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Results for “statistical inference”

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PubMed2026

Schizophrenia and bipolar disorder: a comparative analysis of genetic and brain network connectivity.

BACKGROUND: Schizophrenia (SCZ) and bipolar disorder (BD) are severe psychiatric conditions with overlapping clinical presentations, genetic risk factors, and brain network dysfunction. Whether alterations in large-scale intrinsic brain networks reflect shared or disorder-specific genetic influences remains poorly understood. Clarifying this distinction is essential for refining etiological models and improving diagnostic precision. METHODS: Genome-wide inferred statistics (GWIS) were applied to decompose the genetic architecture of SCZ and BD into shared and unique components. Using resting-state network (RSN) data from the UK Biobank, functional connectivity (FC) and structural connectivity (SC) were extracted as neuroimaging phenotypes. Causal inference approaches were subsequently employed to infer potential directional relationships between brain network connectivity and each disorder. RESULTS: Analyses revealed both common and distinct patterns of brain network connectivity associated with SCZ and BD. Notably, SC within the default mode network (DMN) exhibited opposing effects across the two disorders, suggesting divergent structural underpinnings despite clinical overlap. Additionally, SC within the limbic network (LN) and frontotemporal control network demonstrated potential causal relationships with both conditions, implicating these circuits astransdiagnostic neural substrates. CONCLUSION: These findings illuminate the shared and disorder-specific genetic and neural architecture underlying SCZ and BD. Integrating genome-wide genetic methods with large-scale neuroimaging data offers a powerful framework for disentangling psychiatric comorbidity and may inform more targeted diagnostic criteria and individualized treatment strategies.

Humans↗
PubMedDate not supplied

Thyroid dysfunction after radiation therapy in head and neck cancer patients.

INTRODUCTION: The reported incidence of hypothyroidism following surgery and/or radiation therapy for head and neck cancer varies widely. Most patients undergo thyroid lobectomy during laryngectomy. Standard radiation treatment portals often include the thyroid gland. The insidious development of hypothyroidism may be misdiagnosed. This study examines the incidence of thyroid dysfunction in the setting of head and neck cancer therapy. MATERIALS AND METHODS: Thyroid function tests were performed on 100 consecutive patients treated in the head and neck tumor clinic. Statistical inferences on proportions were made using chi-square analysis. RESULTS: Therapy included surgery only (10 patients), radiation therapy only (28 patients), and combined therapy (62 patients). These patients experienced thyroid dysfunction in 0%, 29%, and 45% of individuals respectively. These differences were statistically significant (P < .05). The highest rate of dysfunction (69%) was associated with patients undergoing laryngectomy and radiation therapy. When laryngectomy was not performed, thyroid dysfunction occurred in 28%. CONCLUSION: The likelihood of thyroid dysfunction after radiation therapy is high particularly when combined with surgery in which thyroid lobectomy is performed and the contralateral lobe is potentially devascularized. These results suggest that radiation therapy is a primary factor in alteration of thyroid function. We recommend that routine thyroid function testing be part of follow-up of all head and neck cancer patients.

Chi-Square Distribution↗
PubMed1996

Statistical methods in the Fourier domain to enhance and classify images.

A mathematical model, for which rigorous methods of statistical inference are available, is described and techniques for image enhancement and linear discriminant analysis of groups are developed. Since the gray values of neighboring pixels in tomographically produced medical images are spatially correlated, the calculations are carried out in the Fourier domain to insure statistical independence of the variables. Furthermore, to increase the power of statistical tests the known spatial covariance was used to specify constraints in the spectral domain. These methods were compared to statistical procedures carried out in the spatial domain. Positron emission tomography (PET) images of alcoholics with organic brain disorders were compared by these techniques to age-matched normal volunteers. Although these techniques are employed to analyze group characteristics of functional images, they provide a comprehensive set of mathematical and statistical procedures in the spectral domain that can also be applied to images of other modalities, such as computed tomography (CT) or magnetic resonance imaging (MRI).

Aged↗
PubMed2003

A primer for statistical analysis of clinical trials.

Randomized controlled trials have become the cornerstone of current practice evidence-based medicine. In this article, we present concise descriptions of fundamental statistical concepts and methods frequently used in the analysis of randomized trials. These include descriptive statistics, statistical inferences, techniques for the comparison of means or proportions from two samples, correlation, and regression analysis methods. The uses of these methods are illustrated with a number of practical examples, and the pitfalls of these topics are also briefly discussed. Lastly, some frequently used statistical terms and their meanings are also provided. By the end of the article, the reader should have sufficient knowledge to appreciate the statistical aspects of most clinical trial reports.

