Single Covariate Research Articles

Relationships of health literacy to self-care behaviors in people with diabetes aged 60 and above: Empowerment as amediator.

To examine the relationships of health literacy to diabetes self-care behaviors (diet, physical exercise, foot care and blood glucose monitoring) through empowerment controlling for diabetes education. Potential mechanisms of how health literacy links to health outcomes have not clearly elucidated. A cross-sectional study design was used. Participants were recruited from three community health centers in South Korea from September 2016 to April 2017 using a convenience sampling method. A total of 136 people with diabetes aged 60 and above were participated in this study. The main study variables of health literacy, empowerment and diabetes self-care activities were assessed using self-reported questionnaires. This study applied a simple mediation analysis with a single covariate using the PROCESS macro, with health literacy entered as an antecedent variable, empowerment as a mediator, diabetes self-care behaviors as outcome variables and diabetes education as a covariate. After controlling for diabetes education, the indirect effects of health literacy to self-care behaviors through empowerment were significant when the self-care behaviors were particularly diet and physical exercise. Whereas, the indirect effects were not significant when the self-care behaviors were foot care and blood glucose monitoring. This study indicates that the people with higher health literacy were more empowered and those with higher empowerment were more likely to eat healthy foods and exercise. In the light of these findings, a health literacy-tailored empowerment enhancing program may be important targets for interventions promoting diabetes self-care behaviors of diet and physical exercise.

Liquid Sucrose Consumption Promotes Liver Lipid Accumulation, Fat Mass, and Glucose Intolerance without Altering Circulating Insulin Levels

A variety of factors contribute to the rising rates of obesity and to difficulties in weight reduction that exist in the worldwide population. One factor that contributes to weight gain is caloric intake via consumption of sugar‐sweetened beverages (SSBs). Therefore, in this study, we investigated specific metabolic outcomes associated with ad libitum liquid sucrose intake over 12 weeks in male C57BL/6J mice. Mice consuming 30% sucrose in liquid form gained 39.6% more weight (p < 0.001) with a 51% increase in fat mass (p< 0.01), stored 3.5‐fold more fat in liver (p< 0.05), and had impaired glucose tolerance relative to control mice (AUC p < 0.01). Intriguingly, all of these metabolic perturbations occurred without alterations in circulating insulin levels and with no detectable changes in physical activity of the mice. In addition, liver and muscle glycogen content did not increase with sucrose consumption. However, sucrose intake produced higher RQ values (p < 0.01), increased serum FGF21 levels (5.9 fold; p < 0.01), promoted browning of white adipose tissue, and enhanced whole body energy expenditure (22% increase; p < 0.01). A correlation analysis based on Pearson's correlation coefficient r indicated that energy expenditure (EE) correlated with total body mass (TBM: r = 0.91, p<0.0001), oxygen consumption (VO2: r=0.75, p<0.001), lean mass (LBM: r=0.94, p<0001) and fat‐free mass (FFM: r=0.94, p<0.0001). Oxygen consumption (VO2) also correlated with fat‐free mass (Pearson r = 0.76; p < 0.001), moderately with total body mass (Pearson r = 0.60; p < 0.05), but not with fat mass (Pearson r = 0.31; p = 0.25). Analysis of covariance (ANCOVA) for treatment effects on EE with TBM, VO2, LBM, and FFM taken as potential covariates for EE revealed that VO2 was the preferred single covariate. When the treatment means for EE were covariate adjusted for VO2, the EE mean for sucrose was significantly elevated (p<0.025). When multiple covariates were considered, TBM and VO2 were significant, but the difference between covariate‐adjusted treatment means was not significant. Congruent with the increase in RQ, there was a marked increase in the glycolytic enzyme gene expression profile in skeletal muscle, but not in epididymal white adipose tissue. We further found that sucrose consumption downregulated the expression of the dgat1 and dgat2 genes in inguinal white adipose tissue (iWAT). By contrast, dgat1 and dgat2 expression are not suppressed in iWAT from db/db mice, a separate model of obesity that displays hyperinsulinemia. Taken together, we conclude that sucrose‐induced weight gain occurs without an increase in circulating insulin and is associated with enhanced lipid accumulation in liver, but not skeletal muscle. This study shows that obesity driven by enhanced consumption of carbohydrate calories in liquid form is distinct from the hyperinsulinemia promoted by high‐fat diet or by obesity driven secondary to deficiencies in leptin receptor signaling. Thus, molecular mechanisms responsible for the obese state are likely to be dependent on the macronutrient source of calories and route of delivery.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Calculating power for the general linear multivariate model with one or more Gaussian covariates

