In biomedical research and practice, quantitative tests or biomarkers are often used for diagnostic or screening purposes, with a cut point established on the quantitative measurement to aid binary classification. This paper introduces an alternative to the traditional methods based on the Youden index and the closest-to-(0, 1) criterion for threshold selection. A concordance probability evaluating the classification accuracy of a dichotomized measure is defined as an objective function of the possible cut point. A nonparametric approach is used to search for the optimal cut point maximizing the objective function. The procedure is shown to perform well in a simulation study. Using data from a real-world study of arsenic-induced skin lesions, we apply the method to a measure of blood arsenic levels, selecting a cut point to be used as a warning threshold. Copyright © 2012 John Wiley & Sons, Ltd.

Many biomedical studies focus on delaying disease relapses and on prolonging survival. Usual methods only consider one event, often the first recurrence or death. However, ignoring the other recurrences may lead to biased results. The whole history of the disease should be considered for each patient. In addition, some diseases involve recurrences that can increase the risk of death. In this case, the death time may be dependent on the recurrent event history. We propose a joint frailty model to analyze recurrences and death simultaneously. Two gamma-distributed frailties take into account both the inter-recurrences dependence and the dependence between the recurrences and the survival times. We estimate separate parameters for disease recurrent event times and survival times in the joint frailty model to distinguish treatment effects and prognostic factors on these two types of events. We show how maximum penalized likelihood estimation can be applied to semiparametric estimation of the continuous hazard functions in the proposed joint frailty model with right censoring. We also propose parametrical approach. We evaluate the model by simulation studies and illustrate through a study of patients with follicular lymphoma. Copyright © 2012 John Wiley & Sons, Ltd.

One of the most critical decision points during drug development is to make a phase 3 go/no go decision after a phase 2 proof of concept trial is conducted. It is particularly challenging in oncology drug development where oftentimes the primary endpoint for phase 3 trial is overall survival (OS), but the phase 2 proof of concept trial is powered only for an early endpoint, typically progression-free survival (PFS), whose relationship to OS is often unclear. We propose the use of predictive power to assist the phase 3 go/no go decision by evaluating the strength of actual observed phase 2 efficacy effects in terms of how likely it will predict the chance of OS success in the subsequent phase 3 trial. The formula is provided for calculation of predictive power based on either observed PFS effect only, or observed OS effect only, or both. An example is provided to compare these three predictive powers, which shows that when there is little prior information about PFS and OS, the predictability based on the observed phase 2 PFS effect is low and not sensitive to the size of the trial and extent of the observed PFS effect, and it also has limited added value to the predictability based on the observed phase 2 OS effect. Therefore, one should be cautious of inherently high risk of making a phase 3 go/no go decision based on phase 2 PFS outcome alone and should take the phase 2 OS data into consideration whenever possible. Copyright © 2012 John Wiley & Sons, Ltd.

Tree-based methods have become popular for analyzing complex data structures where the primary goal is risk stratification of patients. Ensemble techniques improve the accuracy in prediction and address the instability in a single tree by growing an ensemble of trees and aggregating. However, in the process, individual trees get lost. In this paper, we propose a methodology for identifying the most representative trees in an ensemble on the basis of several tree distance metrics. Although our focus is on binary outcomes, the methods are applicable to censored data as well. For any two trees, the distance metrics are chosen to (1) measure similarity of the covariates used to split the trees; (2) reflect similar clustering of patients in the terminal nodes of the trees; and (3) measure similarity in predictions from the two trees. Whereas the latter focuses on prediction, the first two metrics focus on the architectural similarity between two trees. The most representative trees in the ensemble are chosen on the basis of the average distance between a tree and all other trees in the ensemble. Out-of-bag estimate of error rate is obtained using neighborhoods of representative trees. Simulations and data examples show gains in predictive accuracy when averaging over such neighborhoods. We illustrate our methods using a dataset of kidney cancer treatment receipt (binary outcome) and a second dataset of breast cancer survival (censored outcome). Copyright © 2012 John Wiley & Sons, Ltd.

Quality of life (QOL) assessment is a key component of many clinical studies and frequently requires the use of single global summary measures that capture the overall balance of findings from a potentially wide-ranging assessment of QOL issues. We propose and evaluate an irregular multilevel latent variable model suitable for use as a global summary tool for health-related QOL assessments. The proposed model is a multiple indicator and multiple cause style of model with a two-level latent variable structure. We approach the modeling from a general multilevel modeling perspective, using a combination of random and nonrandom cluster types to accommodate the mixture of issues commonly evaluated in health-related QOL assessments—overall perceptions of QOL and health, along with specific psychological, physical, social, and functional issues. Using clinical trial data, we evaluate the merits and application of this approach in detail, both for mean global QOL and for change from baseline. We show that the proposed model generally performs well in comparing global patterns of treatment effect and provides more precise and reliable estimates than several common alternatives such as selecting from or averaging observed global item measures. A variety of computational methods could be used for estimation. We derived a closed-form expression for the marginal likelihood that can be used to obtain maximum likelihood parameter estimates when normality assumptions are reasonable. Our approach is useful for QOL evaluations aimed at pharmacoeconomic or individual clinical decision making and in obtaining summary QOL measures for use in quality-adjusted survival analyses. Copyright © 2012 John Wiley & Sons, Ltd.

