Tests for informative cluster size using a novel balanced bootstrap scheme.

Clustered data are often encountered in biomedical studies, and to date, a number of approaches have been proposed to analyze such data. However, the phenomenon of informative cluster size (ICS) is a challenging problem, and its presence has an impact on the choice of a correct analysis methodology. For example, Dutta and Datta (2015, Biometrics) presented a number of marginal distributions that could be tested. Depending on the nature and degree of informativeness of the cluster size, these marginal distributions may differ, as do the choices of the appropriate test. In particular, they applied their new test to a periodontal data set where the plausibility of the informativeness was mentioned, but no formal test for the same was conducted. We propose bootstrap tests for testing the presence of ICS. A balanced bootstrap method is developed to successfully estimate the null distribution by merging the re-sampled observations with closely matching counterparts. Relying on the assumption of exchangeability within clusters, the proposed procedure performs well in simulations even with a small number of clusters, at different distributions and against different alternative hypotheses, thus making it an omnibus test. We also explain how to extend the ICS test to a regression setting and thereby enhancing its practical utility. The methodologies are illustrated using the periodontal data set mentioned earlier. Copyright © 2017 John Wiley & Sons, Ltd.

Inference on the marginal distribution of clustered data with informative cluster size.

In spite of recent contributions to the literature, informative cluster size settings are not well known and understood. In this paper, we give a formal definition of the problem and describe it from different viewpoints. Data generating mechanisms, parametric and nonparametric models are considered in light of examples. Our emphasis is on nonparametric and robust approaches to the inference on the marginal distribution. Descriptive statistics and parameters of interest are defined as functionals and they are accompanied with a generally applicable testing procedure. The theory is illustrated with an example on patients with incomplete spinal cord injuries.

Dynamic logistic regression model and population attributable fraction to investigate the association between adherence, missed visits and mortality: a study of HIV-infected adults surviving the first year of ART.

BACKGROUND: Adherence is one of the most important determinants of viral suppression and drug resistance in HIV-infected people receiving antiretroviral therapy (ART). METHODS: We examined the association between long-term mortality and poor adherence to ART in DART trial participants in Uganda and Zimbabwe randomly assigned to receive laboratory and clinical monitoring (LCM), or clinically driven monitoring (CDM). Since over 50% of all deaths in the DART trial occurred during the first year on ART, we focussed on participants continuing ART for 12 months to investigate the implications of longer-term adherence to treatment on mortality. Participants' ART adherence was assessed by pill counts and structured questionnaires at 4-weekly clinic visits. We studied the effect of recent adherence history on the risk of death at the individual level (odds ratios from dynamic logistic regression model), and on mortality at the population level (population attributable fraction based on this model). Analyses were conducted separately for both randomization groups, adjusted for relevant confounding factors. Adherence behaviour was also confounded by a partial factorial randomization comparing structured treatment interruptions (STI) with continuous ART (CT). RESULTS: In the CDM arm a significant association was found between poor adherence to ART in the previous 3-9 months with increased mortality risk. In the LCM arm the association was not significant. The odds ratios for mortality in participants with poor adherence against those with optimal adherence was 1.30 (95% CI 0.78,2.10) in the LCM arm and 2.18 (1.47,3.22) in the CDM arm. The estimated proportions of deaths that could have been avoided with optimal adherence (population attributable fraction) in the LCM and CDM groups during the 5 years follow-up period were 16.0% (95% CI 0.7%,31.6%) and 33.1% (20.5%,44.8%), correspondingly. CONCLUSIONS: Recurrent poor adherence determined even through simple measures is associated with high mortality both at individual level as well as at the ART programme level. The number of lives saved through effective interventions to improve adherence could be considerable particularly for individuals monitored without using CD4 cell counts. The findings have important implications for clinical practice and for developing interventions to enhance adherence.

A General Class of Signed Rank Tests for Clustered Data when the Cluster Size is Potentially Informative.

