Defining adequate contact for transmission of Mycobacterium tuberculosis in an African urban environment

Background The risk of infection from respiratory pathogens increases according to the contact rate between the infectious case and susceptible contact, but the definition of adequate contact for transmission is not standard. In this study we aimed to identify factors that can explain the level of contact between tuberculosis cases and their social networks in an African urban environment. Methods This was a cross-sectional study conducted in Kampala, Uganda from 2013 to 2017. We carried out an exploratory factor analysis (EFA) in social network data from tuberculosis cases and their contacts. We evaluated the factorability of the data to EFA using the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO). We used principal axis factoring with oblique rotation to extract and rotate the factors, then we calculated factor scores for each using the weighted sum scores method. We assessed construct validity of the factors by associating the factors with other variables related to social mixing. Results Tuberculosis cases (N = 120) listed their encounters with 1154 members of their social networks. Two factors were identified, the first named “Setting” captured 61% of the variance whereas the second, named ‘Relationship’ captured 21%. Median scores for the setting and relationship factors were 10.2 (IQR 7.0, 13.6) and 7.7 (IQR 6.4, 10.1) respectively. Setting and Relationship scores varied according to the age, gender, and nature of the relationship among tuberculosis cases and their contacts. Family members had a higher median setting score (13.8, IQR 11.6, 15.7) than non-family members (7.2, IQR 6.2, 9.4). The median relationship score in family members (9.9, IQR 7.6, 11.5) was also higher than in non-family members (6.9, IQR 5.6, 8.1). For both factors, household contacts had higher scores than extra-household contacts (p < .0001). Contacts of male cases had a lower setting score as opposed to contacts of female cases. In contrast, contacts of male and female cases had similar relationship scores. Conclusions In this large cross-sectional study from an urban African setting, we identified two factors that can assess adequate contact between tuberculosis cases and their social network members. These findings also confirm the complexity and heterogeneity of social mixing

Optimally splitting cases for training and testing high dimensional classifiers

<p>Abstract</p> <p>Background</p> <p>We consider the problem of designing a study to develop a predictive classifier from high dimensional data. A common study design is to split the sample into a training set and an independent test set, where the former is used to develop the classifier and the latter to evaluate its performance. In this paper we address the question of what proportion of the samples should be devoted to the training set. How does this proportion impact the mean squared error (MSE) of the prediction accuracy estimate?</p> <p>Results</p> <p>We develop a non-parametric algorithm for determining an optimal splitting proportion that can be applied with a specific dataset and classifier algorithm. We also perform a broad simulation study for the purpose of better understanding the factors that determine the best split proportions and to evaluate commonly used splitting strategies (1/2 training or 2/3 training) under a wide variety of conditions. These methods are based on a decomposition of the MSE into three intuitive component parts.</p> <p>Conclusions</p> <p>By applying these approaches to a number of synthetic and real microarray datasets we show that for linear classifiers the optimal proportion depends on the overall number of samples available and the degree of differential expression between the classes. The optimal proportion was found to depend on the full dataset size (n) and classification accuracy - with higher accuracy and smaller <it>n </it>resulting in more assigned to the training set. The commonly used strategy of allocating 2/3rd of cases for training was close to optimal for reasonable sized datasets (<it>n </it>≥ 100) with strong signals (i.e. 85% or greater full dataset accuracy). In general, we recommend use of our nonparametric resampling approach for determing the optimal split. This approach can be applied to any dataset, using any predictor development method, to determine the best split.</p

Human basal-like breast cancer is represented by one of the two mammary tumor subtypes in dogs.