ABSTRACTWe describe a noncentral power approximation for hypotheses about fixed predictors in general linear multivariate models with one or more Gaussian covariates. The results apply to both single and multiple parameter hypotheses. The approach extends power approximations for models with only fixed predictors, and for models with a single Gaussian covariate. The new method approximates the noncentrality parameter under the alternative hypothesis using a Taylor series expansion for the matrix-variate beta distribution of type I. We used a Monte Carlo simulation to evaluate the accuracy of both the novel power approximation, and published power approximations. The simulation study accounted for randomness in both the predictors and the errors. We varied the number of outcomes, the number of parameters in the hypothesis, the per-treatment sample size, and the correlations between the random predictors and the outcomes. We demonstrate that our approximation is more accurate than published methods, both in small and large samples. We show that the run time for a single power calculation with the new method is on the order of milliseconds, compared to an average empirical simulation time of roughly three minutes. Approximate and simulated power can be calculated using the free, open-source rPowerlib package (http://github.com/SampleSizeShop/rPowerlib).

Bayesian inference for unidirectional misclassification of a binary response trait

When assessing association between a binary trait and some covariates, the binary response may be subject to unidirectional misclassification. Unidirectional misclassification can occur when revealing a particular level of the trait is associated with a type of cost, such as a social desirability or financial cost. The feasibility of addressing misclassification is commonly obscured by model identification issues. The current paper attempts to study the efficacy of inference when the binary response variable is subject to unidirectional misclassification. From a theoretical perspective, we demonstrate that the key model parameters possess identifiability, except for the case with a single binary covariate. From a practical standpoint, the logistic model with quantitative covariates can be weakly identified, in the sense that the Fisher information matrix may be near singular. This can make learning some parameters difficult under certain parameter settings, even with quite large samples. In other cases, the stronger identification enables the model to provide more effective adjustment for unidirectional misclassification. An extension to the Poisson approximation of the binomial model reveals the identifiability of the Poisson and zero-inflated Poisson models. For fully identified models, the proposed method adjusts for misclassification based on learning from data. For binary models where there is difficulty in identification, the method is useful for sensitivity analyses on the potential impact from unidirectional misclassification.

Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy

BackgroundTo appraise the ability of a radiomics based analysis to predict local response and overall survival for patients with hepatocellular carcinoma.MethodsA set of 138 consecutive patients (112 males and 26 females, median age 66 years) presented with Barcelona Clinic Liver Cancer (BCLC) stage A to C were retrospectively studied. For a subset of these patients (106) complete information about treatment outcome, namely local control, was available. Radiomic features were computed for the clinical target volume. A total of 35 features were extracted and analyzed. Univariate analysis was used to identify clinical and radiomics significant features. Multivariate models by Cox-regression hazards model were built for local control and survival outcome. Models were evaluated by area under the curve (AUC) of receiver operating characteristic (ROC) curve. For the LC analysis, two models selecting two groups of uncorrelated features were analyzes while one single model was built for the OS analysis.ResultsThe univariate analysis lead to the identification of 15 significant radiomics features but the analysis of cross correlation showed several cross related covariates. The un-correlated variables were used to build two separate models; both resulted into a single significant radiomic covariate: model-1: energy p < 0.05, AUC of ROC 0.6659, C.I.: 0.5585–0.7732; model-2: GLNUp < 0.05, AUC 0.6396, C.I.:0.5266–0.7526.The univariate analysis for covariates significant with respect to local control resulted in 9 clinical and 13 radiomics features with multiple and complex cross-correlations. After elastic net regularization, the most significant covariates were compacity and BCLC stage, with only compacity significant to Cox model fitting (Cox model likelihood ratio test p < 0.0001, compacity p < 0.00001; AUC of the model is 0.8014 (C.I. = 0.7232–0.8797)).ConclusionA robust radiomic signature, made by one single feature was finally identified. A validation phases, based on independent set of patients is scheduled to be performed to confirm the results.

Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models

Abstract. High-resolution maps of soil properties are a prerequisite for assessing soil threats and soil functions and for fostering the sustainable use of soil resources. For many regions in the world, accurate maps of soil properties are missing, but often sparsely sampled (legacy) soil data are available. Soil property data (response) can then be related by digital soil mapping (DSM) to spatially exhaustive environmental data that describe soil-forming factors (covariates) to create spatially continuous maps. With airborne and space-borne remote sensing and multi-scale terrain analysis, large sets of covariates have become common. Building parsimonious models amenable to pedological interpretation is then a challenging task. We propose a new boosted geoadditive modelling framework (geoGAM) for DSM. The geoGAM models smooth non-linear relations between responses and single covariates and combines these model terms additively. Residual spatial autocorrelation is captured by a smooth function of spatial coordinates, and non-stationary effects are included through interactions between covariates and smooth spatial functions. The core of fully automated model building for geoGAM is component-wise gradient boosting. We illustrate the application of the geoGAM framework by using soil data from the Canton of Zurich, Switzerland. We modelled effective cation exchange capacity (ECEC) in forest topsoils as a continuous response. For agricultural land we predicted the presence of waterlogged horizons in given soil depths as binary and drainage classes as ordinal responses. For the latter we used proportional odds geoGAM, taking the ordering of the response properly into account. Fitted geoGAM contained only a few covariates (7 to 17) selected from large sets (333 covariates for forests, 498 for agricultural land). Model sparsity allowed for covariate interpretation through partial effects plots. Prediction intervals were computed by model-based bootstrapping for ECEC. The predictive performance of the fitted geoGAM, tested with independent validation data and specific skill scores for continuous, binary and ordinal responses, compared well with other studies that modelled similar soil properties. Skill score (SS) values of 0.23 to 0.53 (with SS = 1 for perfect predictions and SS = 0 for zero explained variance) were achieved depending on the response and type of score. GeoGAM combines efficient model building from large sets of covariates with effects that are easy to interpret and therefore likely raises the acceptance of DSM products by end-users.

Using Discrete-Time Survival Analysis to Examine Hazard Estimates of Biochemical Failure Among Prostate Cancer Patients

To research and explicate under what conditions the discrete-time survival method is comparable with Cox regression model respect to hazard estimation. A medical research prostate cancer data in Michigan largest health care system was used. The survival and hazard functions of biochemical failure were tested under both Cox regression and discrete-time survival models. Data were restructured from “Person-Level” to “Person-Period” before testing in the discrete-time survival model. This study was undertaken to examine the effect of four different sample sizes and two different time periods to investigate their corresponding impact survival and hazard estimation and goodness of fit statistics between Cox regression and discrete-time survival models. The univariate analysis was tested for each individual factor for different sample size n=1577, n=1213, n=809, and n=422 under both Cox regression and discrete-time survival models. All single covariates were strong predictors of biochemical failure in the univariate analysis. In the Cox regression under sample size n=1577, the patient in high-risk group, with nadir time less than 2 years, or with no hormone treatment before RT had 2.541, 3.947, or 4.974 times more likely to have biochemical failure than the patient in the intermediate-risk group, nadir time greater than 2 year, or with hormone treatment before RT. In the discrete-time survival model with 10 time periods, the hazard estimates were 2.717 times for high-risk patient group, 5.457 times for no hormone receiver before RT, and 4.161 times higher for patients with less than 2 year nadir time. Similarly, in the discrete-time model with 5 time periods, the hazard estimates were 3.242, 5.150 and 3.303 times higher correspondingly. Both discrete-time survival model and Cox regression model provided similar hazard estimations. Sample size had an impact on survival analysis and hazard estimates. Small sample size had larger hazard estimation in general.Abstract 2999VariableCox Regression ModelDiscrete-time Survival Model5 Time PeriodsDiscrete-time Survival Model10 Time PeriodsN=1577N=6041N=9692HZ95% CIHZ95% CIHZ95% CIHigh Risk2.541∗∗∗2.141 - 3.0173.242∗∗∗2.474 - 4.2472.717∗∗∗2.262 - 3.265Nadir Time < 2 yrs3.947∗∗∗3.242 – 4.8053.303∗∗∗2.463 – 4.4294.161∗∗∗3.389 – 5.109No HT before RT4.974∗∗∗3.856 – 6.4165.150∗∗∗3.493 – 7.5945.457∗∗∗4.183 – 7.120Goodness-of-fit-2LL7106.81787.93825n parameters3813AIC7166.81803.93851BIC7182.91857.63944.3 Open table in a new tab

A GABRA2 polymorphism improves a model for prediction of drinking initiation.