Rank based tests are alternatives to likelihood based tests popularized by their relative robustness and underlying elegant mathematical theory. There has been a serge in research activities in this area in recent years since a number of researchers are working to develop and extend rank based procedures to clustered dependent data which include situations with known correlation structures (e.g., as in mixed effects models) as well as more general form of dependence.The purpose of this paper is to test the symmetry of a marginal distribution under clustered data. However, unlike most other papers in the area, we consider the possibility that the cluster size is a random variable whose distribution is dependent on the distribution of the variable of interest within a cluster. This situation typically arises when the clusters are defined in a natural way (e.g., not controlled by the experimenter or statistician) and in which the size of the cluster may carry information about the distribution of data values within a cluster.Under the scenario of an informative cluster size, attempts to use some form of variance adjusted sign or signed rank tests would fail since they would not maintain the correct size under the distribution of marginal symmetry. To overcome this difficulty Datta and Satten (2008; Biometrics, 64, 501-507) proposed a Wilcoxon type signed rank test based on the principle of within cluster resampling. In this paper we study this problem in more generality by introducing a class of valid tests employing a general score function. Asymptotic null distribution of these tests is obtained. A simulation study shows that a more general choice of the score function can sometimes result in greater power than the Datta and Satten test; furthermore, this development offers the user a wider choice. We illustrate our tests using a real data example on spinal cord injury patients.

NMD and microRNA expression profiling of the HPCX1 locus reveal MAGEC1 as a candidate prostate cancer predisposition gene.

BACKGROUND: Several predisposition loci for hereditary prostate cancer (HPC) have been suggested, including HPCX1 at Xq27-q28, but due to the complex structure of the region, the susceptibility gene has not yet been identified. METHODS: In this study, nonsense-mediated mRNA decay (NMD) inhibition was used for the discovery of truncating mutations. Six prostate cancer (PC) patients and their healthy brothers were selected from a group of HPCX1-linked families. Expression analyses were done using Agilent 44 K oligoarrays, and selected genes were screened for mutations by direct sequencing. In addition, microRNA expression levels in the lymphoblastic cells were analyzed to trace variants that might alter miRNA expression and explain partly an inherited genetic predisposion to PC. RESULTS: Seventeen genes were selected for resequencing based on the NMD array, but no truncating mutations were found. The most interesting variant was MAGEC1 p.Met1?. An association was seen between the variant and unselected PC (OR = 2.35, 95% CI = 1.10-5.02) and HPC (OR = 3.38, 95% CI = 1.10-10.40). miRNA analysis revealed altogether 29 miRNAs with altered expression between the PC cases and controls. miRNA target analysis revealed that 12 of them also had possible target sites in the MAGEC1 gene. These miRNAs were selected for validation process including four miRNAs located in the X chromosome. The expressions of 14 miRNAs were validated in families that contributed to the significant signal differences in Agilent arrays. CONCLUSIONS: Further functional studies are needed to fully understand the possible contribution of these miRNAs and MAGEC1 start codon variant to PC.

Clustering based on adherence data.

Adherence to a medical treatment means the extent to which a patient follows the instructions or recommendations by health professionals. There are direct and indirect ways to measure adherence which have been used for clinical management and research. Typically adherence measures are monitored over a long follow-up or treatment period, and some measurements may be missing due to death or other reasons. A natural question then is how to describe adherence behavior over the whole period in a simple way. In the literature, measurements over a period are usually combined just by using averages like percentages of compliant days or percentages of doses taken. In the paper we adapt an approach where patient adherence measures are seen as a stochastic process. Repeated measures are then analyzed as a Markov chain with finite number of states rather than as independent and identically distributed observations, and the transition probabilities between the states are assumed to fully describe the behavior of a patient. The patients can then be clustered or classified using their estimated transition probabilities. These natural clusters can be used to describe the adherence of the patients, to find predictors for adherence, and to predict the future events. The new approach is illustrated and shown to be useful with a simple analysis of a data set from the DART (Development of AntiRetroviral Therapy in Africa) trial in Uganda and Zimbabwe.

Incidence of hospitalised schizophrenia in Finland since 1980: decreasing and increasing again.

