Statistical Methods in Medical Research

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 existing surveillance systems and to facilitate more accurate and rapid detection of safety problems, new systems involving active surveillance of large, population-based cohorts using observational health care databases are being developed. In this article, we review common statistical methods that have been employed previously for post-licensure safety monitoring, including data mining and sequential hypothesis testing, and assess which methods may be promising for potential use within this newly proposed prospective observational cohort monitoring framework. We discuss gaps in existing approaches and identify areas where methodological development is needed to improve the success of safety surveillance efforts in this setting.

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 fully likelihood-based two-part marginal model that satisfies this need by using the bridge distribution for the random effect in the binary part of an underlying two-part mixed model; and its maximum likelihood estimation can be routinely implemented via standard statistical software such as the SAS NLMIXED procedure. We illustrate the usage of this new model by investigating the marginal effects of pre-specified genetic markers on physical functioning, as measured by the Health Assessment Questionnaire, in a cohort of psoriatic arthritis patients from the University of Toronto Psoriatic Arthritis Clinic. An added benefit of our proposed marginal model when compared to a two-part mixed model is the robustness in regression parameter estimation when departure from the true random effects structure occurs. This is demonstrated through simulation.

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 differently, depending on whether the system is in an epidemic or non-epidemic phase. To do so, we propose a hidden Markov model in which the transition between both phases is modelled as a function of the epidemic state of the previous week. Different options for modelling the rates are described, including the option of modelling the mean at each phase as autoregressive processes of order 0, 1 or 2. Bayesian inference is carried out to provide the probability of being in an epidemic state at any given moment. The methodology is applied to various influenza data sets. The results indicate that our methods outperform previous approaches in terms of sensitivity, specificity and timeliness.

The relationship between two independent binomial proportions is commonly estimated and presented using the difference between proportions, the number needed to treat, the ratio of proportions or the odds ratio. Several different confidence intervals are available, but they can produce markedly different results. Some of the traditional approaches, such as the Wald interval for the difference between proportions and the Katz log interval for the ratio of proportions, do not perform well unless the sample size is large. Better intervals are available. 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.

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.

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 as a graphical tool and the area under the ROC (AUROC) as a summary measure in the context of evaluating correlates of protection. A way to use PSR analysis as complementary to Youden's index as a simple and elegant method to determine the discriminatory ability of a test and to set an optimal threshold value will be presented. In addition, the relationships among PSR and other measures of overlap and discrimination will be described. An example based on a clinical trial from a development programme for a vaccine against human papillomavirus (HPV) will be presented.

Recent studies of (cost-) effectiveness in cardiothoracic transplantation have required estimation of mean survival over the lifetime of the recipients. In order to calculate mean survival, the complete survivor curve is required but is often not fully observed, so that survival extrapolation is necessary. After transplantation, the hazard function is bathtub-shaped, reflecting latent competing risks which operate additively in overlapping time periods. The poly-Weibull distribution is a flexible parametric model that may be used to extrapolate survival and has a natural competing risks interpretation. In addition, treatment effects and subgroups can be modelled separately for each component of risk. We describe the model and develop inference procedures using freely available software. The methods are applied to two problems from cardiothoracic transplantation.