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Statistical Methods in Medical Research

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https://www.readbyqxmd.com/read/30648481/a-robust-bayesian-meta-analytic-approach-to-incorporate-animal-data-into-phase-i-oncology-trials
#1
Haiyan Zheng, Lisa V Hampson, Simon Wandel
Before a first-in-man trial is conducted, preclinical studies are performed in animals to help characterise the safety profile of the new medicine. We propose a robust Bayesian hierarchical model to synthesise animal and human toxicity data, using scaling factors to translate doses administered to different animal species onto an equivalent human scale. After scaling doses, the parameters of dose-toxicity models intrinsic to different animal species can be interpreted on a common scale. A prior distribution is specified for each translation factor to capture uncertainty about differences between toxicity of the drug in animals and humans...
January 16, 2019: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/30623732/methods-for-comparing-durability-of-immune-responses-between-vaccine-regimens-in-early-phase-trials
#2
Ted Westling, Michal Juraska, Kelly E Seaton, Georgia D Tomaras, Peter B Gilbert, Holly Janes
The ability to produce a long-lasting, or durable, immune response is a crucial characteristic of many highly effective vaccines. A goal of early-phase vaccine trials is often to compare the immune response durability of multiple tested vaccine regimens. One parameter for measuring immune response durability is the area under the mean post-peak log immune response profile. In this paper, we compare immune response durability across vaccine regimens within and between two phase I trials of DNA-primed HIV vaccine regimens, HVTN 094 and HVTN 096...
January 9, 2019: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/30612522/a-multi-locus-predictiveness-curve-and-its-summary-assessment-for-genetic-risk-prediction
#3
Changshuai Wei, Ming Li, Yalu Wen, Chengyin Ye, Qing Lu
Genetic association studies using high-throughput genotyping and sequencing technologies have identified a large number of genetic variants associated with complex human diseases. These findings have provided an unprecedented opportunity to identify individuals in the population at high risk for disease who carry causal genetic mutations and hold great promise for early intervention and individualized medicine. While interest is high in building risk prediction models based on recent genetic findings, it is crucial to have appropriate statistical measurements to assess the performance of a genetic risk prediction model...
January 7, 2019: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/30612519/nonparametric-competing-risks-analysis-using-bayesian-additive-regression-trees
#4
Rodney Sparapani, Brent R Logan, Robert E McCulloch, Purushottam W Laud
Many time-to-event studies are complicated by the presence of competing risks. Such data are often analyzed using Cox models for the cause-specific hazard function or Fine and Gray models for the subdistribution hazard. In practice, regression relationships in competing risks data are often complex and may include nonlinear functions of covariates, interactions, high-dimensional parameter spaces and nonproportional cause-specific, or subdistribution, hazards. Model misspecification can lead to poor predictive performance...
January 7, 2019: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/30606087/joint-models-of-tumour-size-and-lymph-node-spread-for-incident-breast-cancer-cases-in-the-presence-of-screening
#5
Gabriel Isheden, Linda Abrahamsson, Therese Andersson, Kamila Czene, Keith Humphreys
Continuous growth models show great potential for analysing cancer screening data. We recently described such a model for studying breast cancer tumour growth based on modelling tumour size at diagnosis, as a function of screening history, detection mode, and relevant patient characteristics. In this article, we describe how the approach can be extended to jointly model tumour size and number of lymph node metastases at diagnosis. We propose a new class of lymph node spread models which are biologically motivated and describe how they can be extended to incorporate random effects to allow for heterogeneity in underlying rates of spread...
January 3, 2019: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/30600776/promoting-sign-consistency-in-the-cure-model-estimation-and-selection
#6
Xingjie Shi, Shuangge Ma, Yuan Huang
In survival analysis, when a subset of subjects has extremely long survival, the two-part cure rate model has been commonly adopted. In the two-part model, the first part is for a binary response and describes the probability of cure. The second part is for a survival response and describes the probability of survival. Despite their intuitive interconnections, most of the existing works estimate the two parts without any constraint. The existing works on proportionality promote similarity in magnitudes (i.e...
