Statistical Methods in Medical Research This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Non-randomized response model for sensitive survey with noncompliance
Collecting representative data on sensitive issues has long been problematic and challenging in public health prevalence investigation (e.g. non-suicidal self-injury), medical research (e.g. drug habits), social issue studies (e.g. history of child abuse), and their interdisciplinary studies (e.g. premarital sexual intercourse). Alternative data collection techniques that can be adopted to study sensitive questions validly become more important and necessary. As an alternative to the famous Warner randomized response model, non-randomized response triangular model has recently been developed to encourage participants to pr...
Source: Statistical Methods in Medical Research - November 15, 2016 Category: Statistics Authors: Wu, Q., Tang, M.-L. Tags: Articles Source Type: research

Statistical methods for the analysis of clinical trials data containing many zeros: An application in vaccine development
In recent years, many vaccines have been developed for the prevention of a variety of diseases. Many of these vaccines, like the one for herpes zoster, are supposed to act in a multilevel way. Ideally, they completely prevent expression of the virus, but failing that they help to reduce the severity of the disease. A simple approach to analyze these data is the so-called burden-of-illness test. The method assigns a score, say W, equal to 0 for the uninfected and a post-infection outcome X > 0 for the infected individuals. One of the limitations of this test is the potential low power when the vaccine efficacy is close t...
Source: Statistical Methods in Medical Research - November 15, 2016 Category: Statistics Authors: Callegaro, A., Kassapian, M., Zahaf, T., Tibaldi, F. Tags: Articles Source Type: research

Predicting birth weight with conditionally linear transformation models
Low and high birth weight (BW) are important risk factors for neonatal morbidity and mortality. Gynecologists must therefore accurately predict BW before delivery. Most prediction formulas for BW are based on prenatal ultrasound measurements carried out within one week prior to birth. Although successfully used in clinical practice, these formulas focus on point predictions of BW but do not systematically quantify uncertainty of the predictions, i.e. they result in estimates of the conditional mean of BW but do not deliver prediction intervals. To overcome this problem, we introduce conditionally linear transformation mode...
Source: Statistical Methods in Medical Research - November 15, 2016 Category: Statistics Authors: Möst, L., Schmid, M., Faschingbauer, F., Hothorn, T. Tags: Articles Source Type: research

Modelling life course blood pressure trajectories using Bayesian adaptive splines
No single study has collected data over individuals’ entire lifespans. To understand changes over the entire life course, it is necessary to combine data from various studies that cover the whole life course. Such combination may be methodologically challenging due to potential differences in study protocols, information available and instruments used to measure the outcome of interest. Motivated by our interest in modelling blood pressure changes over the life course, we propose the use of Bayesian adaptive splines within a hierarchical setting to combine data from several UK-based longitudinal studies where blood p...
Source: Statistical Methods in Medical Research - November 15, 2016 Category: Statistics Authors: Muniz-Terrera, G., Bakra, E., Hardy, R., Matthews, F. E., Lunn, D., FALCon collaboration group, Tags: Articles Source Type: research

Receiver operating characteristic curve estimation for time to event with semicompeting risks and interval censoring
Semicompeting risks and interval censoring are frequent in medical studies, for instance when a disease may be diagnosed only at times of visit and disease onset is in competition with death. To evaluate the ability of markers to predict disease onset in this context, estimators of discrimination measures must account for these two issues. In recent years, methods for estimating the time-dependent receiver operating characteristic curve and the associated area under the ROC curve have been extended to account for right censored data and competing risks. In this paper, we show how an approximation allows to use the inverse ...
Source: Statistical Methods in Medical Research - November 15, 2016 Category: Statistics Authors: Jacqmin-Gadda, H., Blanche, P., Chary, E., Touraine, C., Dartigues, J.-F. Tags: Articles Source Type: research

Comparison of models for analyzing two-group, cross-sectional data with a Gaussian outcome subject to a detection limit
A potential difficulty in the analysis of biomarker data occurs when data are subject to a detection limit. This detection limit is often defined as the point at which the true values cannot be measured reliably. Multiple, regression-type models designed to analyze such data exist. Studies have compared the bias among such models, but few have compared their statistical power. This simulation study provides a comparison of approaches for analyzing two-group, cross-sectional data with a Gaussian-distributed outcome by exploring statistical power and effect size confidence interval coverage of four models able to be implemen...
Source: Statistical Methods in Medical Research - November 15, 2016 Category: Statistics Authors: Wiegand, R. E., Rose, C. E., Karon, J. M. Tags: Articles Source Type: research

Bounded influence function based inference in joint modelling of ordinal partial linear model and accelerated failure time model
A common objective in longitudinal studies is to characterize the relationship between a longitudinal response process and a time-to-event data. Ordinal nature of the response and possible missing information on covariates add complications to the joint model. In such circumstances, some influential observations often present in the data may upset the analysis. In this paper, a joint model based on ordinal partial mixed model and an accelerated failure time model is used, to account for the repeated ordered response and time-to-event data, respectively. Here, we propose an influence function-based robust estimation method....
Source: Statistical Methods in Medical Research - November 15, 2016 Category: Statistics Authors: Chakraborty, A. Tags: Articles Source Type: research

A new risk-adjusted Bernoulli cumulative sum chart for monitoring binary health data
To monitor a health event in patients with a specific risk of developing the event, a risk-adjusted cumulative sum chart is needed. The risk-adjusted cumulative sum chart proposed in the literature has some limitations. Setting appropriate control limits is not straightforward, there is no simple formula for constructing them, and they remain sensitive to changes in the underlying risk distribution and the baseline incidence rate. To overcome these limits, we propose a new risk-adjusted Bernoulli cumulative sum chart as a simple and efficient solution. Analyses of simulated and real data sets illustrate the performance and...
Source: Statistical Methods in Medical Research - November 15, 2016 Category: Statistics Authors: Rossi, G., Sarto, S. D., Marchi, M. Tags: Articles Source Type: research