For genome-wide association studies, we propose a new method for identifying significant biological pathways. In this approach, we aggregate data across single-nucleotide polymorphisms to obtain summary measures at the gene level. We then use a hierarchical Bayesian model, which takes the gene-level summary measures as data, in order to evaluate the relevance of each pathway to an outcome of interest (e.g., disease status). Although shifting the focus of analysis from individual genes to pathways has proven to improve the statistical power and provide more robust results, such methods tend to eliminate a large number of genes whose pathways are unknown. For these genes, we propose to use a Bayesian multinomial logit model to predict the associated pathways by using the genes with known pathways as the training data. Our hierarchical Bayesian model takes the uncertainty regarding the pathway predictions into account while assessing the significance of pathways. We apply our method to two independent studies on type 2 diabetes and show that the overlap between the results from the two studies is statistically significant. We also evaluate our approach on the basis of simulated data. Copyright © 2012 John Wiley & Sons, Ltd.

Hidden Markov models (HMMs) are frequently used to analyse longitudinal data, where the same set of subjects is repeatedly observed over time. In this context, several sources of heterogeneity may arise at individual and/or time level, which affect the hidden process, that is, the transition probabilities between the hidden states. In this paper, we propose the use of a finite mixture of non-homogeneous HMMs (NH-HMMs) to face the heterogeneity problem. The non-homogeneity of the model allows us to take into account observed sources of heterogeneity by means of a proper set of covariates, time and/or individual dependent, explaining the variations in the transition probabilities. Moreover, we handle the unobserved sources of heterogeneity at the individual level, due to, for example, omitted covariates, by introducing a random term with a discrete distribution. The resulting model is a finite mixture of NH-HMM that can be used to classify individuals according to their dynamic behaviour or to estimate a mixed NH-HMM without any assumption regarding the distribution of the random term following the non-parametric maximum likelihood approach. We test the effectiveness of the proposal through a simulation study and an application to real data on alcohol abuse. Copyright © 2012 John Wiley & Sons, Ltd.

In many applications two correlated estimates of an effect size need to be considered simultaneously to be combined or compared. Apparently, there is a need for calculating their covariance, which however requires access to the individual data that may not be available to a researcher performing the analysis. We present a simple and efficient method for calculating the covariance of two correlated log-odds ratios. The method is very simple, is based on the well-known large sample approximations, can be applied using only data that are available in the published reports and more importantly, is very general, because it is shown to encompass several previously derived estimates (multiple outcomes, multiple treatments, dose–response models, mutually exclusive outcomes, genetic association studies) as special cases. By encompassing the previous approaches in a unified framework, the method allows easily deriving estimates for the covariance concerning problems that were not easy to be obtained otherwise. We show that the method can be used to derive the covariance of log-odds ratios from matched and unmatched case-control studies that use the same cases, a situation that has been addressed in the past only using individual data. Future applications of the method are discussed. Copyright © 2012 John Wiley & Sons, Ltd.

Net survival, the one that would be observed if cancer were the only cause of death, is the most appropriate indicator to compare cancer mortality between areas or countries. Several parametric and non-parametric methods have been developed to estimate net survival, particularly when the cause of death is unknown. These methods are based either on the relative survival ratio or on the additive excess hazard model, the latter using the general population mortality hazard to estimate the excess mortality hazard (the hazard related to net survival). The present work used simulations to compare estimator abilities to estimate net survival in different settings such as the presence/absence of an age effect on the excess mortality hazard or on the potential time of follow-up, knowing that this covariate has an effect on the general population mortality hazard too. It showed that when age affected the excess mortality hazard, most estimators, including specific survival, were biased. Only two estimators were appropriate to estimate net survival. The first is based on a multivariable excess hazard model that includes age as covariate. The second is non-parametric and is based on the inverse probability weighting. These estimators take differently into account the informative censoring induced by the expected mortality process. The former offers great flexibility whereas the latter requires neither the assumption of a specific distribution nor a model-building strategy. Because of its simplicity and availability in commonly used software, the nonparametric estimator should be considered by cancer registries for population-based studies. Copyright © 2012 John Wiley & Sons, Ltd.

The objective of finding a parsimonious representation of the observed data by a statistical model that is also capable of accurate prediction is commonplace in all domains of statistical applications. The parsimony of the solutions obtained by variable selection is usually counterbalanced by a limited prediction capacity. On the other hand, methodologies that assure high prediction accuracy usually lead to models that are neither simple nor easily interpretable. Regularization methodologies have proven to be useful in addressing both prediction and variable selection problems. The Bayesian approach to regularization constitutes a particularly attractive alternative as it is suitable for high-dimensional modeling, offers valid standard errors, and enables simultaneous estimation of regression coefficients and complexity parameters via computationally efficient MCMC techniques. Bayesian regularization falls within the versatile framework of Bayesian hierarchical models, which encompasses a variety of other approaches suited for variable selection such as spike and slab models and the MC³ approach. In this article, we review these Bayesian developments and evaluate their variable selection performance in a simulation study for the classical small p large n setting. The majority of the existing Bayesian methodology for variable selection deals only with classical linear regression. Here, we present two applications in the contexts of binary and survival regression, where the Bayesian approach was applied to select markers prognostically relevant for the development of rheumatoid arthritis and for overall survival in acute myeloid leukemia patients. Copyright © 2012 John Wiley & Sons, Ltd.