BACKGROUND: It is suggested that the incidence of schizophrenia is decreasing. However, changes in the number of psychiatric beds available and diagnostic practice have not always been taken into account. We studied the annual first-admission rate (per 100,000) for schizophrenia (FARsch) during a rapid deinstitutionalisation period in Finland. METHOD: From the National Finnish Hospital discharge register, we identified all 30,041, 15- to 64-year-old patients admitted for the first time with schizophrenia to hospitals in Finland between 1980 and 2003. RESULTS: FARsch decreased from 56.39 in 1980 to 29.51 in 1991 and slightly increased thereafter. Changes in FARsch corresponded with changes in all admissions. FARsch was higher when using ICD-8, but lower when DSM-IIIR and ICD-10 were used. CONCLUSION: Changes in the number of psychiatric beds available, admission policy and diagnostic practice may explain the majority of variations in FARsch. Possibly, increased use of illegal drugs and improved treatment of depression are reflected in the increase in FARsch.

miR-193b is an epigenetically regulated putative tumor suppressor in prostate cancer.

miRNAs have proven to be key regulators of gene expression and are differentially expressed in various diseases, including cancer. Our aim was to identify epigenetically dysregulated genes in prostate cancer. We performed miRNA expression profiling after relieving epigenetic modifications in 6 prostate cancer cell lines and nonmalignant prostate epithelial cells. Thirty-eight miRNAs showed increased expression in any prostate cancer cell line after 5-aza-2'-deoxycytidine (5azadC) and trichostatin A (TSA) treatments. Six of these also had decreased expression in clinical prostate cancer samples compared to benign prostatic hyperplasia. Among these, miR-193b was methylated in 22Rv1 cell line at a CpG island approximately 1 kb upstream of the miRNA locus. Expressing miR-193b in 22Rv1 cells using pre-miR-193b oligonucleotides caused a significant growth reduction (p < 0.001) resulting from a decrease of cells in S-phase of the cell cycle (p < 0.01). In addition, the anchorage independent growth was partially inhibited in transiently miR-193b-expressing 22Rv1 cells (p < 0.01). Altogether, our data suggest that miR-193b is an epigenetically silenced putative tumor suppressor in prostate cancer.

Estimation of the population attributable fraction for mortality in a cohort study using a piecewise constant hazards model.

Quantification of the impact of exposure to modifiable risk factors on a particular outcome at the population level is a fundamental public health issue. In cohort studies, the population attributable fraction (PAF) is used to assess the proportion of the outcome that is attributable to exposure to certain risk factors in a given population during a certain time interval. This is done by combining information about the prevalence of the risk factor in the population with estimates of the strength of the association between the risk factor and the outcome. In case of mortality, the PAF demonstrates what proportion of mortality can be delayed during the given follow-up time. However, literature on carrying out model-based estimation of PAF and its variance in cohort studies while properly taking follow-up time into account is still scarce. In this article, the authors present formulas for estimation of PAF, its variance, and its confidence interval using the piecewise constant hazards model and apply a SAS macro created for the estimation of PAF (SAS Institute Inc., Cary, North Carolina) to estimate the mortality attributable to some common risk factors.

On linear dimension reduction based on diagonalization of scatter matrices for bioinformatics downstream analyses.

Dimension reduction is often a preliminary step in the analysis of data sets with a large number of variables. Most classical, both supervised and unsupervised, dimension reduction methods such as principal component analysis (PCA), independent component analysis (ICA) or sliced inverse regression (SIR) can be formulated using one, two or several different scatter matrix functionals. Scatter matrices can be seen as different measures of multivariate dispersion and might highlight different features of the data and when compared might reveal interesting structures. Such analysis then searches for a projection onto an interesting (signal) part of the data, and it is also important to know the correct dimension of the signal subspace. These approaches usually make either no model assumptions or work in wide classes of semiparametric models. Theoretical results in the literature are however limited to the case where the sample size exceeds the number of variables which is hardly ever true for data sets encountered in bioinformatics. In this paper, we briefly review the relevant literature and explore if the dimension reduction tools can be used to find relevant and interesting subspaces for small-n-large-p data sets. We illustrate the methods with a microarray dataset of prostate cancer patients and healthy controls.

