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Recommended confidence intervals for two independent binomial proportions
This article describes and compares approximate and exact confidence intervals that are – with one exception – easy to calculate or available in common software packages. We illustrate the performances of the intervals and make recommendations for both small and moderate-to-large sample sizes. (Source: Statistical Methods in Medical Research)
Source: Statistical Methods in Medical Research - April 15, 2015 Category: Statistics Authors: Fagerland, M. W., Lydersen, S., Laake, P. Tags: Articles Source Type: research

Bayesian hierarchical Poisson models with a hidden Markov structure for the detection of influenza epidemic outbreaks
Considerable effort has been devoted to the development of statistical algorithms for the automated monitoring of influenza surveillance data. In this article, we introduce a framework of models for the early detection of the onset of an influenza epidemic which is applicable to different kinds of surveillance data. In particular, the process of the observed cases is modelled via a Bayesian Hierarchical Poisson model in which the intensity parameter is a function of the incidence rate. The key point is to consider this incidence rate as a normal distribution in which both parameters (mean and variance) are modelled differe...
Source: Statistical Methods in Medical Research - April 15, 2015 Category: Statistics Authors: Conesa, D., Martinez-Beneito, M., Amoros, R., Lopez-Quilez, A. Tags: Articles Source Type: research

A likelihood-based two-part marginal model for longitudinal semicontinuous data
Two-part models are an attractive approach for analysing longitudinal semicontinuous data consisting of a mixture of true zeros and continuously distributed positive values. When the population-averaged (marginal) covariate effects are of interest, two-part models that provide straightforward interpretation of the marginal effects are desirable. Presently, the only available approaches for fitting two-part marginal models to longitudinal semicontinuous data are computationally difficult to implement. Therefore, there exists a need to develop two-part marginal models that can be easily implemented in practice. We propose a ...
Source: Statistical Methods in Medical Research - April 15, 2015 Category: Statistics Authors: Su, L., Tom, B. D., Farewell, V. T. Tags: Articles Source Type: research

Methods for observational post-licensure medical product safety surveillance
Post-licensure medical product safety surveillance is important for detecting adverse events potentially not identified pre-licensure. Historically, post-licensure safety monitoring has been accomplished using passive reporting systems and by conducting formal Phase IV randomized trials or large epidemiological studies, also known as safety surveillance or pharmacovigilance studies. However, crucial gaps in the safety evidence base provided by these approaches have led to high profile product withdrawals and growing public concern about unknown health risks associated with licensed products. To address the limitations of e...
Source: Statistical Methods in Medical Research - April 15, 2015 Category: Statistics Authors: Nelson, J. C., Cook, A. J., Yu, O., Zhao, S., Jackson, L. A., Psaty, B. M. Tags: Articles Source Type: research

Meta-analysis of the technical performance of an imaging procedure: Guidelines and statistical methodology
Medical imaging serves many roles in patient care and the drug approval process, including assessing treatment response and guiding treatment decisions. These roles often involve a quantitative imaging biomarker, an objectively measured characteristic of the underlying anatomic structure or biochemical process derived from medical images. Before a quantitative imaging biomarker is accepted for use in such roles, the imaging procedure to acquire it must undergo evaluation of its technical performance, which entails assessment of performance metrics such as repeatability and reproducibility of the quantitative imaging biomar...
Source: Statistical Methods in Medical Research - March 24, 2015 Category: Statistics Authors: Huang, E. P., Wang, X.-F., Choudhury, K. R., McShane, L. M., Gonen, M., Ye, J., Buckler, A. J., Kinahan, P. E., Reeves, A. P., Jackson, E. F., Guimaraes, A. R., Zahlmann, G., Meta-Analysis Working Group Tags: Articles Source Type: research

Statistical issues in the comparison of quantitative imaging biomarker algorithms using pulmonary nodule volume as an example
This study allows a direct assessment of six algorithms’ performance for measuring tumor change. With these three examples we compare and contrast study designs and performance metrics, and we illustrate the advantages and limitations of various common statistical methods for quantitative imaging biomarker studies. (Source: Statistical Methods in Medical Research)
Source: Statistical Methods in Medical Research - March 24, 2015 Category: Statistics Authors: Obuchowski, N. A., Barnhart, H. X., Buckler, A. J., Pennello, G., Wang, X.-F., Kalpathy-Cramer, J., Kim, H. J., Reeves, A. P., for the Case Example Working Group Tags: Articles Source Type: research

Quantitative imaging biomarkers: A review of statistical methods for computer algorithm comparisons
Quantitative biomarkers from medical images are becoming important tools for clinical diagnosis, staging, monitoring, treatment planning, and development of new therapies. While there is a rich history of the development of quantitative imaging biomarker (QIB) techniques, little attention has been paid to the validation and comparison of the computer algorithms that implement the QIB measurements. In this paper we provide a framework for QIB algorithm comparisons. We first review and compare various study designs, including designs with the true value (e.g. phantoms, di...
Source: Statistical Methods in Medical Research - March 24, 2015 Category: Statistics Authors: Obuchowski, N. A., Reeves, A. P., Huang, E. P., Wang, X.-F., Buckler, A. J., Kim, H. J., Barnhart, H. X., Jackson, E. F., Giger, M. L., Pennello, G., Toledano, A. Y., Kalpathy-Cramer, J., Apanasovich, T. V., Kinahan, P. E., Myers, K. J., Goldgof, D. B., B Tags: Articles Source Type: research

