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Efficient design and sample size calculation for trials with clustered data
(Source: Statistical Methods in Medical Research)
Source: Statistical Methods in Medical Research - October 9, 2015 Category: Statistics Authors: van Breukelen, G. J., Candel, M. J. Tags: Editorial Source Type: research

Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model
Missing covariate data commonly occur in epidemiological and clinical research, and are often dealt with using multiple imputation. Imputation of partially observed covariates is complicated if the substantive model is non-linear (e.g. Cox proportional hazards model), or contains non-linear (e.g. squared) or interaction terms, and standard software implementations of multiple imputation may impute covariates from models that are incompatible with such substantive models. We show how imputation by fully conditional specification, a popular approach for performing multiple imputation, can be modified so that covariates are i...
Source: Statistical Methods in Medical Research - July 23, 2015 Category: Statistics Authors: Bartlett, J. W., Seaman, S. R., White, I. R., Carpenter, J. R., for the Alzheimer's Disease Neuroimaging Initiative* Tags: Articles Source Type: research

Sample size considerations in active-control non-inferiority trials with binary data based on the odds ratio
This paper presents an approximate closed form sample size formula for determining non-inferiority in active-control trials with binary data. We use the odds-ratio as the measure of the relative treatment effect, derive the sample size formula based on the score test and compare it with a second, well-known formula based on the Wald test. Both closed form formulae are compared with simulations based on the likelihood ratio test. Within the range of parameter values investigated, the score test closed form formula is reasonably accurate when non-inferiority margins are based on odds-ratios of about 0.5 or above and when the...
Source: Statistical Methods in Medical Research - July 23, 2015 Category: Statistics Authors: Siqueira, A. L., Todd, S., Whitehead, A. Tags: Articles Source Type: research

A combined gamma frailty and normal random-effects model for repeated, overdispersed time-to-event data
This paper presents, extends, and studies a model for repeated, overdispersed time-to-event outcomes, subject to censoring. Building upon work by Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010), gamma and normal random effects are included in a Weibull model, to account for overdispersion and between-subject effects, respectively. Unlike these authors, censoring is allowed for, and two estimation methods are presented. The partial marginalization approach to full maximum likelihood of Molenberghs et al. (2010) is contrasted with pseudo-likelihood estimation. A limited simulation stu...
Source: Statistical Methods in Medical Research - July 23, 2015 Category: Statistics Authors: Molenberghs, G., Verbeke, G., Efendi, A., Braekers, R., Demetrio, C. G. Tags: Articles Source Type: research

Analysis of cross-over studies with missing data
This paper addresses some aspects of the analysis of cross-over trials with missing or incomplete data. A literature review on the topic reveals that many proposals provide correct results under the missing completely at random assumption while only some consider the more general missing at random situation. It is argued that mixed-effects models have a role in this context to recover some of the missing intra-subject from the inter-subject information, in particular when missingness is ignorable. Eventually, sensitivity analyses to deal with more general missingness mechanisms are presented. (Source: Statistical Methods in Medical Research)
Source: Statistical Methods in Medical Research - July 23, 2015 Category: Statistics Authors: Rosenkranz, G. K. Tags: Articles Source Type: research

Various varying variances: The challenge of nuisance parameters to the practising biostatistician
The 1997 Biometrics paper by Mike Kenward and James Roger has become a citation classic (more than 1260 citations by End June 2013 according to Google Scholar) and the solution that they proposed to deal with the problem of significance tests of fixed effects in REML models is now incorporated in many software packages and accepted by all biostatisticians as the method of choice. Nevertheless, it does not solve all problems, since there is more to analysis than just significance and since the problems that models with more than one variance pose arise in many contexts. In this paper, I discuss some problems and application...
Source: Statistical Methods in Medical Research - July 23, 2015 Category: Statistics Authors: Senn, S. Tags: Articles Source Type: research

James Roger: A brief biography
(Source: Statistical Methods in Medical Research)
Source: Statistical Methods in Medical Research - July 23, 2015 Category: Statistics Authors: Kenward, M. G. Tags: Editorial Source Type: research

Robust small area prediction for counts
A new semiparametric approach to model-based small area prediction for counts is proposed and used for estimating the average number of visits to physicians for Health Districts in Central Italy. The proposed small area predictor can be viewed as an outlier robust alternative to the more commonly used empirical plug-in predictor that is based on a Poisson generalized linear mixed model with Gaussian random effects. Results from the real data application and from a simulation experiment confirm that the proposed small area predictor has good robustness properties and in some cases can be more efficient than alternative smal...
Source: Statistical Methods in Medical Research - May 26, 2015 Category: Statistics Authors: Tzavidis, N., Ranalli, M. G., Salvati, N., Dreassi, E., Chambers, R. Tags: Articles Source Type: research

