants. We demonstrate the important and unusual caveat that this method requires equal sample sizes for validity. These three results can be used to more efficiently detect the association of rare genetic variants with disease

Functional mapping of hospitals by diagnosis-dominant case-mix analysis

BACKGROUND: Principles and methods for the allocation of healthcare resources among healthcare providers have long been health policy research issues in many countries. Healthcare reforms including the development of a new case-mix system, Diagnosis Procedure Combination (DPC), and the introduction of a DPC-based payment system are currently underway in Japan, and a methodology for adequately assessing the functions of healthcare providers is needed to determine healthcare resource allocations. METHODS: By two-dimensional mapping of the rarity and complexity of diagnoses for patients receiving treatment, we were able to quantitatively demonstrate differences in the functions of different healthcare service provider groups. RESULTS: On average, inpatients had diseases that were 3.6-times rarer than those seen in outpatients, while major teaching hospitals treated inpatients with diseases 3.0-times rarer on average than those seen at small hospitals. CONCLUSION: We created and evaluated a new indicator for DPC, the diagnosis-dominant case-mix system developed in Japan, whereby the system was used to assess the functions of healthcare service providers. The results suggest that it is possible to apply the case-mix system to the integrated evaluation of outpatient and inpatient healthcare services and to the appropriate allocation of healthcare resources among health service providers

Novel Aptamer-Nanoparticle Bioconjugates Enhances Delivery of Anticancer Drug to MUC1-Positive Cancer Cells In Vitro

MUC1 protein is an attractive target for anticancer drug delivery owing to its overexpression in most adenocarcinomas. In this study, a reported MUC1 protein aptamer is exploited as the targeting agent of a nanoparticle-based drug delivery system. Paclitaxel (PTX) loaded poly (lactic-co-glycolic-acid) (PLGA) nanoparticles were formulated by an emulsion/evaporation method, and MUC1 aptamers (Apt) were conjugated to the particle surface through a DNA spacer. The aptamer conjugated nanoparticles (Apt-NPs) are about 225.3 nm in size with a stable in vitro drug release profile. Using MCF-7 breast cancer cell as a MUC1-overexpressing model, the MUC1 aptamer increased the uptake of nanoparticles into the target cells as measured by flow cytometry. Moreover, the PTX loaded Apt-NPs enhanced in vitro drug delivery and cytotoxicity to MUC1+ cancer cells, as compared with non-targeted nanoparticles that lack the MUC1 aptamer (P<0.01). The behavior of this novel aptamer-nanoparticle bioconjugates suggests that MUC1 aptamers may have application potential in targeted drug delivery towards MUC1-overexpressing tumors

Measuring diversity in medical reports based on categorized attributes and international classification systems

<p>Abstract</p> <p>Background</p> <p>Narrative medical reports do not use standardized terminology and often bring insufficient information for statistical processing and medical decision making. Objectives of the paper are to propose a method for measuring diversity in medical reports written in any language, to compare diversities in narrative and structured medical reports and to map attributes and terms to selected classification systems.</p> <p>Methods</p> <p>A new method based on a general concept of f-diversity is proposed for measuring diversity of medical reports in any language. The method is based on categorized attributes recorded in narrative or structured medical reports and on international classification systems. Values of categories are expressed by terms. Using SNOMED CT and ICD 10 we are mapping attributes and terms to predefined codes. We use f-diversities of Gini-Simpson and Number of Categories types to compare diversities of narrative and structured medical reports. The comparison is based on attributes selected from the Minimal Data Model for Cardiology (MDMC).</p> <p>Results</p> <p>We compared diversities of 110 Czech narrative medical reports and 1119 Czech structured medical reports. Selected categorized attributes of MDMC had mostly different numbers of categories and used different terms in narrative and structured reports. We found more than 60% of MDMC attributes in SNOMED CT. We showed that attributes in narrative medical reports had greater diversity than the same attributes in structured medical reports. Further, we replaced each value of category (term) used for attributes in narrative medical reports by the closest term and the category used in MDMC for structured medical reports. We found that relative Gini-Simpson diversities in structured medical reports were significantly smaller than those in narrative medical reports except the "Allergy" attribute.</p> <p>Conclusions</p> <p>Terminology in narrative medical reports is not standardized. Therefore it is nearly impossible to map values of attributes (terms) to codes of known classification systems. A high diversity in narrative medical reports terminology leads to more difficult computer processing than in structured medical reports and some information may be lost during this process. Setting a standardized terminology would help healthcare providers to have complete and easily accessible information about patients that would result in better healthcare.</p

A New Calibrated Bayesian Internal Goodness-of-Fit Method: Sampled Posterior p-Values as Simple and General p-Values That Allow Double Use of the Data

Background: Recent approaches mixing frequentist principles with Bayesian inference propose internal goodness-of-fit (GOF) p-values that might be valuable for critical analysis of Bayesian statistical models. However, GOF p-values developed to date only have known probability distributions under restrictive conditions. As a result, no known GOF p-value has a known probability distribution for any discrepancy function. Methodology/Principal Findings: We show mathematically that a new GOF p-value, called the sampled posterior p-value (SPP), asymptotically has a uniform probability distribution whatever the discrepancy function. In a moderate finite sample context, simulations also showed that the SPP appears stable to relatively uninformative misspecifications of the prior distribution. Conclusions/Significance: These reasons, together with its numerical simplicity, make the SPP a better canonical GOF p-value than existing GOF p-values