January 2, 2019: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/30600769/small-sample-performance-and-underlying-assumptions-of-a-bootstrap-based-inference-method-for-a-general-analysis-of-covariance-model-with-possibly-heteroskedastic-and-nonnormal-errors
#7
Georg Zimmermann, Markus Pauly, Arne C Bathke
It is well known that the standard F test is severely affected by heteroskedasticity in unbalanced analysis of covariance models. Currently available potential remedies for such a scenario are based on heteroskedasticity-consistent covariance matrix estimation (HCCME). However, the HCCME approach tends to be liberal in small samples. Therefore, in the present paper, we propose a combination of HCCME and a wild bootstrap technique, with the aim of improving the small-sample performance. We precisely state a set of assumptions for the general analysis of covariance model and discuss their practical interpretation in detail, since this issue may have been somewhat neglected in applied research so far...
January 2, 2019: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/30599802/a-bayesian-method-to-estimate-the-optimal-threshold-of-a-marker-used-to-select-patients-treatment
#8
Yoann Blangero, Muriel Rabilloud, René Ecochard, Fabien Subtil
The use of a quantitative treatment selection marker to choose between two treatment options requires the estimate of an optimal threshold above which one of these two treatments is preferred. Herein, the optimal threshold expression is based on the definition of a utility function which aims to quantify the expected utility of the population (e.g. life expectancy, quality of life) by taking into account both efficacy (success or failure) and toxicity of each treatment option. Therefore, the optimal threshold is the marker value that maximizes the expected utility of the population...
January 2, 2019: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/30592240/efficient-estimation-of-a-linear-transformation-model-for-current-status-data-via-penalized-splines
#9
Minggen Lu, Yan Liu, Chin-Shang Li
We propose a flexible and computationally efficient penalized estimation method for a semi-parametric linear transformation model with current status data. To facilitate model fitting, the unknown monotone function is approximated by monotone B-splines, and a computationally efficient hybrid algorithm involving the Fisher scoring algorithm and the isotonic regression is developed. A goodness-of-fit test and model diagnostics are also considered. The asymptotic properties of the penalized estimators are established, including the optimal rate of convergence for the function estimator and the semi-parametric efficiency for the regression parameter estimators...
December 28, 2018: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/30569832/covariate-adjusted-survival-analyses-in-propensity-score-matched-samples-imputing-potential-time-to-event-outcomes
#10
Peter C Austin, Neal Thomas, Donald B Rubin
Matching on an estimated propensity score is frequently used to estimate the effects of treatments from observational data. Since the 1970s, different authors have proposed methods to combine matching at the design stage with regression adjustment at the analysis stage when estimating treatment effects for continuous outcomes. Previous work has consistently shown that the combination has generally superior statistical properties than either method by itself. In biomedical and epidemiological research, survival or time-to-event outcomes are common...
December 20, 2018: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/30543154/a-default-prior-for-regression-coefficients
#11
Erik van Zwet
When the sample size is not too small, M-estimators of regression coefficients are approximately normal and unbiased. This leads to the familiar frequentist inference in terms of normality-based confidence intervals and p-values. From a Bayesian perspective, use of the (improper) uniform prior yields matching results in the sense that posterior quantiles agree with one-sided confidence bounds. For this, and various other reasons, the uniform prior is often considered objective or non-informative. In spite of this, we argue that the uniform prior is not suitable as a default prior for inference about a regression coefficient in the context of the bio-medical and social sciences...
December 13, 2018: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/30543153/transformation-model-estimation-of-survival-under-dependent-truncation-and-independent-censoring
#12
Sy Han Chiou, Matthew D Austin, Jing Qian, Rebecca A Betensky
Truncation is a mechanism that permits observation of selected subjects from a source population; subjects are excluded if their event times are not contained within subject-specific intervals. Standard survival analysis methods for estimation of the distribution of the event time require quasi-independence of failure and truncation. When quasi-independence does not hold, alternative estimation procedures are required; currently, there is a copula model approach that makes strong modeling assumptions, and a transformation model approach that does not allow for right censoring...
December 13, 2018: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/30526385/shape-invariant-mixture-model-for-clustering-non-linear-longitudinal-growth-trajectories
#13
Zihang Lu, Wendy Lou
In longitudinal studies, it is often of great interest to cluster individual trajectories based on repeated measurements taken over time. Non-linear growth trajectories are often seen in practice, and the individual data can also be measured sparsely, and at irregular time points, which may complicate the modeling process. Motivated by a study of pregnant women hormone profiles, we proposed a shape invariant growth mixture model for clustering non-linear growth trajectories. Bayesian inference via Monte Carlo Markov Chain was employed to estimate the parameters of interest...