We consider longitudinal studies with binary outcomes that are measured repeatedly on subjects over time. The goal of our analysis was to fit a logistic model that relates the expected value of the outcomes with explanatory variables that are measured on each subject. However, additional care must be taken to adjust for the association between the repeated measurements on each subject. We propose a new maximum likelihood method for covariates that may be fixed or time varying. We also implement and make comparisons with two other approaches: generalized estimating equations, which may be more robust to misspecification of the true correlation structure, and alternating logistic regression, which models association via odds ratios that are subject to less restrictive constraints than are correlations. The proposed estimation procedure will yield consistent and asymptotically normal estimates of the regression and correlation parameters if the correlation on consecutive measurements on a subject is correctly specified. Simulations demonstrate that our approach can yield improved efficiency in estimation of the regression parameter; for equally spaced and complete data, the gains in efficiency were greatest for the parameter associated with a time-by-group interaction term and for stronger values of the correlation. For unequally spaced data and with dropout according to a missing-at-random mechanism, MARK1ML with correctly specified consecutive correlations yielded substantial improvements in terms of both bias and efficiency. We present an analysis to demonstrate application of the methods we consider. We also offer an R function for easy implementation of our approach. Copyright © 2012 John Wiley & Sons, Ltd.

Bayesian Poisson log-linear multilevel models scalable to epidemiological studies are proposed to investigate population variability in sleep state transition rates. Hierarchical random effects are used to account for pairings of subjects and repeated measures within those subjects, as comparing diseased with non-diseased subjects while minimizing bias is of importance. Essentially, non-parametric piecewise constant hazards are estimated and smoothed, allowing for time-varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming exponentially distributed survival times. Such re-derivation allows synthesis of two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear generalized estimating equations (GEE) models for transition counts. An example data set from the Sleep Heart Health Study is analyzed. Supplementary material includes the analyzed data set as well as the code for a reproducible analysis. Copyright © 2012 John Wiley & Sons, Ltd.

This paper compares three different methods for performing cancer incidence prediction in an area without a cancer registry under a Bayesian framework, using linear and log-linear age-period models with either age-specific slopes or a common slope across age groups. The three methods assume that a nearby area with a cancer registration has similar incidence and mortality patterns as the area of interest without a cancer registry where the cancer incidence prediction is carried out. The three methods differ in modeling strategies: (i) modeling the incidence rate directly; (ii) modeling the ratio of the number of incident cases to that of mortality cases; and (iii) modeling the difference between the incidence rate and the mortality rate. Strategy (iii) is a new approach in this type of projection. Empirical assessment is made using real data from the cancer registry of Tarragona, Spain, to predict cancer incidence in Girona, Spain, and vice versa. Predictions of short-term (3–4 years) incidence were made for 2001 in Tarragona using observed cancer incidence and mortality data for 1994–1998 from Girona. Short-term predictions were made for 2002 in Girona using Tarragona's 1994–1998 data. Additionally, long-term (10 years) incidence rate predictions were made for 2002 in Girona using data from Tarragona for the period 1985–1992. Our results suggest that extrapolating time-trends of incidence rates minus mortality rates may have the best predictive performance overall. These methods of population-level disease vincidence prediction are highly relevant to health care planning and policy decisions. Copyright © 2012 John Wiley & Sons, Ltd.

Most new drug development in oncology is based on targeting specific molecules. Genomic profiles and deregulated drug targets vary from patient to patient making new treatments likely to benefit only a subset of patients traditionally grouped in the same clinical trials. Predictive biomarkers are being developed to identify patients who are most likely to benefit from a particular treatment; however, their biological basis is not always conclusive. The inclusion of marker-negative patients in a trial is therefore sometimes necessary for a more informative evaluation of the therapy. In this paper, we present a two-stage Bayesian design that includes both marker-positive and marker-negative patients in a clinical trial. We formulate a family of prior distributions that represent the degree of a priori confidence in the predictive biomarker. To avoid exposing patients to a treatment to which they may not be expected to benefit, we perform an interim analysis that may stop accrual of marker-negative patients or accrual of all patients. We demonstrate with simulations that the design and priors used control type I errors, give adequate power, and enable the early futility analysis of test-negative patients to be based on prior specification on the strength of evidence in the biomarker. Copyright © 2012 John Wiley & Sons, Ltd.

Traditional Chinese medicine (TCM) has an unique theory system and abundant preventative and therapeutic methods. In recent years, TCM has gradually become the hot topic of application and research around the world. This paper introduces how TCM uses the theory of yin-yang and five-phase to explain the mechanism of balancing the function of human body, the methods of diagnosing, abundant therapeutic methods and technologies, and treating principles, analyzes the features of TCM, and points out some of the problems waitiing to be solved in TCM scientific researches and statistical analysis. Copyright © 2012 John Wiley & Sons, Ltd.