Survival analysis was used to explore the addition of a single nucleotide polymorphism (SNP) and covariates (sex, interview age, and ancestry) on a previously published model's ability to predict onset of drinking. A SNP variant of rs279871, in the chromosome 4 gene encoding gamma-aminobutyric acid receptor (GABRA2), was selected due to its associations with alcoholism in young adults and with behaviors that increased risk for early drinking. A subsample of 674 adolescents (ages 14-17) participating in the Collaborative Study on the Genetics of Alcoholism (COGA) was examined using a previously derived Cox proportional hazards model containing: 1) number of non-drinking related conduct disorder (CD) symptoms, 2) membership in a high-risk alcohol-dependent (AD) family, 3) most best friends drank (MBFD), 4) Achenbach Youth Self Report (YSR) externalizing score, and 5) YSR social problems score. The above covariates along with the SNP variant of GABRA2, rs279871, were added to this model. Five new prototype models were examined. The most parsimonious model was chosen based on likelihood ratio tests and model fit statistics. The final model contained four of the five original predictors (YSR social problems score was no longer significant and hence dropped from subsequent models), the three covariates, and a recessive GABRA2 rs279871 TT genotype (two copies of the high-risk allele containing thymine). The model indicated that adolescents with the high-risk TT genotype were more likely to begin drinking than those without this genotype. The joint effect of the gene (rs279871 TT genotype) and environment (MBFD) on adolescent alcohol initiation is additive, but not interactive, after controlling for behavior problems (CD and YSR externalizing score). This suggests that the impact of the high-risk TT genotype on the onset of drinking is affected by controlling for peer drinking and does not include genotype-by-environment interactions.

Attributing changes in the distribution of species abundance to weather variables using the example of British breeding birds

Abstract Modelling spatio‐temporal changes in species abundance and attributing those changes to potential drivers such as climate, is an important but difficult problem. The standard approach for incorporating climatic variables into such models is to include each weather variable as a single covariate, whose effect is expressed through a low‐order polynomial or smoother in an additive model. This, however, confounds the spatial and temporal effects of the covariates. We developed a novel approach to distinguish between three types of change in any particular weather covariate. We decomposed the weather covariate into three new covariates by separating out temporal variation in weather (averaging over space), spatial variation in weather (averaging over years) and a space–time anomaly term (residual variation). These three covariates were each fitted separately in the models. We illustrate the approach using generalized additive models applied to count data for a selection of species from the UK's Breeding Bird Survey, 1994–2013. The weather covariates considered were the mean temperatures during the preceding winter and temperatures and rainfall during the preceding breeding season. We compare models that include these covariates directly with models including decomposed components of the same covariates, considering both linear and smooth relationships. The lowest QAIC values were always associated with a decomposed weather covariate model. Different relationships between counts and the three new covariates provided strong evidence that the effects of changes in covariate values depended on whether changes took place in space, in time, or in the space–time anomaly. These results promote caution in predicting species distribution and abundance in future climate, based on relationships that are largely determined by environmental variation over space. Our methods estimate the effect of temporal changes in weather, while accounting for spatial effects of long‐term climate, improving inference on overall and/or localized effects of climate change. With increasing availability of large‐scale datasets, need is growing for appropriate analytical tools. The proposed decomposition of the weather variables represents an important advance by eliminating the confounding issue often inherent in analyses of large‐scale datasets.

Smooth copula-based estimation of the conditional density function with a single covariate

Some recent papers deal with smooth nonparametric estimators for copula functions and copula derivatives. These papers contain results on copula-based Bernstein estimators for conditional distribution functions and related functionals such as regression and quantile functions. The focus in the present paper is on new copula-based smooth Bernstein estimators for the conditional density. Our approach avoids going through separate density estimation of numerator and denominator. Our estimator is defined as a smoother of the copula-based Bernstein estimator of the conditional distribution function. We establish asymptotic properties of bias and variance and discuss the asymptotic mean squared error in terms of the smoothing parameters. We also obtain the asymptotic normality of the new estimator. In a simulation study we show the good performance of the new estimator in comparison with other estimators proposed in the literature.