December 10, 2018: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/30514179/confidence-intervals-for-the-mann-whitney-test
#14
Maja Pohar Perme, Damjan Manevski
The Mann-Whitney test is a commonly used non-parametric alternative of the two-sample t-test. Despite its frequent use, it is only rarely accompanied with confidence intervals of an effect size. If reported, the effect size is usually measured with the difference of medians or the shift of the two distribution locations. Neither of these two measures directly coincides with the test statistic of the Mann-Whitney test, so the interpretation of the test results and the confidence intervals may be importantly different...
December 4, 2018: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/30514161/meta-analysis-of-non-statistically-significant-unreported-effects
#15
Anton Albajes-Eizagirre, Aleix Solanes, Joaquim Radua
Published studies in Medicine (and virtually any other discipline) sometimes report that a difference or correlation did not reach statistical significance but do not report its effect size or any statistic from which the latter may be derived. Unfortunately, meta-analysts should not exclude these studies because their exclusion would bias the meta-analytic outcome, but also they cannot be included as null effect sizes because this strategy is also associated to bias. To overcome this problem, we have developed MetaNSUE, a novel method based on multiple imputations of the censored information...
December 4, 2018: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/30472921/a-gee-type-approach-to-untangle-structural-and-random-zeros-in-predictors
#16
Peng Ye, Wan Tang, Jiang He, Hua He
Count outcomes with excessive zeros are common in behavioral and social studies, and zero-inflated count models such as zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) can be applied when such zero-inflated count data are used as response variable. However, when the zero-inflated count data are used as predictors, ignoring the difference of structural and random zeros can result in biased estimates. In this paper, a generalized estimating equation (GEE)-type mixture model is proposed to jointly model the response of interest and the zero-inflated count predictors...
November 26, 2018: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/30474497/predicting-diabetes-related-hospitalizations-based-on-electronic-health-records
#17
Theodora S Brisimi, Tingting Xu, Taiyao Wang, Wuyang Dai, Ioannis Ch Paschalidis
OBJECTIVE: To derive a predictive model to identify patients likely to be hospitalized during the following year due to complications attributed to Type II diabetes. METHODS: A variety of supervised machine learning classification methods were tested and a new method that discovers hidden patient clusters in the positive class (hospitalized) was developed while, at the same time, sparse linear support vector machine classifiers were derived to separate positive samples from the negative ones (non-hospitalized)...
November 25, 2018: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/30474490/modelling-of-zero-inflation-improves-inference-of-metagenomic-gene-count-data
#18
Viktor Jonsson, Tobias Ă–sterlund, Olle Nerman, Erik Kristiansson
Metagenomics enables the study of gene abundances in complex mixtures of microorganisms and has become a standard methodology for the analysis of the human microbiome. However, gene abundance data is inherently noisy and contains high levels of biological and technical variability as well as an excess of zeros due to non-detected genes. This makes the statistical analysis challenging. In this study, we present a new hierarchical Bayesian model for inference of metagenomic gene abundance data. The model uses a zero-inflated overdispersed Poisson distribution which is able to simultaneously capture the high gene-specific variability as well as zero observations in the data...
November 25, 2018: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/30474484/now-trending-coping-with-non-parallel-trends-in-difference-in-differences-analysis
#19
Andrew M Ryan, Evangelos Kontopantelis, Ariel Linden, James F Burgess
Difference-in-differences (DID) analysis is used widely to estimate the causal effects of health policies and interventions. A critical assumption in DID is "parallel trends": that pre-intervention trends in outcomes are the same between treated and comparison groups. To date, little guidance has been available to researchers who wish to use DID when the parallel trends assumption is violated. Using a Monte Carlo simulation experiment, we tested the performance of several estimators (standard DID; DID with propensity score matching; single-group interrupted time-series analysis; and multi-group interrupted time-series analysis) when the parallel trends assumption is violated...
November 25, 2018: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/30474473/beta-regression-in-the-presence-of-outliers-a-wieldy-bayesian-solution
#20
Janet van Niekerk, Andriette Bekker, Mohammad Arashi
Real phenomena often leads to challenges in data. One of these is outliers or influential values. Especially in a small sample, these values can have a major influence on the modeling process. In the beta regression framework, this issue has been addressed mainly in two ways: the assumption of a different response model and the application of a minimum density power divergence estimation (MDPDE) procedure. In this paper, however, we propose a simple hierarchical Bayesian methodology in the context of a varying dispersion beta response model that is robust to outliers, as shown through an extensive simulation study and analysis of two real data sets...
November 25, 2018: Statistical Methods in Medical Research
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