A score test for assessing the cured proportion in the long‐term survivor mixture model

The long‐term survivor mixture model is commonly applied to analyse survival data when some individuals may never experience the failure event of interest. A score test is presented to assess whether the cured proportion is significant to justify the long‐term survivor mixture model. Sampling distribution and power of the test statistic are evaluated by simulation studies. The results confirm that the proposed test statistic performs well in finite sample situations. The test procedure is illustrated using a breast cancer survival data set and the clustered multivariate failure times from a multi‐centre clinical trial of carcinoma. Copyright © 2009 John Wiley & Sons, Ltd.     

Testing for change-point in the covariate effects based on the Cox regression model

Models with change-point in covariates have wide applications in cancer research with the response being the time to a certain event. A Cox model with change-point in covariate is considered at which the pattern of the change-point effects can be flexibly specified. To test for the existence of the change-point effects, three statistical tests, namely, the maximal score, maximal normalized score, and maximal Wald tests are proposed. The asymptotic properties of the test statistics are established. Monte Carlo approaches to simulate the critical values are suggested. A large-scale simulation study is carried out to study the finite sample performance of the proposed test statistics under the null hypothesis of no change-points and various alternative hypothesis settings. Each of the proposed methods provides a natural estimate for the location of the change-point, but it is found that the performance of the maximal score test can be sensitive to the true location of the change-point in some cases, while the performance of the maximal Wald test is very satisfactory in general even in cases with moderate sample size. For illustration, the proposed methods are applied to two medical datasets concerning patients with primary biliary cirrhosis and breast cancer, respectively.     

Joint modeling of repeated multivariate cognitive measures and competing risks of dementia and death: a latent process and latent class approach

Joint models initially dedicated to a single longitudinal marker and a single time‐to‐event need to be extended to account for the rich longitudinal data of cohort studies. Multiple causes of clinical progression are indeed usually observed, and multiple longitudinal markers are collected when the true latent trait of interest is hard to capture (e.g., quality of life, functional dependency, and cognitive level). These multivariate and longitudinal data also usually have nonstandard distributions (discrete, asymmetric, bounded, etc.). We propose a joint model based on a latent process and latent classes to analyze simultaneously such multiple longitudinal markers of different natures, and multiple causes of progression. A latent process model describes the latent trait of interest and links it to the observed longitudinal outcomes using flexible measurement models adapted to different types of data, and a latent class structure links the longitudinal and cause‐specific survival models. The joint model is estimated in the maximum likelihood framework. A score test is developed to evaluate the assumption of conditional independence of the longitudinal markers and each cause of progression given the latent classes. In addition, individual dynamic cumulative incidences of each cause of progression based on the repeated marker data are derived. The methodology is validated in a simulation study and applied on real data about cognitive aging obtained from a large population‐based study. The aim is to predict the risk of dementia by accounting for the competing death according to the profiles of semantic memory measured by two asymmetric psychometric tests. Copyright © 2015 John Wiley & Sons, Ltd.     

Derivation and external validation of a simple prediction model for the diagnosis of type 2 Diabetes Mellitus in the Brazilian urban population

A risk score model was developed based in a population of 1,224 individuals from the general population without known diabetes aging 35 years or more from an urban Brazilian population sample in order to select individuals who should be screened in subsequent testing and improve the efficacy of public health assurance. External validation was performed in a second, independent, population from a different city ascertained through a similar epidemiological protocol. The risk score was developed by multiple logistic regression and model performance and cutoff values were derived from a receiver operating characteristic curve. Model’s capacity of predicting fasting blood glucose levels was tested analyzing data from a 5-year follow-up protocol conducted in the general population. Items independently and significantly associated with diabetes were age, BMI and known hypertension. Sensitivity, specificity and proportion of further testing necessary for the best cutoff value were 75.9, 66.9 and 37.2%, respectively. External validation confirmed the model’s adequacy (AUC equal to 0.72). Finally, model score was also capable of predicting fasting blood glucose progression in non-diabetic individuals in a 5-year follow-up period. In conclusion, this simple diabetes risk score was able to identify individuals with an increased likelihood of having diabetes and it can be used to stratify subpopulations in which performing of subsequent tests is necessary and probably cost-effective.     