Reversals of Least-Square Estimates and Model-Invariant Estimation for Directions of Unique Effects

ABSTRACTWhen a linear model is adjusted to control for additional explanatory variables, the sign of a fitted coefficient may reverse. Here, these reversals are studied using coefficients of determination. The resulting theory can be used to determine directions of unique effects in the presence of model uncertainty. This process is called model-invariant estimation when the estimates are invariant across changes to the model structure. When a single covariate is added, the reversal region can be understood geometrically as an elliptical cone of two nappes with an axis of symmetry relating to a best-possible condition for a reversal using a single coefficient of determination. When a set of covariates are added to a model with a single explanatory variable, model-invariant estimation can be implemented using subject matter knowledge. More general theory with partial coefficients is applicable to analysis of large datasets. Applications are demonstrated with dietary health data from the United Nations.

The Risk of Hepatobiliary Cancer with Metabolic Syndrome: A Nested Case-Control Study

Introduction The incidence of hepatobiliary and gall bladder cancers is increasing. The age standardised incidence rates of liver cancers has increased by 224% for males and 227% for females over the last few decades with similar trends in the incidence of bile duct and gall bladder cancers. It is unclear if this rise is related to increasing trends in in obesity, metabolic syndrome and lifestyle changes. The current evidence base is unclear. Aim To determine the association between obesity, metabolic syndrome and hepatobiliary cancer using primary care data. Method A nested case-control study was performed using the THIN (The Health Improvement Network) database in the UK. Read codes were used to capture patients with a diagnosis of liver, bile duct and gall bladder cancers and these patients were matched in a 1:2 fashion with controls without a diagnosis of cancer by age, GP practice and gender, with at least one year of follow up on the database. Data on co-morbidities, events (GP or hospital visits), drug history, anthropometric data (weight, log-transformed body mass index (BMI), blood pressure, cholesterol and glucose levels) and data on smoking and alcohol consumption was extracted from the database and analysed in an analytical model for potential associations between hepatobiliary cancer and obesity/metabolic syndrome. Results 4287 patients (62% male, 38% female) with hepatobiliary cancers were matched with 8574 controls. On univariate analysis, age (0.99 (0.98–0.99), p On step-wise forward conditional logistic regression, age (0.98 (0.977–0.98), p On modelling log BMI, diabetes and hypertension as a single covariate, there was a significant association of a combination of BMI, diabetes and hypertension with hepatobiliary cancer (1.59 (1,49–1.69), p Conclusion Obesity and metabolic syndrome is significantly associated with the risk of hepatobiliary cancer. Statin use seems to be protective. Disclosure of Interest None Declared

Iron and Parkinson's disease: A systematic review and meta-analysis.

A possible association between iron serum levels and Parkinson's disease(PD) using a meta‑analytic approach was evaluated. A systematic MEDLINE search was conducted to identify published observational, case‑control studies dealing with the association between iron blood levels and PD. In both groups, iron blood levels were extracted as means and standard deviations to calculate the standardized mean differences(SMDs) with 95% confidence intervals(CIs). Heterogeneity of selected studies was investigated. Then, a meta-analysis was performed applying a random effects model. Possible causes of bias were also examined. A meta-regression analysis was finally conducted to investigate whether associations varied according to specified confounding factors. Of 155 studies detected by the research strategy, a total of 23case‑control studies with full available data were selected based on the adopted criteria. A small, around zero, overall SMD of -0.052 (95%CI,-0.303-0.2) was estimated, indicating no substantial differences between groups among selected studies. High heterogeneity among studies was detected(I2=91.42%; p<0.001). By performing a meta-regression analysis considering single available demographic, geographical and clinical covariates, no significant association was detected. Based on our systematic revision and meta-analysis of available case‑control studies, there was not sufficient evidence supporting a possible significant association between iron serum levels and PD as compared to controls. Principal reasons should be sought in the elevated methodological heterogeneity we found among available studies. A particular attention should be paid on bias and confounding effects to limit heterogeneity among studies and to facilitate the summary of results.

Association of the DBH Polymorphism rs3025343 With Smoking Cessation in a Large Population-Based Sample.