In testing genome-wide gene expression quantitative trait loci, efficiency robust statistical methods and their computational convenience are most relevant. For this purpose, we propose to use a modified locally most powerful rank test for the analysis of case–control expression data. This modified rank test statistic is computationally simple, robust for non-normally distributed expression data, and asymptotically locally most powerful. It depends on the specification of a location distribution form for data but is not sensitive to misspecifications. When such a location distribution form cannot be specified, we apply Gastwirth's maximin efficiency robust rank test to gene expression data to maximize the worst Pitman asymptotic relative efficiency among a family of location distributions. We conduct simulation studies to assess their performance and use an application to real data for illustration. Copyright © 2011 John Wiley & Sons, Ltd.

This work focuses on the assessment of the discrimination ability of a binary marker to identify patients that will relapse in time. We consider the cumulative definition of sensitivity and dynamic definition of specificity at a time horizon, that is, the probability of a positive marker in the population that will relapse (cases) and that will not relapse (controls). In the presence of censoring, sensitivity and specificity cannot be estimated by proportions because it is not known whether censored subjects should be considered as cases or controls. The solutions proposed do not enable to obtain asymptotic confidence intervals. We explore the use of inverse probability of censoring weighting/imputation (borrowed from the methodology used to correct for verification bias) to adjust the classification matrix for the presence of censoring. The adjustment based on weights estimated conditional on the marker turned to be equivalent to the adjustment based on imputation. These approaches, which address for the presence of marker-dependent censoring, showed a better performance than the adjustment based on weights estimated on the entire sample, even in the case of marker-independent censoring. We derived single intervals and confidence region for sensitivity and 1-specificity using the delta method. The confidence region is particularly useful for a binary marker because the marker has some ability to discriminate among cases and controls only if the region does not intersect the first quadrant bisector. Copyright © 2011 John Wiley & Sons, Ltd.

Research into the diagnostic accuracy of clinical tests is often hampered by single or double partial verification mechanisms, that is, not all patients have their disease status verified by a reference test, neither do all patients receive all tests under evaluation (index tests). We show methods that reduce verification bias introduced when omitting data from partially tested patients. Adjustment techniques are well established when there are no missing index tests and when the reference test is ‘missing at random’. However, in practice, index tests tend to be omitted, and the choice of applying a reference test may depend on unobserved variables related to disease status, that is, verification may be missing not at random (MNAR). We study double partial verification in a clinical example from reproductive medicine in which we analyse the diagnostic values of the chlamydia antibody test and the hysterosalpingography in relation to a diagnostic laparoscopy. First, we plot all possible combinations of sensitivity and specificity of both index tests in two test ignorance regions. Then, we construct models in which we impose different assumptions for the verification process. We allow for missing index tests, study the influence of patient characteristics and study the accuracy estimates if an MNAR mechanism would operate. It is shown that data on tests used in the diagnostic process of the same population are preferably studied jointly and that the influence of an MNAR verification process was limited in a clinical study where more than half of the patients did not have the reference test. Copyright © 2011 John Wiley & Sons, Ltd.

The demand for unequal allocation in clinical trials is growing. Most commonly, the unequal allocation is achieved through permuted block randomization. However, other allocation procedures might be required to better approximate the allocation ratio in small samples, reduce the selection bias in open-label studies, or balance on baseline covariates. When these allocation procedures are generalized to unequal allocation, special care is to be taken to preserve the allocation ratio at every allocation step. This paper offers a way to expand the biased coin randomization to unequal allocation that preserves the allocation ratio at every allocation. The suggested expansion works with biased coin randomization that balances only on treatment group totals and with covariate-adaptive procedures that use a random biased coin element at every allocation. Balancing properties of the allocation ratio preserving biased coin randomization and minimization are described through simulations. It is demonstrated that these procedures are asymptotically protected against the shift in the rerandomization distribution identified for some examples of minimization with 1:2 allocation. The asymptotic shift in the rerandomization distribution of the difference in treatment means for an arbitrary unequal allocation procedure is explicitly derived in the paper. Copyright © 2011 John Wiley & Sons, Ltd.

We propose a method to combine several predictors (markers) that are measured repeatedly over time into a composite marker score without assuming a model and only requiring a mild condition on the predictor distribution. Assuming that the first and second moments of the predictors can be decomposed into a time and a marker component via a Kronecker product structure that accommodates the longitudinal nature of the predictors, we develop first-moment sufficient dimension reduction techniques to replace the original markers with linear transformations that contain sufficient information for the regression of the predictors on the outcome. These linear combinations can then be combined into a score that has better predictive performance than a score built under a general model that ignores the longitudinal structure of the data. Our methods can be applied to either continuous or categorical outcome measures. In simulations, we focus on binary outcomes and show that our method outperforms existing alternatives by using the AUC, the area under the receiver–operator characteristics (ROC) curve, as a summary measure of the discriminatory ability of a single continuous diagnostic marker for binary disease outcomes. Copyright © 2011 John Wiley & Sons, Ltd.