Quantitative imaging biomarkers: A review of statistical methods for technical performance assessment
Technological developments and greater rigor in the quantitative measurement of biological features in medical images have given rise to an increased interest in using quantitative imaging biomarkers to measure changes in these features. Critical to the performance of a quantitative imaging biomarker in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs,...
Source: Statistical Methods in Medical Research - March 24, 2015 Category: Statistics Authors: Raunig, D. L., McShane, L. M., Pennello, G., Gatsonis, C., Carson, P. L., Voyvodic, J. T., Wahl, R. L., Kurland, B. F., Schwarz, A. J., Gonen, M., Zahlmann, G., Kondratovich, M. V., O'Donnell, K., Petrick, N., Cole, P. E., Garra, B., Sullivan, D. C., QIBA Tags: Articles Source Type: research

The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions
The development and implementation of quantitative imaging biomarkers has been hampered by the inconsistent and often incorrect use of terminology related to these markers. Sponsored by the Radiological Society of North America, an interdisciplinary group of radiologists, statisticians, physicists, and other researchers worked to develop a comprehensive terminology to serve as a foundation for quantitative imaging biomarker claims. Where possible, this working group adapted existing definitions derived from national or international standards bodies rather than invent new definitions for these terms. This terminology also ...
Source: Statistical Methods in Medical Research - March 24, 2015 Category: Statistics Authors: Kessler, L. G., Barnhart, H. X., Buckler, A. J., Choudhury, K. R., Kondratovich, M. V., Toledano, A., Guimaraes, A. R., Filice, R., Zhang, Z., Sullivan, D. C., QIBA Terminology Working Group Tags: Articles Source Type: research

Introduction to metrology series
(Source: Statistical Methods in Medical Research)
Source: Statistical Methods in Medical Research - March 24, 2015 Category: Statistics Authors: Sullivan, D. C., Bresolin, L., Seto, B., Obuchowski, N. A., Raunig, D. L., Kessler, L. G. Tags: Editorial Source Type: research

Joint spatial Bayesian modeling for studies combining longitudinal and cross-sectional data
Design for intervention studies may combine longitudinal data collected from sampled locations over several survey rounds and cross-sectional data from other locations in the study area. In this case, modeling the impact of the intervention requires an approach that can accommodate both types of data, accounting for the dependence between individuals followed up over time. Inadequate modeling can mask intervention effects, with serious implications for policy making. In this paper we use data from a large-scale larviciding intervention for malaria control implemented in Dar es Salaam, United Republic of Tanzania, collected...
Source: Statistical Methods in Medical Research - November 18, 2014 Category: Statistics Authors: Lawson, A. B., Carroll, R., Castro, M. Tags: Articles Source Type: research

Semiparametric M-quantile regression for count data
Lung cancer incidence over 2005–2010 for 326 Local Authority Districts in England is investigated by ecological regression. Motivated from mis-specification of a Negative Binomial additive model, a semiparametric Negative Binomial M-quantile regression model is introduced. The additive part relates to those univariate or bivariate smoothing components, which are included in the model to capture nonlinearities in the predictor or to account for spatial dependence. All such components are estimated using penalized splines. The results show the capability of the semiparametric Negative Binomial M-quantile regression mod...
Source: Statistical Methods in Medical Research - November 18, 2014 Category: Statistics Authors: Dreassi, E., Ranalli, M. G., Salvati, N. Tags: Articles Source Type: research

Prospective analysis of infectious disease surveillance data using syndromic information
In this paper, we describe a Bayesian hierarchical Poisson model for the prospective analysis of data for infectious diseases. The proposed model consists of two components. The first component describes the behavior of disease during nonepidemic periods and the second component represents the increase in disease counts due to the presence of an epidemic. A novelty of our model formulation is that the parameters describing the spread of epidemics are allowed to vary in both space and time. We also show how syndromic information can be incorporated into the model to provide a better description of the data and more accurate...
Source: Statistical Methods in Medical Research - November 18, 2014 Category: Statistics Authors: Corberan-Vallet, A., Lawson, A. B. Tags: Articles Source Type: research

Bayesian hierarchical modelling of noisy spatial rates on a modestly large and discontinuous irregular lattice
We present Bayesian hierarchical spatial model development motivated from a recent analysis of noisy small area response rate data, named the Booster data. The Booster data are postcode-level aggregates from a recent mail-out recruitment for a physical exercise intervention in deprived urban neighbourhoods in Sheffield, UK. Bayesian hierarchical Bernoulli-binomial spatial mixture zero-inflated Binomial models were developed for modelling overdispersion and for separation of systematic and random variations in the noisy and mostly low crude response rates. We present methods that enabled us to explore the underlying spatial...
Source: Statistical Methods in Medical Research - November 18, 2014 Category: Statistics Authors: MacNab, Y. C., Read, S., Strong, M., Pearson, T., Maheswaran, R., Goyder, E. Tags: Articles Source Type: research

Evaluation of cluster recovery for small area relative risk models
The analysis of disease risk is often considered via relative risk. The comparison of relative risk estimation methods with "true risk" scenarios has been considered on various occasions. However, there has been little examination of how well competing methods perform when the focus is clustering of risk. In this paper, a simulated evaluation of a range of potential spatial risk models and a range of measures that can be used for (a) cluster goodness of fit, (b) cluster diagnostics are considered. Results suggest that exceedence probability is a poor measure of hot spot clustering because of model dependence, whereas resid...
Source: Statistical Methods in Medical Research - November 18, 2014 Category: Statistics Authors: Rotejanaprasert, C. Tags: Articles Source Type: research