Classification using ensemble learning under weighted misclassification loss

Binary classification rules based on covariates typically depend on simple loss functions such as zero-one misclassification. Some cases may require more complex loss functions. For example, individual-level monitoring of HIV-infected individuals on antiretroviral therapy requires periodic assessment of treatment failure, defined as having a viral load (VL) value above a certain threshold. In some resource limited settings, VL tests may be limited by cost or technology, and diagnoses are based on other clinical markers. Depending on scenario, higher premium may be placed on avoiding false-positives, which brings greater cost and reduced treatment options. Here, the optimal rule is determined by minimizing a weighted misclassification loss/risk. We propose a method for finding and cross-validating optimal binary classification rules under weighted misclassification loss. We focus on rules comprising a prediction score and an associated threshold, where the score is derived using an ensemble learner. Simulations and examples show that our method, which derives the score and threshold jointly, more accurately estimates overall risk and has better operating characteristics compared with methods that derive the score first and the cutoff conditionally on the score especially for finite samples.     

Bias in progression‐free survival analysis due to intermittent assessment of progression

Cancer clinical trials are routinely designed to assess the effect of treatment on disease progression and death, often in terms of a composite endpoint called progression‐free survival. When progression status is known only at periodic assessment times, the progression time is interval censored, and complications arise in the analysis of progression‐free survival. Despite the advances in methods for dealing with interval‐censored data, naive methods such as right‐endpoint imputation are widely adopted in this setting. We examine the asymptotic and empirical properties of estimators of the marginal progression‐free survival functions and associated treatment effects under this scheme. Specifically, we explore the determinants of the asymptotic bias and point out that there is typically a loss in power of tests for treatment effects. Copyright © 2015 John Wiley & Sons, Ltd.     

A Cochran–Armitage‐type and a score‐free global test for multivariate ordinal data

We propose a Cochran–Armitage‐type and a score‐free global test that can be used to assess the presence of an association between a set of ordinally scaled covariates and an outcome variable within the range of generalized linear models. Both tests are developed within the framework of the well‐established ‘global test’ methodology and as such are feasible in high‐dimensional data situations under any correlation and enable adjustment for covariates. The Cochran–Armitage‐type test, for which an intimate connection with the traditional score‐based Cochran–Armitage test is shown, rests upon explicit assumptions on the distances between the covariates' ordered categories. The score‐free test, in contrast, parametrizes these distances and thus keeps them flexible, rendering it ideally suited for covariates measured on an ordinal scale. As confirmed by means of simulations, the Cochran–Armitage‐type test focuses its power on set‐outcome relationships where the distances between the covariates' categories are equal or close to those assumed, whereas the score‐free test spreads its power over a wide range of possible set‐outcome relationships, putting more emphasis on monotonic than on non‐monotonic ones. Based on the tests' power properties, it is discussed when to favour one or the other, and the practical merits of both of them are illustrated by an application in the field of rehabilitation medicine. Our proposed tests are implemented in the R package globaltest . Copyright © 2016 John Wiley & Sons, Ltd.     

Quantifying the bias due to observed individual confounders in causal treatment effect estimates

It is often of interest to use observational data to estimate the causal effect of a target exposure or treatment on an outcome. When estimating the treatment effect, it is essential to appropriately adjust for selection bias due to observed confounders using, for example, propensity score weighting. Selection bias due to confounders occurs when individuals who are treated are substantially different from those who are untreated with respect to covariates that are also associated with the outcome. A comparison of the unadjusted, naive treatment effect estimate with the propensity score adjusted treatment effect estimate provides an estimate of the selection bias due to these observed confounders. In this article, we propose methods to identify the observed covariate that explains the largest proportion of the estimated selection bias. Identification of the most influential observed covariate or covariates is important in resource-sensitive settings where the number of covariates obtained from individuals needs to be minimized due to cost and/or patient burden and in settings where this covariate can provide actionable information to healthcare agencies, providers, and stakeholders. We propose straightforward parametric and nonparametric procedures to examine the role of observed covariates and quantify the proportion of the observed selection bias explained by each covariate. We demonstrate good finite sample performance of our proposed estimates using a simulation study and use our procedures to identify the most influential covariates that explain the observed selection bias in estimating the causal effect of alcohol use on progression of Huntington's disease, a rare neurological disease.     

Micronutrients and HIV disease: a review pre- and post-HAART.

Low serum micronutrient levels are common in HIV-positive individuals and have been associated with immune impairment, HIV disease progression, and increased mortality. Studies of micronutrient supplementation have yielded conflicting results, although several large trials suggest that multivitamin supplements, but not vitamin A, may decrease morbidity and mortality in some HIV-positive populations. Studies also suggest that antioxidant supplementation may decrease markers of oxidative stress in individuals with HIV, while selenium may enhance immune function by modulating cytokine production. Clearly, more research is needed, but current knowledge supports the use of a multivitamin supplement as a low-cost adjunct to antiretroviral treatment.     

