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Statistical Methods in Medical Research

Piero Quatto, Nicolò Margaritella, Isa Costantini, Francesca Baglio, Massimo Garegnani, Raffaello Nemni, Luigi Pugnetti
Brain functional connectivity is a widely investigated topic in neuroscience. In recent years, the study of brain connectivity has been largely aided by graph theory. The link between time series recorded at multiple locations in the brain and the construction of a graph is usually an adjacency matrix. The latter converts a measure of the connectivity between two time series, typically a correlation coefficient, into a binary choice on whether the two brain locations are functionally connected or not. As a result, the choice of a threshold τ over the correlation coefficient is key...
May 14, 2019: Statistical Methods in Medical Research
Ralf-Dieter Hilgers, Martin Manolov, Nicole Heussen, William F Rosenberger
BACKGROUND: Among various design aspects, the choice of randomization procedure have to be agreed on, when planning a clinical trial stratified by center. The aim of the paper is to present a methodological approach to evaluate whether a randomization procedure mitigates the impact of bias on the test decision in clinical trial stratified by center. METHODS: We use the weighted t test to analyze the data from a clinical trial stratified by center with a two-arm parallel group design, an intended 1:1 allocation ratio, aiming to prove a superiority hypothesis with a continuous normal endpoint without interim analysis and no adaptation in the randomization process...
May 10, 2019: Statistical Methods in Medical Research
Matteo Di Maso, Francesca Bravi, Jerry Polesel, Eva Negri, Adriano Decarli, Diego Serraino, Carlo La Vecchia, Monica Ferraroni
The attributable fraction is the candidate tool to quantify individual shares of each risk factor on the disease burden in a population, expressing the proportion of cases ascribable to the risk factors. The original formula ignored the presence of other factors (i.e. multiple risk factors and/or confounders), and several adjusting methods for potential confounders have been proposed. However, crude and adjusted attributable fractions do not sum up to their joint attributable fraction (i.e. the number of cases attributable to all risk factors together) and their sum may exceed one...
May 10, 2019: Statistical Methods in Medical Research
Miguel de Carvalho, Bradley J Barney, Garritt L Page
We propose new summary measures of biomarker accuracy which can be used as companions to existing diagnostic accuracy measures. Conceptually, our summary measures are tantamount to the so-called Hellinger affinity and we show that they can be regarded as measures of agreement constructed from similar geometrical principles as Pearson correlation. We develop a covariate-specific version of our summary index, which practitioners can use to assess the discrimination performance of a biomarker, conditionally on the value of a predictor...
May 10, 2019: Statistical Methods in Medical Research
Atanu Biswas, Rahul Bhattacharya, Soumyadeep Das
A multi-treatment response adaptive procedure is developed considering "comparison with the best" philosophy of multiple comparison procedures for clinical trials with ordinal categorical responses, when there is no shared control. For each response category, we arbitrarily create two outcome groups; one by combining the categories more favorable to it and the other by merging the categories at most as favorable to it and hence define the odds (i.e. cumulative odds). Pairwise ratios of such odds (i...
May 10, 2019: Statistical Methods in Medical Research
Oyelola A Adegboye, Tomoki Fujii, Denis Hy Leung
Non-response is a commonly encountered problem in many population-based surveys. Broadly speaking, non-response can be due to refusal or failure to contact the sample units. Although both types of non-response may lead to bias, there is much evidence to indicate that it is much easier to reduce the proportion of non-contacts than to do the same with refusals. In this article, we use data collected from a nationally representative survey under the Demographic and Health Surveys program to study non-response due to refusals to HIV testing in Malawi...
May 9, 2019: Statistical Methods in Medical Research
Mirjam Moerbeek
With group randomized trials complete groups of subject are randomized to treatment conditions. Such grouping also occurs in individually randomized trials where treatment is administered in groups. Outcomes may be measured at the level of the subject, but also at the level of the group. The optimal design determines the number of groups and the number of subjects per group in the intervention and control conditions. It is found by taking a budgetary constraint into account, where costs are associated with implementing the intervention and control, and with taking measurements on subject and groups...
May 1, 2019: Statistical Methods in Medical Research
Patrick Taffé
Recently, a new estimation procedure has been developed to assess bias and precision of a new measurement method, relative to a reference standard. However, the author did not develop confidence bands around the bias and standard deviation curves. Therefore, the goal in this paper is to extend this methodology in several important directions. First, by developing simultaneous confidence bands for the various parameters estimated to allow formal comparisons between different measurement methods. Second, by proposing a new index of agreement...
April 24, 2019: Statistical Methods in Medical Research
Giovanna Cilluffo, Gianluca Sottile, Stefania La Grutta, Vito Mr Muggeo
This paper focuses on hypothesis testing in lasso regression, when one is interested in judging statistical significance for the regression coefficients in the regression equation involving a lot of covariates. To get reliable p-values, we propose a new lasso-type estimator relying on the idea of induced smoothing which allows to obtain appropriate covariance matrix and Wald statistic relatively easily. Some simulation experiments reveal that our approach exhibits good performance when contrasted with the recent inferential tools in the lasso framework...
April 16, 2019: Statistical Methods in Medical Research
Feng-Shou Ko
In this paper, we consider joint modeling of repeated measurements and competing risks failure time data to allow for more than one distinct failure type in the survival endpoint. Hence, we can fit a cause-specific hazards submodel to allow for competing risks, with a separate latent association between longitudinal measurements and each cause of failure. We also consider the possible masked causes of failure in joint modeling of repeated measurements and competing risks failure time data. We also derive a score test to identify longitudinal biomarkers or surrogates for a time-to-event outcome in competing risks data which contain masked causes of failure...