Traditional Chinese medicine (TCM) is a clinical-based discipline in which real-world clinical practice plays a significant role for both the development of clinical therapy and theoretical research. The large-scale clinical data generated during the daily clinical operations of TCM provide a highly valuable knowledge source for clinical decision making. Secondary analysis of these data would be a vital task for TCM clinical studies before the randomised controlled trials are conducted. In this article, we discuss the challenges and issues, such as structured data curation, data preprocessing and quality, large-scale data management and complex data analysis requirements, in the data processing and analysis of real-world TCM clinical data. Furthermore, we also discuss related state-of-the-art research and solutions in China. We have shown that the clinical data warehouse based on the collection of structured electronic medical record data and clinical terminology would be a promising approach for generating clinical hypotheses and helping the discovery of clinical knowledge from large-scale real-world TCM clinical data. Copyright © 2011 John Wiley & Sons, Ltd.

High-throughput technology in metabolomics, genomics, and proteomics gives rise to high dimension, low sample size data when the number of metabolites, genes, or proteins exceeds the sample size. For a limited class of designs, the classic ‘univariate approach’ for Gaussian repeated measures can provide a reasonable global hypothesis test. We derive new tests that not only accurately allow more variables than subjects, but also give valid analyses for data with complex between-subject and within-subject designs. Our derivations capitalize on the dual of the error covariance matrix, which is nonsingular when the number of variables exceeds the sample size, to ensure correct statistical inference and enhance computational efficiency. Simulation studies demonstrate that the new tests accurately control Type I error rate and have reasonable power even with a handful of subjects and a thousand outcome variables. We apply the new methods to the study of metabolic consequences of vitamin B6 deficiency. Free software implementing the new methods applies to a wide range of designs, including one group pre-intervention and post-intervention comparisons, multiple parallel group comparisons with one-way or factorial designs, and the adjustment and evaluation of covariate effects. Copyright © 2011 John Wiley & Sons, Ltd.

We propose a Bayesian framework for analyzing multivariate linear mixed effect models with linear constraints on the fixed effect parameters. The procedure can incorporate both firm and soft restrictions on the parameters and Bayesian model selection for the random effects. The framework is used to analyze data from the BioCycle study. One of the main objectives of the BioCycle study is to investigate the association between markers of oxidative stress and hormone levels during menstrual cycles of healthy women. Contrary to the popular belief that ovarian hormones are negatively associated with level of F ₂-isoprostanes, a known marker for oxidative stress, our analysis finds a positive association between ovarian hormone levels and isoprostane levels. The positive association corroborates the findings from a previous analysis of the BioCycle data. Copyright © 2011 John Wiley & Sons, Ltd.

Health-care providers in the UK and elsewhere are required to maintain records of incidents relating to patient safety, including the date and time of each incident. However, for reporting and analysis, the resulting data are typically grouped into discrete time intervals, for example, weekly or monthly counts. The grouping represents a potential loss of information for estimating variations in incidence over time. We use a Poisson point process model to quantify this loss of information. We also suggest some diagnostic procedures for checking the goodness of fit of the Poisson model. Finally, we apply the model to the data on hospital-acquired methicillin-resistant Staphylococcus aureus infections in two hospitals in the north of England. We find that, in one of the hospitals, the estimated incidence decreased by a factor of approximately 2.3 over a 7-year period from 0.323 to 0.097 cases per day per 1000 beds, whereas in the other, the estimated incidence showed only a small and nonsignificant decrease over the same period from 0.137 to 0.131. Copyright © 2011 John Wiley & Sons, Ltd.

Pharmacoepidemiology investigates associations between time-varying medication use/dose and risk of adverse events. Applied research typically relies on a priori chosen simple conventional models, such as current dose or any use in the past 3 months. However, different models imply different risk predictions, and only one model can be etiologically correct in any specific applications. We first formally defined several candidate models mapping the time vector of past drug doses (X (t), t  =  1, … ,u) into the value of a time-varying exposure metric M(u) at current time u. In addition to conventional one-parameter models, we considered two-parameter models accounting for recent dose increase or withdrawal and a flexible spline-based weighted cumulative exposure (WCE) model that defines M(u) as the weighted sum of past doses. In simulations, we generated event times assuming one of the models was correct and then analyzed the data with all candidate models. We demonstrated that the minimum AIC criterion is able to identify the correct model as the best-fitting model or one of the equivalent (within 4 AIC points of the minimum) models in a vast majority of simulated samples, especially with 500 or more events. We also showed how relying on an incorrect a priori chosen model may largely reduce the power to test for an association. Finally, we demonstrated how the flexible WCE estimates may help with model diagnostics even if the correct model is not WCE. We illustrated the practical advantages of AIC-based a posteriori model selection and WCE modeling in a real-life pharmacoepidemiology example. Copyright © 2011 John Wiley & Sons, Ltd.