Effects of proactive population-based nephrologist oversight on progression of chronic kidney disease: a retrospective control analysis

ABSTRACT: BACKGROUND: Benefits of early nephrology care are well-established, but as many as 40 % of U.S. patients with end-stage renal disease (ESRD) do not see a nephrologist before its onset. Our objective was to evaluate the effect of proactive, population-based nephrologist oversight (PPNO) on chronic kidney disease (CKD) progression. METHODS: Retrospective control analysis of Kaiser Permanente Hawaii members with CKD using propensity score matching methods. We matched 2,938 control and case pairs of individuals with stage 3a CKD for the pre-PPNO period (2001--2004) and post-PPNO period (2005--2008) that were similar in other characteristics: age, gender, and the presence of diabetes and hypertension. After three years, we classified the stage outcomes for all individuals. We assessed the PPNO effect across all stages of progression with a chi2- test. We used the z-score test to assess the proportional differences in progression within a stage. RESULTS: The progression within the post-PPNO period was less severe and significantly different from the pre-PPNO period (p = 0.027). Within the stages, there were 2.6 % more individuals remaining in 3a in the post-period (95 % confidence interval [CI], 1.5 % to 3.8 %; P value < 0.00001). Progression from 3a to 3b was 2.2 % less in the post-period (95 % [CI], 0.7 % to 3.6 %; P value = 0.0017), 3a to 4/5 was 0.2 % less (95 % CI, 0.0 % to 0.87 %; P value = 0.26), and 3a to ESRD was 0.24 % less (95 % CI, 0.0 % to 0.66 %, P value = 0.10). CONCLUSIONS: Proactive, population-based nephrologist oversight was associated with a statistically significant decrease in progression. With enabling health information technology, risk stratification and targeted intervention by collaborative primary and specialty care achieves population-level care improvements. This model may be applicable to other chronic conditions.     

On the analysis of viral load endpoints in HIV vaccine trials

First generation HIV vaccines are not likely to provide complete protection from HIV-1 infection. Therefore, it is important to assess a vaccine's effect on disease progression and infectiousness of infected vaccinees in an efficacy trial; however, direct assessment of such vaccine effects is not feasible within current trial designs. Viral load in HIV-infected individuals correlates with infectiousness and disease progression in a natural history setting, and thus is a reasonable candidate for a surrogate outcome in vaccine efficacy trials. We consider comparisons of viral load of infected vaccinees to that of infected trial participants in the control group. Dramatic differences in viral loads between these groups would suggest a vaccine effect on disease progression. However, modest differences, even if statistically significant, could be consistent with an imperfect vaccine effect on susceptibility to infection and not an effect on disease progression, that is, a selection effect of the vaccine. Thus, the usual statistical tests for no difference between groups do not test the biologically and clinically relevant hypothesis. We propose a model for the possible selective effects of a vaccine and develop several test statistics for assessing a direct effect of the vaccine on viral load given this selection model. Finite sample properties of these tests are evaluated using computer simulations.     

Optimal two-stage genotyping in population-based association studies

We propose a cost-effective two-stage approach to investigate gene-disease associations when testing a large number of candidate markers using a case-control design. Under this approach, all the markers are genotyped and tested at stage 1 using a subset of affected cases and unaffected controls, and the most promising markers are genotyped on the remaining individuals and tested using all the individuals at stage 2. The sample size at stage 1 is chosen such that the power to detect the true markers of association is 1-beta(1) at significance level alpha(1). The most promising markers are tested at significance level alpha(2) at stage 2. In contrast, a one-stage approach would evaluate and test all the markers on all the cases and controls to identify the markers significantly associated with the disease. The goal is to determine the two-stage parameters (alpha(1), beta(1), alpha(2)) that minimize the cost of the study such that the desired overall significance is alpha and the desired power is close to 1-beta, the power of the one-stage approach. We provide analytic formulae to estimate the two-stage parameters. The properties of the two-stage approach are evaluated under various parametric configurations and compared with those of the corresponding one-stage approach. The optimal two-stage procedure does not depend on the signal of the markers associated with the study. Further, when there is a large number of markers, the optimal procedure is not substantially influenced by the total number of markers associated with the disease. The results show that, compared to a one-stage approach, a two-stage procedure typically halves the cost of the study.     