April 16, 2019: Statistical Methods in Medical Research
Camille Sabathé, Per K Andersen, Catherine Helmer, Thomas A Gerds, Hélène Jacqmin-Gadda, Pierre Joly
Pseudo-values provide a method to perform regression analysis for complex quantities with right-censored data. A further complication, interval-censored data, appears when events such as dementia are studied in an epidemiological cohort. We propose an extension of the pseudo-value approach for interval-censored data based on a semi-parametric estimator computed using penalised likelihood and splines. This estimator takes interval-censoring and competing risks into account in an illness-death model. We apply the pseudo-value approach to three mean value parameters of interest in studies of dementia: the probability of staying alive and non-demented, the restricted mean survival time without dementia and the absolute risk of dementia...
April 16, 2019: Statistical Methods in Medical Research
Chan Wang, Shufang Deng, Leiming Sun, Liming Li, Yue-Qing Hu
The genome-wide association studies aim at identifying common or rare variants associated with common diseases and explaining more heritability. It is well known that common diseases are influenced by multiple single nucleotide polymorphisms (SNPs) that are usually correlated in location or function. In order to powerfully detect association signals, it is highly desirable to take account of correlations or linkage disequilibrium (LD) information among multiple SNPs in testing for association. In this article, we propose a test SLIDE that depicts the difference of the average multi-locus genotypes between cases and controls and derive its variance-covariance matrix in the retrospective design...
April 16, 2019: Statistical Methods in Medical Research
Peter C Austin, C Fangyun Wu, Douglas S Lee, Jack V Tu
Administrative healthcare databases are increasingly being used for research purposes. When used to estimate the effects of treatments and interventions, an important limitation of these databases is the lack of information on important confounding variables. The high-dimensional propensity score (hdPS) is an algorithm that generates a large number of empirically-derived covariates using administrative healthcare databases. The hdPS has been described as enabling adjustment by proxy, in which a large number of empirically-derived covariates may serve as proxies for unmeasured confounding variables...
April 11, 2019: Statistical Methods in Medical Research
Sandrine Boulet, Moreno Ursino, Peter Thall, Bruno Landi, Céline Lepère, Simon Pernot, Anita Burgun, Julien Taieb, Aziz Zaanan, Sarah Zohar, Anne-Sophie Jannot
BACKGROUND: Building tools to support personalized medicine needs to model medical decision-making. For this purpose, both expert and real world data provide a rich source of information. Currently, machine learning techniques are developing to select relevant variables for decision-making. Rather than using data-driven analysis alone, eliciting prior information from physicians related to their medical decision-making processes can be useful in variable selection. Our framework is electronic health records data on repeated dose adjustment of Irinotecan for the treatment of metastatic colorectal cancer...
April 9, 2019: Statistical Methods in Medical Research
Qiqi Deng, Ying-Ying Zhang, Dooti Roy, Ming-Hui Chen
Traditionally, statistical methods for futility analysis are developed based on a single study. To establish a drug's effectiveness, usually at least two adequate and well-controlled studies need to demonstrate convincing evidence on its own. Therefore, in a standard clinical development program in chronic diseases, two independent studies are generally conducted for drug registration. This paper proposes a statistical method to combine interim data from two independent and similar studies for interim futility analysis and shows that the conditional power approach based on combined interim data has better operating characteristics compared to the approach based on single-trial interim data, even with small to moderate heterogeneity on the treatment effects between the two studies...
April 8, 2019: Statistical Methods in Medical Research
Hua Zhang, Guogen Shan
No abstract text is available yet for this article.
April 4, 2019: Statistical Methods in Medical Research
Lili Zhao, Carl Koschmann
Testing anti-cancer agents with multiple disease subtypes is challenging and it becomes more complicated when the subgroups have different types of endpoints (such as binary endpoints of tumor response and progression-free survival endpoints). When this occurs, one common approach in oncology is to conduct a series of small screening trials in specific patient subgroups, and these trials are typically run in parallel, independent of each other. However, this approach does not consider the possibility that some of the patient subpopulations respond similarly to therapy...
April 4, 2019: Statistical Methods in Medical Research
Hang Zhang, Wenjian Bi, Yuehua Cui, Honglei Chen, Jinbo Chen, Yanlong Zhao, Guolian Kang
In epidemiology cohort studies, exposure data are collected in sub-studies based on a primary outcome (PO) of interest, as with the extreme-value sampling design (EVSD), to investigate their correlation. Secondary outcomes (SOs) data are also readily available, enabling researchers to assess the correlations between the exposure and the SOs. However, when the EVSD is used, the data for SOs are not representative samples of a general population; thus, many commonly used statistical methods, such as the generalized linear model (GLM), are not valid...
April 4, 2019: Statistical Methods in Medical Research
Maria-Athina Altzerinakou, Xavier Paoletti
We present a new adaptive dose-finding method, based on a joint modeling of longitudinal continuous biomarker activity measurements and time to first dose limiting toxicity, with a shared random effect. Estimation relies on likelihood that does not require approximation, an important property in the context of small sample sizes, typical of phase I/II trials. We address the important case of missing at random data that stem from unacceptable toxicity, lack of activity and rapid deterioration of phase I patients...
April 4, 2019: Statistical Methods in Medical Research
Colin O Wu, Xin Tian, Lu Tian, Jared P Reis, Lihui Zhao, Norrina B Allen, Sejong Bae, Kiang Liu
Tracking a subject's risk factors or health status over time is an important objective in long-term epidemiological studies with repeated measurements. An important issue of time-trend tracking is to define appropriate statistical indices to quantitatively measure the tracking abilities of the targeted risk factors or health status over time. We present a number of local and global statistical tracking indices based on the rank-tracking probabilities, which are derived from the conditional distribution functions, and propose a class of kernel-based nonparametric estimation methods...
April 4, 2019: Statistical Methods in Medical Research
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