Evidence-Based Medicine↗
PubMed1991

The validity of inferences based on incomplete observations in disease state models.

In many survival time studies or studies on the progression of a disease, information is often incomplete in the sense that it is known only that a patient has been in certain disease states at several time points. In this paper, conditions concerning the interrelationship between the disease process and the examination scheme (i.e., the pattern of examination times) are derived under which a valid statistical inference is possible. These conditions are confronted with examination schemes that are of practical importance in clinical research. A cancer marker study is used as an example to estimate the magnitude of the potential bias when the conditions derived are violated.

Biometry↗
PubMed1979

The estimation of mutation rates when premeiotic events are involved.

When mutation or recombination events occur premeiotically, the distribution of exceptional individuals among the offspring will be "clustered" as opposed to binomial. Even though the exact nature of the clustering is usually unknown, unbiased methods for measuring mutation rate and determining the precision of these measurements are given to replace a biased method now frequently used. When clustering is pronounced, the unweighted average mutation rate is found to be a more efficient estimator than the usual average weighted by family size. Methods of statistical inference and optimal experimental design in the absence of specific knowledge of the mechanism of clustering are also discussed.

Animals↗
PubMed2005

Spreading interviews over time in health surveys: do temporal variations of self-reported alcohol consumption affect measurement?

OBJECTIVE: To address systematic variations related to the day of the interview in self-reports of alcohol consumption in telephone health surveys. The investigations include temporal clustering effects, prediction of alcohol consumption and variations across days by characteristics of respondents and interviewing period, and sensitivity to variations of measurements instruments. METHOD: Data at baseline collected in Spring 1999 from 2846 participants in a longitudinal probabilistic general-population survey in Switzerland were used. The study is representative for drinkers in Switzerland. Alcohol consumption measures include a 6-month quantity frequency and a 1-week graduated frequency measure. RESULTS: Evidence for systematic variations in self-reports related to the day of interview was found on the graduated-frequency measure even after controlling for sample characteristics. Similar variations on the quantity frequency measure were found, but were no longer significant after statistical control of the sample characteristics. CONCLUSIONS: Statistical inference in alcohol survey research by telephone interviews based on graduated-frequency measures with short reference period may be plagued with errors related to clustering effects of the day of the interview. Temporal aspects of conducting the fieldwork should therefore be accounted for in statistical analysis.

Adolescent↗
PubMed2003

Statistical significance analysis of longitudinal gene expression data.

MOTIVATION: Time-course microarray experiments are designed to study biological processes in a temporal fashion. Longitudinal gene expression data arise when biological samples taken from the same subject at different time points are used to measure the gene expression levels. It has been observed that the gene expression patterns of samples of a given tumor measured at different time points are likely to be much more similar to each other than are the expression patterns of tumor samples of the same type taken from different subjects. In statistics, this phenomenon is called the within-subject correlation of repeated measurements on the same subject, and the resulting data are called longitudinal data. It is well known in other applications that valid statistical analyses have to appropriately take account of the possible within-subject correlation in longitudinal data. RESULTS: We apply estimating equation techniques to construct a robust statistic, which is a variant of the robust Wald statistic and accounts for the potential within-subject correlation of longitudinal gene expression data, to detect genes with temporal changes in expression. We associate significance levels to the proposed statistic by either incorporating the idea of the significance analysis of microarrays method or using the mixture model method to identify significant genes. The utility of the statistic is demonstrated by applying it to an important study of osteoblast lineage-specific differentiation. Using simulated data, we also show pitfalls in drawing statistical inference when the within-subject correlation in longitudinal gene expression data is ignored.

Adaptation, Physiological↗
PubMed2004

Healthy passages. A multilevel, multimethod longitudinal study of adolescent health.

PURPOSE: To provide an overview of a multisite, long-term study that focuses on risk and protective factors, health behaviors (e.g., dietary practices, physical inactivity, tobacco use, and violent activity), and health outcomes (e.g., diabetes, obesity, and sexually transmitted diseases) for a fifth-grade cohort to be followed biennially from ages 10 to 20 years. METHODS: A two-stage probability sampling procedure was used to select 5250 fifth-grade students from schools in Birmingham AL, Houston TX, and Los Angeles CA to ensure a sufficient sample size of African Americans, Hispanics, and non-Hispanic whites, to support precise statistical inferences. Computer-assisted technology was used to collect data from children and their primary caregivers. Teachers and other school personnel responded to questionnaires, and observational procedures were used to obtain information about schools and neighborhoods. RESULTS: To exploit the multilevel, multimethod structure of the data, statistical models include latent-growth mixture modeling, multilevel modeling, time-series analysis, survival analysis, latent transition analysis, and structural equation modeling. Analyses focus both on the co-occurrence and predictors of growth trajectories for different health behaviors across time. CONCLUSIONS: By using a prospective research design and studying the predictors and time course of multiple health behaviors with a multilevel, multimethod assessment protocol, this research project could provide an empirical basis for effective social and educational policies and intervention programs that foster positive health and well-being during both adolescence and adulthood.