The Distribution of Sexually-Transmitted Human Papillomaviruses in HIV Positive and Negative Patients in Zambia, Africa

Background: Human Papillomaviruses (HPV) are double-stranded DNA viruses, considered to be the primary etiological agents in cervical intraepithelial neoplasias and cancers. Approximately 15–20 of the 40 mucosal HPVs confer a high-risk of progression of lesions to invasive cancer. In this study, we investigated the prevalence of sexually transmitted HPVs in Human Immunodeficiency Virus (HIV) positive and negative patients in Zambia, Africa. The rate of high-risk HPV genotypes worldwide varies within each country. Thus, we sought to investigate the rates of HPV infection in sub-Saharan Africa and the potential role of HIV in affecting the HPV genotype distribution. Methods: This retrospective cross-sectional study reports findings on the association and effects of HIV on HPV infections in an existing cohort of patients at University Teaching Hospital (UTH) Lusaka, Zambia. The objective of this study was to assess HPV prevalence, genotype distribution and to identify co-factors that influence HPV infection. Polymerase chain reaction (PCR) with two standard consensus primer sets (CpI/II and GP5+/6+) was used to test for the presence of HPV DNA. Primers specific for β-actin were used to monitor DNA quality. Vaginal lavage samples, collected between 1998-1999 from a total of 70 women, were part of a larger cohort that was also analyzed for HIV and human herpesvirus infection. Seventy of the samples yielded usable DNA. HIV status was determined by two rapid assays, Capillus and Determine. The incidence of HIV and HPV infections and HPV genotype distributions were calculated and statistical significance was determined by Chi-Squared test. Results: We determined that most common HPV genotypes detected among these Zambian patients were types 16 and 18 (21.6% each), which is approximately three-fold greater than the rates for HPV16, and ten-fold greater than the rates for HPV18 in the United States. The worldwide prevalence of HPV16 is approximately 14% and HPV18 is 5%. The overall ratio of high-risk (HR) to low-risk (LR) HPVs in the patient cohort was 69% and 31% respectively; essentially identical to that for the HR and LR distributions worldwide. However, we discovered that HIV positive patients were two-times as likely to have an HR HPV as HIV negative individuals, while the distribution of LR HPVs was unaffected by HIV status. Interestingly, we observed a nine-fold increase in HPV18 infection frequency in HIV positive versus HIV negative individuals. Conclusion: The rate of oncogenic HPVs (type 16 and 18) in Zambia was much higher than in the U.S., potentially providing an explanation for the high-rates of cervical cancer in Zambia. Surprisingly, we discovered a strong association between positive HIV status and the prevalence of HR HPVs, and specifically HPV18

Oblique decision trees for spatial pattern detection: optimal algorithm and application to malaria risk

BACKGROUND: In order to detect potential disease clusters where a putative source cannot be specified, classical procedures scan the geographical area with circular windows through a specified grid imposed to the map. However, the choice of the windows' shapes, sizes and centers is critical and different choices may not provide exactly the same results. The aim of our work was to use an Oblique Decision Tree model (ODT) which provides potential clusters without pre-specifying shapes, sizes or centers. For this purpose, we have developed an ODT-algorithm to find an oblique partition of the space defined by the geographic coordinates. METHODS: ODT is based on the classification and regression tree (CART). As CART finds out rectangular partitions of the covariate space, ODT provides oblique partitions maximizing the interclass variance of the independent variable. Since it is a NP-Hard problem in R(N), classical ODT-algorithms use evolutionary procedures or heuristics. We have developed an optimal ODT-algorithm in R(2), based on the directions defined by each couple of point locations. This partition provided potential clusters which can be tested with Monte-Carlo inference. We applied the ODT-model to a dataset in order to identify potential high risk clusters of malaria in a village in Western Africa during the dry season. The ODT results were compared with those of the Kulldorff' s SaTScan™. RESULTS: The ODT procedure provided four classes of risk of infection. In the first high risk class 60%, 95% confidence interval (CI95%) [52.22–67.55], of the children was infected. Monte-Carlo inference showed that the spatial pattern issued from the ODT-model was significant (p < 0.0001). Satscan results yielded one significant cluster where the risk of disease was high with an infectious rate of 54.21%, CI95% [47.51–60.75]. Obviously, his center was located within the first high risk ODT class. Both procedures provided similar results identifying a high risk cluster in the western part of the village where a mosquito breeding point was located. CONCLUSION: ODT-models improve the classical scanning procedures by detecting potential disease clusters independently of any specification of the shapes, sizes or centers of the clusters

Molecular imaging in oncology: the acceptance of PET/CT and the emergence of MR/PET imaging

In the last decade, PET-only systems have been phased out and replaced with PET-CT systems. This merger of a functional and anatomical imaging modality turned out to be extremely useful in clinical practice. Currently, PET-CT is a major diagnostic tool in oncology. At the dawn of the merger of MRI and PET, another breakthrough in clinical imaging is expected. The combination of these imaging modalities is challenging, but has particular features such as imaging biological processes at the same time in specific body locations