Penalized count data regression with application to hospital stay after pediatric cardiac surgery
Pediatric cardiac surgery may lead to poor outcomes such as acute kidney injury (AKI) and prolonged hospital length of stay (LOS). Plasma and urine biomarkers may help with early identification and prediction of these adverse clinical outcomes. In a recent multi-center study, 311 children undergoing cardiac surgery were enrolled to evaluate multiple biomarkers for diagnosis and prognosis of AKI and other clinical outcomes. LOS is often analyzed as count data, thus Poisson regression and negative binomial (NB) regression are common choices for developing predictive models. With many correlated prognostic factors and biomark...
Source: Statistical Methods in Medical Research - November 15, 2016 Category: Statistics Authors: Wang, Z., Ma, S., Zappitelli, M., Parikh, C., Wang, C.-Y., Devarajan, P. Tags: Articles Source Type: research

Funnel plot control limits to identify poorly performing healthcare providers when there is uncertainty in the value of the benchmark
There is an increasing use of statistical methods, such as funnel plots, to identify poorly performing healthcare providers. Funnel plots comprise the construction of control limits around a benchmark and providers with outcomes falling outside the limits are investigated as potential outliers. The benchmark is usually estimated from observed data but uncertainty in this estimate is usually ignored when constructing control limits. In this paper, the use of funnel plots in the presence of uncertainty in the value of the benchmark is reviewed for outcomes from a Binomial distribution. Two methods to derive the control limit...
Source: Statistical Methods in Medical Research - November 15, 2016 Category: Statistics Authors: Manktelow, B. N., Seaton, S. E., Evans, T. A. Tags: Articles Source Type: research

A comparison of imputation strategies in cluster randomized trials with missing binary outcomes
In cluster randomized trials, clusters of subjects are randomized rather than subjects themselves, and missing outcomes are a concern as in individual randomized trials. We assessed strategies for handling missing data when analysing cluster randomized trials with a binary outcome; strategies included complete case, adjusted complete case, and simple and multiple imputation approaches. We performed a simulation study to assess bias and coverage rate of the population-averaged intervention-effect estimate. Both multiple imputation with a random-effects logistic regression model or classical logistic regression provided unbi...
Source: Statistical Methods in Medical Research - November 15, 2016 Category: Statistics Authors: Caille, A., Leyrat, C., Giraudeau, B. Tags: Articles Source Type: research

Bayesian latent structure modeling of walking behavior in a physical activity intervention
The analysis of walking behavior in a physical activity intervention is considered. A Bayesian latent structure modeling approach is proposed whereby the ability and willingness of participants is modeled via latent effects. The dropout process is jointly modeled via a linked survival model. Computational issues are addressed via posterior sampling and a simulated evaluation of the longitudinal model’s ability to recover latent structure and predictor effects is considered. We evaluate the effect of a variety of socio-psychological and spatial neighborhood predictors on the propensity to walk and the estimation of la...
Source: Statistical Methods in Medical Research - November 15, 2016 Category: Statistics Authors: Lawson, A. B., Ellerbe, C., Carroll, R., Alia, K., Coulon, S., Wilson, D. K., VanHorn, M. L., George, S. M. S. Tags: Articles Source Type: research

Assessing the inter-rater agreement for ordinal data through weighted indexes
Assessing the inter-rater agreement between observers, in the case of ordinal variables, is an important issue in both the statistical theory and biomedical applications. Typically, this problem has been dealt with the use of Cohen’s weighted kappa, which is a modification of the original kappa statistic, proposed for nominal variables in the case of two observers. Fleiss (1971) put forth a generalization of kappa in the case of multiple observers, but both Cohen’s and Fleiss’ kappa could have a paradoxical behavior, which may lead to a difficult interpretation of their magnitude. In this paper, a modific...
Source: Statistical Methods in Medical Research - November 15, 2016 Category: Statistics Authors: Marasini, D., Quatto, P., Ripamonti, E. Tags: Articles Source Type: research

Multivariate tests based on interpoint distances with application to magnetic resonance imaging
The multivariate location problem is addressed. The most familiar method to address the problem is the Hotelling test. When the hypothesis of normal distributions holds, the Hotelling test is optimal. Unfortunately, in practice the distributions underlying the samples are generally unknown and without assuming normality the finite sample unbiasedness of the Hotelling test is not guaranteed. Moreover, high-dimensional data are increasingly encountered when analyzing medical and biological problems, and in these situations the Hotelling test performs poorly or cannot be computed. A test that is unbiased for non-normal data, ...
Source: Statistical Methods in Medical Research - November 15, 2016 Category: Statistics Authors: Marozzi, M. Tags: Articles Source Type: research

Detecting adverse drug reactions following long-term exposure in longitudinal observational data: The exposure-adjusted self-controlled case series
Most approaches used in postmarketing drug safety monitoring, including spontaneous reporting and statistical risk identification using electronic health care records, are primarily suited to pick up only acute adverse drug effects. With the availability of increasingly larger electronic health record and administrative claims databases comes the opportunity to monitor for potential adverse effects that occur only after prolonged exposure to a drug, but analysis methods are lacking. We propose an adaptation of the self-controlled case series design that uses the notion of accumulated exposure to capture long-term effects o...
Source: Statistical Methods in Medical Research - November 15, 2016 Category: Statistics Authors: Schuemie, M. J., Trifiro, G., Coloma, P. M., Ryan, P. B., Madigan, D. Tags: Articles Source Type: research