Classification based on hybridization of parametric and nonparametric classifiers.

Parametric methods of classification assume specific parametric models for competing population densities (e.g., Gaussian population densities can lead to linear and quadratic discriminant analysis) and they work well when these model assumptions are valid. Violation in one or more of these parametric model assumptions often leads to a poor classifier. On the other hand, nonparametric classifiers (e.g., nearest-neighbor and kernel-based classifiers) are more flexible and free from parametric model assumptions. But, the statistical instability of these classifiers may lead to poor performance when we have small numbers of training sample observations. Nonparametric methods, however, do not use any parametric structure of population densities. Therefore, even when one has some additional information about population densities, that important information is not used to modify the nonparametric classification rule. This paper makes an attempt to overcome these limitations of parametric and nonparametric approaches and combines their strengths to develop some hybrid classification methods. We use some simulated examples and benchmark data sets to examine the performance of these hybrid discriminant analysis tools. Asymptotic results on their misclassification rates have been derived under appropriate regularity conditions.

Characterisation of human dental stem cells and buccal mucosa fibroblasts.

Human craniofacial stem cells are recently discovered sources of putative mesenchymal stem cells that hold great promise for autogenic or allogenic cell therapy and tissue engineering. Prior to employing these cells in clinical applications, they must be thoroughly investigated and characterized. In this study, the surface marker expression was investigated on dental pulp stem cells (DPSCs), dental follicle cells (DFCs), periodontal ligament stem cells (PDLSCs), and buccal mucosa fibroblasts (BMFs) utilising surface markers for flow cytometry. The osteogenic potential was also examined by bone-associated markers alkaline phosphatase, Runx2, collagen type I, osteocalcin, and osteopontin. The results from our study demonstrate that the dental cell sources exhibit comparable surface marker and bone-associated marker profiles parallel to those of other mesenchymal stem cell sources, yet distinct from the buccal mucosa fibroblasts. Our data support evidence towards clinical applicability of dental stem cells in hard tissue regeneration.

Topographic differences in mean computational sleep depth between healthy controls and obstructive sleep apnoea patients.

In this work, topographic differences in computational sleep depth between healthy controls and obstructive sleep apnoea syndrome (OSAS) patients have been examined. Sleep depth estimation was based on continuous monitoring of the mean frequency of the EEG. During the experiments, all-night sleep EEG recordings of carefully age and gender matched sets of 16 healthy controls and 16 OSAS patients were compared on six electrode locations (Fp1-M2, Fp2-M1, C3-M2, C4-M1, O1-M2, and O2-M1). To optimise the diagnostic ability of the method, we examined the influence of 45 sets of adjustable analysis parameters on the ability of the method to show differences in computational sleep depth between the diagnostic groups. The results show clearly that although the visual scores for a set of epochs are the same for both clinical groups, computational sleep depth measure still shows deeper local sleep for healthy controls, both during NREM and REM sleep. Although the best achievable performance in different sleep stages is reached in different EEG derivations and with different parameter values, computation of sleep depth with 1-s output resolution in non-overlapping segments of 2s (400 samples) with maximum analysis band frequency of 20.5 Hz and 51-point moving median smoothing on Fp2-M1 or O1-M2 leads to near-optimal performance in deep sleep or wakefulness/light sleep, respectively.

MiRNA Profiles in Lymphoblastoid Cell Lines of Finnish Prostate Cancer Families.