Outcome-dependent sampling (ODS) study designs are commonly implemented with rare diseases or when prospective studies are infeasible. In longitudinal data settings, when a repeatedly measured binary response is rare, an ODS design can be highly efficient for maximizing statistical information subject to resource limitations that prohibit covariate ascertainment of all observations. This manuscript details an ODS design where individual observations are sampled with probabilities determined by an inexpensive, time-varying auxiliary variable that is related but is not equal to the response. With the goal of validly estimating marginal model parameters based on the resulting biased sample, we propose a semi-parametric, sequential offsetted logistic regressions (SOLR) approach. The SOLR strategy first estimates the relationship between the auxiliary variable and the response and covariate data by using an offsetted logistic regression analysis where the offset is used to adjust for the biased design. Results from the auxiliary variable model are then combined with the known or estimated sampling probabilities to formulate a second offset that is used to correct for the biased design in the ultimate target model relating the longitudinal binary response to covariates. Because the target model offset is estimated with SOLR, we detail asymptotic standard error estimates that account for uncertainty associated with the auxiliary variable model. Motivated by an analysis of the BioCycle Study (Gaskins et al., Effect of daily fiber intake on reproductive function: the BioCycle Study. American Journal of Clinical Nutrition 2009; 90(4): 1061–1069) that aims to describe the relationship between reproductive health (determined by luteinizing hormone levels) and fiber consumption, we examine properties of SOLR estimators and compare them with other common approaches. Copyright © 2011 John Wiley & Sons, Ltd.

The basic parameters in both survival analysis and more general multistate models, including the competing risks model and the illness–death model, are the transition hazards. It is often necessary to supplement the analysis of such models with other model parameters, which are all functionals of the transition hazards. Unfortunately, not all such functionals are equally meaningful in practical contexts, even though they may be mathematically well defined. We have found it useful to check whether the functionals satisfy three simple principles, which may be used as criteria for practical interpretability. Copyright © 2011 John Wiley & Sons, Ltd.

Evaluation of patient-reported outcomes (PRO) is increasingly performed in health sciences. PRO differs from other measurements because such patient characteristics cannot be directly observed. Item response theory (IRT) is an attractive way for PRO analysis. However, in the framework of IRT, sample size justification is rarely provided or ignores the fact that PRO measures are latent variables with the use of formulas developed for observed variables. It might therefore be inappropriate and might provide inadequately sized studies. The objective was to develop valid sample size methodology for the comparison of PRO in two groups of patients using IRT. The proposed approach takes into account questionnaire's items parameters, the difference of the latent variables means, and its variance whose derivation is approximated using Cramer–Rao bound (CRB). We also computed the associated power. We realized a simulation study taking into account sample size, number of items, and value of the group effect. We compared power obtained from CRB with the one obtained from simulations (SIM) and with the power based on observed variables (OBS). For a given sample size, powers using CRB and SIM were similar and always lower than OBS. We observed a strong impact of the number of items for CRB and SIM, the power increasing with the questionnaire's length but not for OBS. In the context of latent variables, it seems important to use an adapted sample size formula because the formula developed for observed variables seems to be inadequate and leads to an underestimated study size. Copyright © 2011 John Wiley & Sons, Ltd.

The spatial scan statistic has been widely used in spatial disease surveillance and spatial cluster detection for more than a decade. However, overdispersion often presents in real-world data, causing not only violation of the Poisson assumption but also excessive type I errors or false alarms. In order to account for overdispersion, we extend the Poisson-based spatial scan test to a quasi-Poisson-based test. The simulation shows that the proposed method can substantially reduce type I error probabilities in the presence of overdispersion. In a case study of infant mortality in Jiangxi, China, both tests detect a cluster; however, a secondary cluster is identified by only the Poisson-based test. It is recommended that a cluster detected by the Poisson-based scan test should be interpreted with caution when it is not confirmed by the quasi-Poisson-based test. Copyright © 2011 John Wiley & Sons, Ltd.

Methodology for sample size calculation for phase III clinical trials is well established and widely used. In contrast, for earlier phase clinical trials or pilot studies, although there is an acceptance that the methods used for phase III trials are not appropriate, there is little consensus over methods that should be used. This paper explores this problem from a Bayesian decision-theoretic perspective. The aim is to obtain sample sizes that would be appropriate for studies funded by a large funder such as a public sector body or major pharmaceutical company. The sample sizes obtained are optimal in that they minimise the average number of patients required per successfully identified effective therapy or equivalently maximise the number of effective therapies successfully identified over a long period.

The question of molecular heterogeneity and of tumoral phenotype in cancer remains unresolved. To understand the underlying molecular basis of this phenomenon, we analyzed genome-wide expression data of colon cancer metastasis samples, as these tumors are the most advanced and hence would be anticipated to be the most likely heterogeneous group of tumors, potentially exhibiting the maximum amount of genetic heterogeneity. Casting a statistical net around such a complex problem proves difficult because of the high dimensionality and multicollinearity of the gene expression space, combined with the fact that genes act in concert with one another and that not all genes surveyed might be involved. We devise a strategy to identify distinct subgroups of samples and determine the genetic/molecular signature that defines them. This involves use of the local sparse bump hunting algorithm, which provides a much more optimal and biologically faithful transformed space within which to search for bumps. In addition, thanks to the variable selection feature of the algorithm, we derived a novel sparse gene expression signature, which appears to divide all colon cancer patients into two populations: a population whose expression pattern can be molecularly encompassed within the bump and an outlier population that cannot be. Although all patients within any given stage of the disease, including the metastatic group, appear clinically homogeneous, our procedure revealed two subgroups in each stage with distinct genetic/molecular profiles. We also discuss implications of such a finding in terms of early detection, diagnosis and prognosis. Copyright © 2011 John Wiley & Sons, Ltd.