Genetic variations in DBH-gene and its surroundings have been shown to associate with smoking behavior including smoking cessation in several studies. In this study we replicate and measure the effect size for association between DBH polymorphism rs3025343 and smoking cessation in a large population-based sample while examining environmental factors that could relate to the association. We studied 11 926 adult subjects from four surveys of the National FINRISK Study. The analysis was restricted to either current or former smokers. Logistic and linear regression analyses were conducted to investigate the relationships of the single nucleotide polymorphism (SNP), covariates, smoking cessation, and smoking severity (cotinine, CPD). Gene-environment interactions were tested by likelihood-ratio test. The association between rs3025343 and smoking cessation (prevalence odds ratio, OR = 1.12, p = .094, 95%CI = 0.98-1.30) was replicated identically with the GWAS study of The Tobacco and Genetics Consortium (OR = 1.12, 95%CI = 1.08-1.18). None of our tested phenotypes significantly influenced the association between rs3025343 and smoking cessation. Overall, marital status, education, depression, alcohol use, self-rated health, and chronic obstructive pulmonary disease (COPD) showed phenotypic associations with smoking cessation, but the association of various phenotypes with smoking cessation did not vary by genotype. The current study replicates the effect size for the association between rs3025343 and smoking cessation despite lack of overall significance due to smaller sample size. We could not show environmental influences on the association of rs3025343 with smoking cessation. Our study replicates the direction and strength of the association of DBH polymorphism rs3025343 with smoking cessation. We could not detect environmental influences on the strength of the association of rs3025343 with smoking cessation, but the limited power of our analysis needs to be taken into account.

Subject-specific modelling of paired comparison data: A lasso-type penalty approach

In traditional paired comparison models heterogeneity in the population is simply ignored and it is assumed that all persons or subjects have the same preference structure. In the models considered here the preference of an object over another object is explicitly modelled as depending on subject-specific covariates, therefore allowing for heterogeneity in the population. Since by construction the models contain a large number of parameters we propose to use penalized estimation procedures to obtain estimates of the parameters. The used regularized estimation approach penalizes the differences between the parameters corresponding to single covariates. It enforces variable selection and allows to find clusters of objects with respect to covariates. We consider simple binary but also ordinal paired comparisons models. The method is applied to data from a pre-election study from Germany.

Bayesian two-component measurement error modelling for survival analysis using INLA—A case study on cardiovascular disease mortality in Switzerland

Measurement error (ME) in explanatory variables is a common problem in regression and survival analysis, as it may cause bias in the estimated parameters. It is shown how the integrated nested Laplace approximations (INLA) method can handle classical and Berkson ME in a single explanatory variable, illustrated for the case of a Weibull regression model. To this end, a two-component error model to account for a mix of Berkson and classical ME in a single covariate is introduced and applied to a study on cardiovascular disease (CVD) mortality in Switzerland. In particular, the model was used to correct for error in the self-reported mean daily number of cigarettes smoked, as well as in reportings of systolic blood pressure (SBP). Both variables suffer from classical error induced by an imprecision, either due to misremembering of study participants (cigarettes), or due to practical difficulties in obtaining accurate measurements (SBP), but also from a Berkson-type error that is induced by a rounding behaviour, also known as digit preference. In both cases, the effect estimates increased when the error was taken into account. Therefore, an important conclusion is that ME modelling in survival analysis is relevant, and a ready-to-use Bayesian solution including R-code is provided.

Individual treatment effect prediction for amyotrophic lateral sclerosis patients.

A treatment for a complicated disease might be helpful for some but not all patients, which makes predicting the treatment effect for new patients important yet challenging. Here we develop a method for predicting the treatment effect based on patient characteristics and use it for predicting the effect of the only drug (Riluzole) approved for treating amyotrophic lateral sclerosis. Our proposed method of model-based random forests detects similarities in the treatment effect among patients and on this basis computes personalised models for new patients. The entire procedure focuses on a base model, which usually contains the treatment indicator as a single covariate and takes the survival time or a health or treatment success measurement as primary outcome. This base model is used both to grow the model-based trees within the forest, in which the patient characteristics that interact with the treatment are split variables, and to compute the personalised models, in which the similarity measurements enter as weights. We applied the personalised models using data from several clinical trials for amyotrophic lateral sclerosis from the Pooled Resource Open-Access Clinical Trials database. Our results indicate that some amyotrophic lateral sclerosis patients benefit more from the drug Riluzole than others. Our method allows gradually shifting from stratified medicine to personalised medicine and can also be used in assessing the treatment effect for other diseases studied in a clinical trial.