BACKGROUND: Heritable factors are evidently involved in prostate cancer (PrCa) carcinogenesis, but currently, genetic markers are not routinely used in screening or diagnostics of the disease. More precise information is needed for making treatment decisions to distinguish aggressive cases from indolent disease, for which heritable factors could be a useful tool. The genetic makeup of PrCa has only recently begun to be unravelled through large-scale genome-wide association studies (GWAS). The thus far identified Single Nucleotide Polymorphisms (SNPs) explain, however, only a fraction of familial clustering. Moreover, the known risk SNPs are not associated with the clinical outcome of the disease, such as aggressive or metastasised disease, and therefore cannot be used to predict the prognosis. Annotating the SNPs with deep clinical data together with miRNA expression profiles can improve the understanding of the underlying mechanisms of different phenotypes of prostate cancer. RESULTS: In this study microRNA (miRNA) profiles were studied as potential biomarkers to predict the disease outcome. The study subjects were from Finnish high risk prostate cancer families. To identify potential biomarkers we combined a novel non-parametrical test with an importance measure provided from a Random Forest classifier. This combination delivered a set of nine miRNAs that was able to separate cases from controls. The detected miRNA expression profiles could predict the development of the disease years before the actual PrCa diagnosis or detect the existence of other cancers in the studied individuals. Furthermore, using an expression Quantitative Trait Loci (eQTL) analysis, regulatory SNPs for miRNA miR-483-3p that were also directly associated with PrCa were found. CONCLUSION: Based on our findings, we suggest that blood-based miRNA expression profiling can be used in the diagnosis and maybe even prognosis of the disease. In the future, miRNA profiling could possibly be used in targeted screening, together with Prostate Specific Antigene (PSA) testing, to identify men with an elevated PrCa risk.

Five-year mortality of Finnish schizophrenia patients in the era of deinstitutionalization.

BACKGROUND: Excess mortality among people with schizophrenia due to natural and unnatural causes, especially due to suicides, is a well-known fact. It has been suggested that deinstitutionalization increases suicide mortality but there are also contradictory results. We studied the changes in mortality and causes of death among schizophrenia sufferers during and after the years of deinstitutionalization process in Finland. METHOD: The sample, identified from the Finnish hospital discharge register (FHDR), consisted of patients aged 15-65 and hospitalized for the first time due to schizophrenia. We focused on the 5-year follow-up from inclusion years 1980-1998 (N=23,959). Changes in 5-year follow-up mortality during the study period were explored for both genders and for different causes of death separately using multivariate logistic regression analyses. RESULTS: During the study period 1,926 deaths occurred. Suicide was the major cause of death in both genders. A significant reduction in overall 5-year mortality was observed among persons hospitalized in 1995-1998 when compared to people hospitalized 1980-1984. In males a significant reduction was seen in all mortality (P=0.025) due to suicides (P=0.007) but not in the case of natural deaths. In females no significant changes in mortality were found. CONCLUSIONS: Our study confirms a reduction in suicide mortality of male schizophrenia sufferers after the deinstitutionalization process. However the overall mortality is still very high and the attention needs to be focused on the general well-being of schizophrenia patients.

Ageing and cardiovascular responses to head-up tilt in healthy subjects.

OBJECTIVE: Ageing is associated with increased postural blood pressure changes and attenuated cardiovascular reactivity. As the background of these changes remains uncertain, we examined arterial stiffness, cardiac function and vascular resistance in healthy subjects in supine position and during orthostatic challenge. METHODS: Haemodynamics of 179 normotensive subjects (109 female and 70 male, 21-59 years) were examined using continuous radial pulse wave analysis, whole-body impedance cardiography, and plethysmographic finger blood pressures. RESULTS: Both in supine position and during head-up tilt central and peripheral blood pressure and augmentation index (amplitude of the reflected pressure wave divided by pulse pressure) increased, and time of pulse wave reflection decreased with age. Supine pulse wave velocity progressively increased with age. There were only minor differences in supine and upright systemic vascular resistance, cardiac index, stroke index, and heart rate that were not systematically related to age. In regression analysis, the explanatory factors for a more pronounced decrease in central systolic blood pressure during the head-up tilt were in the age of 50-59 years, higher baseline pulse wave velocity and central systolic BP. None of the changes in other haemodynamic variables during tilt were related to age. CONCLUSION: As no systematic age-related differences were observed in cardiac function or vascular resistance, these results support the view that progressive reduction of large arterial compliance contributes to the exaggerated age-related decrease in central systolic blood pressure in response to head-up tilt.