Prognostic models for survival outcomes are often developed by fitting standard survival regression models, such as the Cox proportional hazards model, to representative datasets. However, these models can be unreliable if the datasets contain few events, which may be the case if either the disease or the event of interest is rare. Specific problems include predictions that are too extreme, and poor discrimination between low-risk and high-risk patients. The objective of this paper is to evaluate three existing penalised methods that have been proposed to improve predictive accuracy. In particular, ridge, lasso and the garotte, which use penalised maximum likelihood to shrink coefficient estimates and in some cases omit predictors entirely, are assessed using simulated data derived from two clinical datasets. The predictions obtained using these methods are compared with those from Cox models fitted using standard maximum likelihood. The simulation results suggest that Cox models fitted using maximum likelihood can perform poorly when there are few events, and that significant improvements are possible by taking a penalised modelling approach. The ridge method generally performed the best, although lasso is recommended if variable selection is required. Copyright © 2011 John Wiley & Sons, Ltd.

In genetic association studies, a gene-set analysis can be more powerful than the separate analyses of multiple genetic variants and can offer unique insights into the genetic basis of many common human diseases. The goal of such an analysis is to study the joint effect of multiple single-nucleotide polymorphisms (SNPs) which belong to certain genes, and these genes are assumed to be involved in a common biological function. Currently, few approaches acknowledge the within-genes and between-genes correlations when testing for gene-set effects. Thus, here we propose a two-stage approach, which in the first stage uses a mixed-effects model with a general random-effects structure to capture the correlation between the SNPs and in the second stage tests for gene-set effects by using the empirical Bayes estimates of the random effects of the first stage as covariates in the model for the longitudinal phenotype. The advantage of this approach is its broad applicability because it can be used for any phenotypic outcome and any genetic model and can be implemented with standard statistical software. Copyright © 2011 John Wiley & Sons, Ltd.

Recent genome-wide association studies have reported the discoveries of genetic variants of small to moderate effects. However, most studies of complex diseases face a great challenge because the number of significant variants is less than what is required to explain the disease heritability. A new approach is needed to recognize all potential discoveries in the data. In this paper, we present a practical model-free procedure to estimate the number of true discoveries as a function of the number of top-ranking SNPs together with the confidence bounds. This approach allows a practical methodology of general utility and produces relevant statistical quantities with simple interpretation. Copyright © 2011 John Wiley & Sons, Ltd.

We develop a sensitivity analysis technique to assess the sensitivity of interaction analyses to unmeasured confounding. We give bias formulas for sensitivity analysis for interaction under unmeasured confounding on both additive and multiplicative scales. We provide simplified formulas in the case in which either one of the two factors does not interact with the unmeasured confounder in its effects on the outcome. An interesting consequence of the results is that if the two exposures of interest are independent (e.g., gene–environment independence), even under unmeasured confounding, if the estimate of the interaction is nonzero, then either there is a true interaction between the two factors or there is an interaction between one of the factors and the unmeasured confounder; an interaction must be present in either scenario. We apply the results to two examples drawn from the literature. Copyright © 2011 John Wiley & Sons, Ltd.

Pharmacokinetic (PK) studies aim to understand the kinetics of absorption, distribution, metabolism and elimination of a drug. Typically, such studies involve measuring the concentration of the drug in the plasma or blood at several time points after drug administration. In studying the PK behaviour, either the non-compartmental approach or alternatively a modelling approach can be utilized. Traditionally, the non-compartmental approach makes minimal assumptions about the data-generating process but requires the data to be collected in a very structured way. Conversely, the modelling approach depends heavily on assumptions about the data-generating process but does not impose a specific data structure. In this paper, we will discuss non-compartmental methods for estimating the area under the concentration versus time curve and other common PK parameters that use minimal assumptions about the data structure making it applicable to a wide range of PK studies. We will evaluate the methods using simulation and give an illustrative example. Copycenter © 2011 John Wiley & Sons, Ltd.

Bioequivalence or interaction trials are commonly studied in crossover design and can be analysed by nonlinear mixed effects models as an alternative to noncompartmental approach. We propose an extension of the population Fisher information matrix in nonlinear mixed effects models to design crossover pharmacokinetic trials, using a linearisation of the model around the random effect expectation, including within-subject variability and discrete covariates fixed or changing between periods. We use the expected standard errors of treatment effect to compute the power for the Wald test of comparison or equivalence and the number of subjects needed for a given power. We perform various simulations mimicking crossover two-period trials to show the relevance of these developments. We then apply these developments to design a crossover pharmacokinetic study of amoxicillin in piglets and implement them in the new version 3.2 of the r function PFIM. Copyright © 2011 John Wiley & Sons, Ltd.

The syndrome is one of the most important concepts and ingredients in the theoretical and clinical research of traditional Chinese medicine (TCM). TCM doctors believe that all diseases are caused by an imbalance in the patient's body, which is called syndrome. All the therapies and formulas in TCM are decided according to the patients' syndrome situation. To quantitatively evaluate the level of syndrome, many statistical methodologies have been discussed in recent years. In this article, we introduce a second-order latent variable model to evaluate the level of patients' syndrome with many clinical symptoms. An objective evaluation score can be easily derived by the proposed model, with a high speed of convergence and without joint-distribution assumption. We illustrate the application of this model by an analysis of premenstrual disorder syndrome of liver-qi invasion syndrome evaluation research. Copyright © 2011 John Wiley & Sons, Ltd.