Expertise and Ideology in Statistical Evaluation of Circumcision for Protection against HIV Infection

Aim: To critically evaluate data and arguments by Van Howe defending his stance opposing male circumcision (MC), in particular his meta-regression analyses evaluating the ability of MC to reduce HIV infection risk in heterosexual populations within and outside Africa. Methods: We performed metaregression analysis of log odds of HIV infection between uncircumcised and circumcised men using a single covariate (MC prevalence) in the meta-regression model involving the metareg package in STATA 13 for 103 populations worldwide and for populations within Africa. The meta-regression of log odds and MC prevalence was fitted to a line, as were empirical Bayes estimates resulting from post-estimation. Results: Our critical evaluation of Van Howe’s arguments attempting to undermine the scientific evidence in support of the benefits of MC in protection of men against HIV during heterosexual intercourse, as well as other infections and conditions, together with his use of statistics to support his beliefs, revealed serious flaws, obfuscation and missing data. We therefore performed our own meta-regression analysis using a trivariate model. Doing so revealed that for MC prevalences of 50%, 75% and 100% for general populations within Africa, odds ratios for HIV risk in uncircumcised vs. circumcised men were 1.35, 1.58 and 1.85, respectively. Our meta-regression analysis of data for all countries yielded similar findings. For a general population outside Africa with 100% MC prevalence, OR was 1.5. Van Howe failed to acknowledge that since MC prevalence in US whites (91%) and blacks (76%) exceeds 75% his results support MC having a protective effect in those population groups. Conclusions: The protective effect of MC against HIV infection during heterosexual intercourse applies to populations both within and outside Africa. The debate engineered by MC opponents, and led by Van Howe, now appears to have run its course. The scientific evidence has prevailed.

Provider Influences on Sperm Banking Outcomes Among Adolescent Males Newly Diagnosed With Cancer

PurposeThe purpose of this study was to examine provider communication and sociodemographic factors which associate with sperm banking outcomes in at-risk adolescents newly diagnosed with cancer. MethodsA prospective single-group quasi-experimental study design was used to test the contributions of provider factors on sperm banking outcomes. Medical providers (N = 52, 86.5% oncologists) and 99 of their at-risk adolescent patients from eight leading pediatric oncology centers in North America completed questionnaires querying provider factors and patient sperm banking outcomes. Logistic regression with single covariates was used to test each provider factor as a potential correlate of the two binary sperm banking study outcomes (collection attempt/no attempt and successful sperm bank/no bank). Multicovariate logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CIs) for specified banking outcomes. ResultsFertility referral (OR, 9.01; 95% CI, 2.54–31.90; p < .001) and provider comfort/skills in negotiating barriers to sperm banking with families (OR, 1.94; 95% CI, 1.03–3.63; p < .04) were associated with collection attempts. Adolescents who were referred for a specialized fertility consultation were also almost five times more likely to successfully bank (OR, 4.96; 95% CI, 1.54–16.00; p < .01) compared to those who were not. ConclusionsProvider training in communicating/managing adolescents and their families about sperm banking, and increasing utilization of fertility preservation referrals, should increase the proportion of at-risk males preserving fertility before treatment initiation.

EFFECTS OF DIFFERENT TYPE OF COVARIATES AND SAMPLE SIZE ON PARAMETER ESTIMATION FOR MULTINOMIAL LOGISTIC REGRESSION MODEL

The sample size and distributions of covariate may affect many statistical modeling techniques. This paper investigates the effects of sample size and data distribution on parameter estimates for multinomial logistic regression. A simulation study was conducted for different distributions (symmetric normal, positively skewed, negatively skewed) for the continuous covariates. In addition, we simulate categorical covariates to investigate their effects on parameter estimation for the multinomial logistic regression model. The simulation results show that the effect of skewed and categorical covariate reduces as sample size increases. The parameter estimates for normal distribution covariate apparently are less affected by sample size. For multinomial logistic regression model with a single covariate study, a sample size of at least 300 is required to obtain unbiased estimates when the covariate is positively skewed or is a categorical covariate. A much larger sample size is required when covariates are negatively skewed.