BackgroundAbout 20% of breast cancers in humans are basal-like, a subtype that is often triple-negative and difficult to treat. An effective translational model for basal-like breast cancer is currently lacking and urgently needed. To determine whether spontaneous mammary tumors in pet dogs could meet this need, we subtyped canine mammary tumors and evaluated the dog-human molecular homology at the subtype level.MethodsWe subtyped 236 canine mammary tumors from 3 studies by applying various subtyping strategies on their RNA-seq data. We then performed PAM50 classification with canine tumors alone, as well as with canine tumors combined with human breast tumors. We identified feature genes for human BLBC and luminal A subtypes via machine learning and used these genes to repeat canine-alone and cross-species tumor classifications. We investigated differential gene expression, signature gene set enrichment, expression association, mutational landscape, and other features for dog-human subtype comparison.ResultsOur independent genome-wide subtyping consistently identified two molecularly distinct subtypes among the canine tumors. One subtype is mostly basal-like and clusters with human BLBC in cross-species PAM50 and feature gene classifications, while the other subtype does not cluster with any human breast cancer subtype. Furthermore, the canine basal-like subtype recaptures key molecular features (e.g., cell cycle gene upregulation, TP53 mutation) and gene expression patterns that characterize human BLBC. It is enriched in histological subtypes that match human breast cancer, unlike the other canine subtype. However, about 33% of canine basal-like tumors are estrogen receptor negative (ER-) and progesterone receptor positive (PR+), which is rare in human breast cancer. Further analysis reveals that these ER-PR+ canine tumors harbor additional basal-like features, including upregulation of genes of interferon-γ response and of the Wnt-pluripotency pathway. Interestingly, we observed an association of PGR expression with gene silencing in all canine tumors and with the expression of T cell exhaustion markers (e.g., PDCD1) in ER-PR+ canine tumors.ConclusionsWe identify a canine mammary tumor subtype that molecularly resembles human BLBC overall and thus could serve as a vital translational model of this devastating breast cancer subtype. Our study also sheds light on the dog-human difference in the mammary tumor histology and the hormonal cycle

Factors Associated with Revision Surgery after Internal Fixation of Hip Fractures

Background: Femoral neck fractures are associated with high rates of revision surgery after management with internal fixation. Using data from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial evaluating methods of internal fixation in patients with femoral neck fractures, we investigated associations between baseline and surgical factors and the need for revision surgery to promote healing, relieve pain, treat infection or improve function over 24 months postsurgery. Additionally, we investigated factors associated with (1) hardware removal and (2) implant exchange from cancellous screws (CS) or sliding hip screw (SHS) to total hip arthroplasty, hemiarthroplasty, or another internal fixation device. Methods: We identified 15 potential factors a priori that may be associated with revision surgery, 7 with hardware removal, and 14 with implant exchange. We used multivariable Cox proportional hazards analyses in our investigation. Results: Factors associated with increased risk of revision surgery included: female sex, [hazard ratio (HR) 1.79, 95% confidence interval (CI) 1.25-2.50; P = 0.001], higher body mass index (fo

Accommodating stochastic departures from percentile invariance in causal models

Consider a clinical trial in which participants are randomized to a single-dose treatment or a placebo control and assume that the adherence level is accurately recorded. If the treatment is effective, then good adherers in the treatment group should do better than poor ad- herers because they received more drug; the treatment group data follow a dose-response curve. But, good adherers to the placebo often do better than poor adherers, so the observed adherence-response in the treatment group cannot be completely attributed to the treatment. Efron and Feldman proposed an adjustment to the observed adherence-response in the treatment group by using the adherence-response in the control group. It relies on a percentile invariance assumption under which each participant's adherence percentile within their assigned treatment group does not depend on the assigned group (active drug or placebo). The Efron and Feldman approach is valid under percentile invariance, but not necessarily under departures from it. We propose an analysis based on a generalization of percentile invariance that allows adherence percentiles to be stochastically permuted across treatment groups, using a broad class of stochastic permutation models. We show that approximate maximum likelihood estimates of the underlying dose-response curve perform well when the stochastic permutation process is correctly specified and are quite robust to model misspecification. Copyright 2003 Royal Statistical Society.