Adolescent↗
PubMed2003

Statistical method for analysis of the disease curve in animals with experimental autoimmune encephalomyelitis.

Experimental autoimmune encephalomyelitis (EAE) is a procedure used in the laboratory to examine drugs that may have utility in treating multiple sclerosis (MS). The problem of modeling the disease curve in animals with EAE is studied. The classification of animals after each experiment is considered and the chi-square test is proposed to test a homogeneity between treatment groups. A mixture type of nonlinear mixed-effects model with repeated measurements is considered, assuming that the onset and/or remission of disease is the fixed effect as well as the random effect. Statistical inference on the parameters of the disease curves is discussed. The proposed model is shown to be efficient for comparing the disease curves with different treatments. Examples concerning the study of the effects of test compounds in EAE are presented to illustrate the proposed model and statistical methodologies.

Animals↗
PubMed2005

Phylogenetic approaches in comparative physiology.

Over the past two decades, comparative biological analyses have undergone profound changes with the incorporation of rigorous evolutionary perspectives and phylogenetic information. This change followed in large part from the realization that traditional methods of statistical analysis tacitly assumed independence of all observations, when in fact biological groups such as species are differentially related to each other according to their evolutionary history. New phylogenetically based analytical methods were then rapidly developed, incorporated into ;the comparative method', and applied to many physiological, biochemical, morphological and behavioral investigations. We now review the rationale for including phylogenetic information in comparative studies and briefly discuss three methods for doing this (independent contrasts, generalized least-squares models, and Monte Carlo computer simulations). We discuss when and how to use phylogenetic information in comparative studies and provide several examples in which it has been helpful, or even crucial, to a comparative analysis. We also consider some difficulties with phylogenetically based statistical methods, and of comparative approaches in general, both practical and theoretical. It is our personal opinion that the incorporation of phylogeny information into comparative studies has been highly beneficial, not only because it can improve the reliability of statistical inferences, but also because it continually emphasizes the potential importance of past evolutionary history in determining current form and function.

Computer Simulation↗
PubMed2006

A cluster mass permutation test with contextual enhancement for fMRI activation detection.

Gaussian random field (GRF)-based methods are commonly used for statistical inference and to control the family-wise error rate (FWE) in neuroimaging. They require that the error fields are reasonable lattice approximations to an underlying continuous multivariate Gaussian random field and have differentiable and invertible spatial autocorrelation function. Permutation test estimates the distribution of the test statistic from the data and adjusts automatically for the FWE. Here we present a new analysis procedure, the cluster mass permutation test with contextual enhancement (CMPCE), and compare it to GRF. In CMPCE, the data are first pre-whitened to remove temporal autocorrelations. The FWE rates, the cluster detection probability and delineation accuracy of CMPCE and GRF were compared using measured null data and null data containing simulated activations. We also applied both methods to an fMRI experiment where tactile somatosensory stimulation into the right hand was used. When analyzing the FWE using null data, both CMPCE and GRF gave significantly higher FWEs (CMPCE up to 0.12, GRF up to 0.18) than the nominal significance level 0.05, indicating that the pre-whitening, motion correction or high-pass filtering partially failed. In the simulated activation data, CMPCE gave less falsely classified voxels for the same cluster detection probability level than GRF. The maximal cluster detection probability was on the other hand higher in the GRF-based method. Both methods gave qualitatively similar results in the tactile fMRI data. CMPCE seems to be a promising fMRI analysis method, especially if high delineation accuracy is required.

Adult↗
PubMed1987

The analysis of multiple endpoints in clinical trials.

Treatment comparisons in randomized clinical trials usually involve several endpoints such that conventional significance testing can seriously inflate the overall Type I error rate. One option is to select a single primary endpoint for formal statistical inference, but this is not always feasible. Another approach is to apply Bonferroni correction (i.e., multiply each P-value by the total number of endpoints). Its conservatism for correlated endpoints is examined for multivariate normal data. A third approach is to derive an appropriate global test statistic and this paper explores one such test applicable to any set of asymptotically normal test statistics. Quantitative, binary, and survival endpoints are all considered within this general framework. Two examples are presented and the relative merits of the proposed strategies are discussed.