A Bayesian network for modelling blood glucose concentration and exercise in type 1 diabetes
This article presents a new statistical approach to analysing the effects of everyday physical activity on blood glucose concentration in people with type 1 diabetes. A physiologically based model of blood glucose dynamics is developed to cope with frequently sampled data on food, insulin and habitual physical activity; the model is then converted to a Bayesian network to account for measurement error and variability in the physiological processes. A simulation study is conducted to determine the feasibility of using Markov chain Monte Carlo methods for simultaneous estimation of all model parameters and prediction of bloo...
Source: Statistical Methods in Medical Research - May 26, 2015 Category: Statistics Authors: Ewings, S. M., Sahu, S. K., Valletta, J. J., Byrne, C. D., Chipperfield, A. J. Tags: Articles Source Type: research

Dependent censoring in piecewise exponential survival models
There are often reasons to suppose that there is dependence between the time to event and time to censoring, or dependent censoring, for survival data, particularly when considering medical data. This is because the decision to treat or not is often made according to prognosis, usually with the most ill patients being prioritised. Due to identifiability issues, sensitivity analyses are often used to assess whether independent censoring can lead to misleading results. In this paper, a sensitivity analysis method for piecewise exponential survival models is presented. This method assesses the sensitivity of the results of st...
Source: Statistical Methods in Medical Research - May 26, 2015 Category: Statistics Authors: Staplin, N., Kimber, A., Collett, D., Roderick, P. Tags: Articles Source Type: research

Transform-both-sides nonlinear models for in vitro pharmacokinetic experiments
Transform-both-sides nonlinear models have proved useful in many experimental applications including those in pharmaceutical sciences and biochemistry. The maximum likelihood method is commonly used to fit transform-both-sides nonlinear models, where the regression and transformation parameters are estimated simultaneously. In this paper, an analysis of variance-based method is described in detail for estimating transform-both-sides nonlinear models from randomized experiments. It estimates the transformation parameter from the full treatment model and then the regression parameters are estimated conditionally on this esti...
Source: Statistical Methods in Medical Research - May 26, 2015 Category: Statistics Authors: Latif, A. M., Gilmour, S. G. Tags: Articles Source Type: research

Editorial for the Special Issue of Statistical Methods in Medical Research at the occasion of the 10th Anniversary of the Southampton Statistical Sciences Research Institute
(Source: Statistical Methods in Medical Research)
Source: Statistical Methods in Medical Research - May 26, 2015 Category: Statistics Authors: Bohning, D., Gilmour, S. G., Smith, P. W. Tags: Editorial Source Type: research

Survival extrapolation using the poly-Weibull model
We describe the model and develop inference procedures using freely available software. The methods are applied to two problems from cardiothoracic transplantation. (Source: Statistical Methods in Medical Research)
Source: Statistical Methods in Medical Research - April 15, 2015 Category: Statistics Authors: Demiris, N., Lunn, D., Sharples, L. D. Tags: Articles Source Type: research

Using proportion of similar response to evaluate correlates of protection for vaccine efficacy
A question of interest in many vaccine clinical development programmes is whether vaccine-induced serum antibody level can be used as a correlate of vaccine efficacy; that is, whether serum antibody levels induced by a candidate vaccine can reliably predict the risk of breakthrough disease. Traditionally, analyses to answer this question have been based on modelling the incidence of breakthrough disease as a function of antibody level, among vaccinated subjects in clinical trials. The Proportion of Similar Response (PSR) method will be described and explored, and compared to the Receive Operator Characteristics (ROC) curve...
Source: Statistical Methods in Medical Research - April 15, 2015 Category: Statistics Authors: Giacoletti, K. E., Heyse, J. Tags: Articles Source Type: research

Measuring continuous baseline covariate imbalances in clinical trial data
This paper presents and compares several methods of measuring continuous baseline covariate imbalance in clinical trial data. Simulations illustrate that though the t-test is an inappropriate method of assessing continuous baseline covariate imbalance, the test statistic itself is a robust measure in capturing imbalance in continuous covariate distributions. Guidelines to assess effects of imbalance on bias, type I error rate and power for hypothesis test for treatment effect on continuous outcomes are presented, and the benefit of covariate-adjusted analysis (ANCOVA) is also illustrated. (Source: Statistical Methods in Medical Research)
Source: Statistical Methods in Medical Research - April 15, 2015 Category: Statistics Authors: Ciolino, J. D., Martin, R. H., Zhao, W., Hill, M. D., Jauch, E. C., Palesch, Y. Y. Tags: Articles Source Type: research