A test for the relationship between a time-varying marker and both recovery and progression with missing data

For clinical studies of chronic diseases, patients are followed to determine whether treatment results in either improvement or decline in their clinical status. During periodic exams, laboratory specimens are collected that are believed to reflect both the progression and improvement in the disease status. Often patients miss visits and return with a changed disease status, resulting in interval-censored laboratory markers and indicators of disease status. The goal of this paper is to propose a single test that would evaluate the relationship between a longitudinal marker and clinical progression or recovery when missing visits result in interval-censored covariate and outcome data. We apply our test to evaluate the relationship between treatment compliance and progression and remission of renal disease in diabetic patients.     

Breastfeeding in infancy is not associated with inflammatory status in healthy adolescents

It has been suggested that breast-feeding (BF) may be associated with a decreased risk of cardiovascular disease in adulthood. A low-grade inflammation is associated with an increased risk of cardiovascular disease, even in apparently healthy children. The objective of this study was to assess the potential modulating effect of BF on the inflammatory status of healthy adolescents. Information on BF (duration) was obtained from parental records in 484 of 1040 healthy European urban adolescents (56.4% females) that had a blood sample obtained as part of the Healthy Lifestyle in Europe by Nutrition and Adolescence study. Blood serum inflammatory markers were measured, including high sensitivity C-reactive protein, complement factors 3 and 4, ceruloplasmin, adhesion molecules (L-selectin and soluble endothelial selectin, soluble vascular cell adhesion molecule 1, and intercellular adhesion molecule 1), cytokines, TGFβ1, and white blood cells. After univariate analysis, a propensity score, including the potential confounding factors, was computed and used to assess the association between BF and selected inflammatory markers. BF was not significantly associated with any of the selected inflammatory markers after adjustment for gender and propensity score. In our study, BF was not associated with low-grade inflammatory status in healthy adolescents, suggesting that the potential cardiovascular benefits of BF are related to other mechanisms than modulation of inflammation or might become relevant at a later age. Groups at high risk for cardiovascular disease should be a target for further research concerning the effects of BF.     

A multiple testing procedure to associate gene expression levels with survival

In many microarray studies the primary objective is to identify, from a large panel of genes, those which are prognostic markers of a censored survival endpoint such as time to disease recurrence or death. Often, these genes are considered prognostic in that their respective expressions are associated with the survival endpoint of interest. To assess this association requires specifying an appropriate measure of association, a suitable test statistic and, as the number of genes is large, proper handling of multiplicity issues. In this paper, we will address these issues by utilizing a general correlation measure, a non-parametric test statistic, and control of the family-wise error rate by employing permutation resampling. Comprehensive simulation studies are conducted to investigate the statistical properties of the proposed procedure. The proposed method is applied to a recently published data set on patients with lung cancer.     

Genetic determinant factors of resistance to HIV infection and of control of progression to AIDS: implications on pathogenesis and therapeutic approaches for the eradication of HIV. A review

In this review, we describe and discuss the genetic factors that, up to some point, determine resistance to the infection and control the progression of the disease in HIV-infected individuals. Genetic factors may account for non-progression or slow progression of the disease in some of so called long-term non progressors HIV-infected individuals. In general, this group shows no symptoms for more than 10 years, while their circulating T CD4+ cells levels remain stable and they usually have a low virus load. Even though non-progression and rapid progression phenomenon are still not fully understood, there probability exists that some class I and class II MHC alleles are associated with a greater or smaller risk to develop AIDS. Class I HLA-B*35 and Cw*04 alleles are the ones commonly associated with the rapid transition of the infection into AIDS. In contrast, heterozygosity for class I HLA alleles and, particularly, the absence of HLA-B*35 and Cw*04 may contribute to non-progression. Studies which set forward other HLA alleles as possibly taking part of the pathogenic mechanism of non-progression are also described; although, relevant methodological problems can be noticed. Furthermore, this review explains and discusses allelic variations for some of the components of the chemokine receptors family, particularly the genes which codify for CCR5 and CCR2 and other genetic factors such as the SDF1-3'. A variant of the alpha SDF1 chemokine gene that have been associated with AIDS' slow progression or non-progression in HIV-infected individuals. As a whole, the factors described in this review are those that influence the natural history of the disease due to HIV and give an example of what genetic or multigenetic influence can have over the pattern of evolution of HIV infection. Finally, we mention the possible implications that the identification of the genetic markers has in the pathogenesis of HIV disease and in the development of the new therapeutic strategies to control or eliminate HIV.     