Asthma↗
PubMed2001

Analysis of recurrent failure times data: should the baseline hazard be stratified?

Over the past two decades, a variety of fruitful statistical methods for the analysis of recurrent events has been proposed for the estimation of covariates effect using the Cox proportional hazard model. Besides frailty modelling, two simple trends of modelling have been developed: the first one uses stratification on the rank of the event, whereas the second one, more closely related to Poisson processes theory, does not use stratification. Although they both take into account the correlation of the unit failure times, each of these approaches emphasizes a different aspect of the underlying point process and there is still an ongoing debate concerning the most appropriate method. The aim of this paper is to stress current interests and trends concerning these two approaches. For each model, main statistical methods for estimating the covariates effects are presented. Methods are illustrated and compared in two randomized clinical trials which involve recurrences of severe adverse events following chemotherapy in 938 patients with chronic lymphocytic leukaemia, and recurrences of infectious rhinitis episodes in 327 patients. The discussion, based on the previous examples and on the properties of underlying statistical inference, deals with the appropriateness of the model choice, which is closely related to the data structure.

Adjuvants, Immunologic↗
PubMed2005

Are we really that blind?

In double-blind randomized clinical trials, it is common practice to randomize patients using randomly permuted blocks. In this article, it is demonstrated that before unblinding statistical inference of the treatment effects can be conducted, yielding consistent and rather precise estimates even in the presence of an additive block effect. With an even greater precision, the within-group standard deviation on which power calculation are usually based can be inferred from blinded data. The use of blocks of random lengths as suggested by ICH-E9 in the (unlikely) case that previous treatment allocation can be guessed by strong pharmacological effects, merely complicates the analysis but blinded inference can still be conducted without much extra loss of information. On the one hand, one might argue that this possibility of blinded inference takes away the need of conducting interim analyses for administrative or business reasons or for sample size reestimation. On the other hand, however, it most probably will have a disputable, positive or negative effect on the conduct of the remainder of the trial. If regulators and the pharmaceutical world at large want to avoid this possibility, then other unrestricted, biased coin, or more general dynamic allocation randomization procedures may be less controversial alternatives. It at least provides another strong argument in favor of using large blocks as the precision of blinded inference decreases with increasing block lengths. If blinded inferences are deemed a useful replacement of interim analyses in nonpivotal trials, then further guidelines will be needed on consequent decision-making aspects.

Algorithms↗
PubMed1984

Effects of the use of unreliable surrogate variables on the validity of epidemiologic research studies.

When certain key factors of interest in epidemiologic research studies cannot be measured directly, epidemiologists often turn to the use of surrogate variables. The potential bias in making statistical inferences about an adjusted exposure-disease association parameter (e.g., a partial correlation) is described as a function of the degree of unreliability in the surrogate variables used in place of the underlying disease, exposure, and confounding factors of real interest. It is shown that unreliability in the surrogate confounder is much more apt to produce seriously misleading inferences than is unreliability in the surrogate measures for disease and exposure. Practical methods are discussed for dealing with less than perfectly reliable surrogate variables.

Epidemiologic Methods↗
PubMed2004

Bayesian extrapolation of space-time trends in cancer registry data.

We apply a full Bayesian model framework to a dataset on stomach cancer mortality in West Germany. The data are stratified by age group, year, and district. Using an age-period-cohort model with an additional spatial component, our goal is to investigate whether there is evidence for space-time interactions in these data. Furthermore, we will determine whether a period-space or a cohort-space interaction model is more appropriate to predict future mortality rates. The setup will be fully Bayesian based on a series of Gaussian Markov random field priors for each of the components. Statistical inference is based on efficient algorithms to block update Gaussian Markov random fields, which have recently been proposed in the literature.

Bayes Theorem↗
PubMed1998

A critical review of papers from clinical cancer research.

A review of 75 original articles from clinical cancer research in Norway is presented. Articles published in 1993 and with at least one Norwegian author were included in the review. Sixty papers were observational, whereas 15 were experimental. Of the observational studies 44 were retrospective. Most of the papers concerned prognostic factors. Prior hypotheses were explicitly defined in 16 papers only, and less than half of the articles described inclusion and exclusion criteria. Sample size calculations were performed in four papers only. The choice of statistical method was considered to be suitable in 22 of the 58 articles presenting statistical inferences. Problems related to multiple significance testing were rarely addressed, although the median number of p-values reported was as high as 8. Confidence intervals for main findings were presented in 14 papers. For proper planning of studies, as well as for analysis and interpretation of study results, statistical advice is indeed required.

Clinical Trials as Topic↗