How to assess functional status: A new muscle quality index

Aging is associated with decreases in muscle mass, muscle strength and muscle power, with muscle strength declining at a higher rate than muscle mass, but at a lower rate than muscle power. This progressive mismatch suggests a deterioration of muscle “quality” that may lead to functional incapacities. Although it may be difficult to synthesize the concept of muscle quality, the aim of the present paper was to propose a clinical definition of muscle quality in regard to the functional status. Accordingly, the muscle strength or muscle power per unit of muscle mass ratios appear to be clinically relevant markers of muscle quality. Several mechanisms susceptible to influence these ratios have been described, among which age, gender, sex hormones, obesity, physical activity and fibrosis. Various methods to assess muscle quality in both the clinical and research fields have also been listed, with a particular interest for the tests used to measure muscle power. Finally, we proposed a clinical screening tool to detect individuals at risk of functional incapacities. Briefly, the muscle quality score is based on handgrip strength assessment by hand dynamometer, muscle mass measurement by bioelectrical analysis, and leg muscle power estimation using a chair stand test.     

Joint modeling of binary response and survival for clustered data in clinical trials

In clinical trials, it is often desirable to evaluate the effect of a prognostic factor such as a marker response on a survival outcome. However, the marker response and survival outcome are usually associated with some potentially unobservable factors. In this case, the conventional statistical methods that model these two outcomes separately may not be appropriate. In this paper, we propose a joint model for marker response and survival outcomes for clustered data, providing efficient statistical inference by considering these two outcomes simultaneously. We focus on a special type of marker response: a binary outcome, which is investigated together with survival data using a cluster-specific multivariate random effect variable. A multivariate penalized likelihood method is developed to make statistical inference for the joint model. However, the standard errors obtained from the penalized likelihood method are usually underestimated. This issue is addressed using a jackknife resampling method to obtain a consistent estimate of standard errors. We conduct extensive simulation studies to assess the finite sample performance of the proposed joint model and inference methods in different scenarios. The simulation studies show that the proposed joint model has excellent finite sample properties compared to the separate models when there exists an underlying association between the marker response and survival data. Finally, we apply the proposed method to a symptom control study conducted by Canadian Cancer Trials Group to explore the prognostic effect of covariates on pain control and overall survival.     

Comparison of statistics in association tests of genetic markers for survival outcomes

Computationally efficient statistical tests are needed in association testing of large scale genetic markers for survival outcomes. In this study, we explore several test statistics based on the Cox proportional hazards model for survival data. First, we consider the classical partial likelihood‐based Wald and score tests. A revised way to compute the score statistics is explored to improve the computational efficiency. Next, we propose a Cox–Snell residual‐based score test, which allows us to handle the controlling variables more conveniently. We also illustrated the incorporation of these three tests into a permutation procedure to adjust for the multiple testing. In addition, we examine a simulation‐based approach proposed by Lin (2005) to adjust for multiple testing. We presented the comparison of these four statistics in terms of type I error, power, family‐wise error rate, and computational efficiency under various scenarios via extensive simulation. Copyright © 2013 John Wiley & Sons, Ltd.     

Healthy Eating Index and Nutrition Biomarkers among Army Soldiers and Civilian Control Group Indicate an Intervention Is Necessary to Raise Omega-3 Index and Vitamin D and Improve Diet Quality

Diet quality and nutrition status are important for optimal health and military performance. Few studies have simultaneously evaluated diet quality and biochemical markers of nutritional status of military service members. The Healthy Eating Index (HEI) can be used to assess dietary quality and adherence to federal nutrition guidelines. The aim of this study was to assess soldiers’ diet quality and nutritional status and compare results to a civilian control group. Methods: A cross-sectional study was conducted with 531 soldiers. A food frequency questionnaire was used to calculate HEI scores. A blood sample was collected for analysis of select nutrition biochemical markers. Non-parametric analyses were conducted to compare the diet quality and nutritional status of soldiers and controls. Differences in non-normally distributed variables were determined by using the Wilcoxon signed-rank test. Results: Soldiers had an HEI score of 59.9 out of 100, marginally higher than the control group (55.4). Biochemical markers of interest were within normal reference values for soldiers, except for the omega-3 index and vitamin D. Conclusions: This study identified dietary components that need improvement and deficits in biochemical markers among soldiers. Improving diet quality and nutritional status should lead to better health, performance, and readiness of the force.