Life expectancy has dramatically increased in industrialized nations over the last 200 hundred years. The aging of populations carries over to clinical research and leads to an increasing representation of elderly and multimorbid individuals in study populations. Clinical research in these populations is complicated by the fact that individuals are likely to experience several potential disease endpoints that prevent some disease-specific endpoint of interest from occurrence. Large developments in competing risks methodology have been achieved over the last decades, but we assume that recognition of competing risks in the clinical community is still marginal. It is the aim of this article to address translational aspects of competing risks to the clinical community. We describe clinical populations where competing risks issues may arise. We then discuss the importance of agreement between the competing risks methodology and the study aim, in particular the distinction between etiologic and prognostic research questions. In a review of 50 clinical studies performed in individuals susceptible to competing risks published in high-impact clinical journals, we found competing risks issues in 70% of all articles. Better recognition of issues related to competing risks and of statistical methods that deal with competing risks in accordance with the aim of the study is needed. Copyright © 2011 John Wiley & Sons, Ltd.

There is growing interest in pooling specimens across subjects in epidemiologic studies, especially those involving biomarkers. This paper is concerned with regression analysis of epidemiologic data where a binary exposure is subject to pooling and the pooled measurement is dichotomized to indicate either that no subjects in the pool are exposed or that some are exposed, without revealing further information about the exposed subjects in the latter case. The pooling process may be stratified on the disease status (a binary outcome) and possibly other variables but is otherwise assumed random. We propose methods for estimating parameters in a prospective logistic regression model and illustrate these with data from a population-based case-control study of colorectal cancer. Simulation results show that the proposed methods perform reasonably well in realistic settings and that pooling can lead to sizable gains in cost efficiency. We make recommendations with regard to the choice of design for pooled epidemiologic studies. Copyright © 2011 John Wiley & Sons, Ltd.

In the diagnostic area, the usual setting considers two populations: nondiseased and diseased. The use of the standard ROC analysis methodology is well established. Sometimes, however, diagnostic problems inherently include more than two classification states. For example, ‘yes, uncertain, no’ or ‘low, normal, high’. Here we consider a three-normal distribution setting and derive estimators for the optimum thresholds between states based on a cost function. These estimators can be extended for clinical contexts with more than three states. This approach is well known for the two-state setting and its advantage lies in the fact that it accounts for the specific context's properties, such as disease prevalence and classification costs. Here we calculated the variance of the estimators by the use of parametric methods on nonlinear equations and we constructed confidence intervals accounting for possible uncertainty in the threshold estimation. We conducted a simulation study to assess the performance of these estimators and the confidence intervals. Comparisons with the naive threshold estimation method of joining the distributions two-by-two and applying standard ROC techniques proved that the latter method is not reliable for all parameter combinations and should be avoided. Copyright © 2011 John Wiley & Sons, Ltd.

International regulatory guidelines require that a ‘qualified statistician’ takes responsibility for the statistical aspects of a clinical trial used for drug licensing. No consensus on what constitutes a ‘qualified statistician’ appears to have been developed so far.

Measurement error (ME) problems can cause bias or inconsistency of statistical inferences. When investigators are unable to obtain correct measurements of biological assays, special techniques to quantify MEs need to be applied. Sampling based on repeated measurements is a common strategy to allow for ME. This method has been well addressed in the literature under parametric assumptions. The approach with repeated measures data may not be applicable when the replications are complicated because of cost and/or time concerns. Pooling designs have been proposed as cost-efficient sampling procedures that can assist to provide correct statistical operations based on data subject to ME. We demonstrate that a mixture of both pooled and unpooled data (a hybrid pooled–unpooled design) can support very efficient estimation and testing in the presence of ME. Nonparametric techniques have not been well investigated to analyze repeated measures data or pooled data subject to ME. We propose and examine both the parametric and empirical likelihood methodologies for data subject to ME. We conclude that the likelihood methods based on the hybrid samples are very efficient and powerful. The results of an extensive Monte Carlo study support our conclusions. Real data examples demonstrate the efficiency of the proposed methods in practice. Copyright © 2011 John Wiley & Sons, Ltd.

In evaluating discriminatory performance of a new modality in a screening setting, a logistical constraint is that the prevalence of the disease of interest is typically very low. This implies that under a standard study design large numbers of subjects have to be evaluated using the new modality. However, if a predicate modality exists in clinical practice, one can base inclusion into the study of the new modality on the clinical results from the predicate to ‘enrich’ the population of diseased subjects in the study. If this enrichment is not accounted for when estimating sensitivity, specificity, and area under the ROC curve, these ‘naive’ estimates may be substantially biased compared with expected performance in the intended use population. We derive expressions for the magnitude of this bias in terms of correlations of modality scores. When such estimates are ‘corrected’ for the sampling weights using inverse probability weighting, the variances of the estimates of the above quantities are affected. We derive here analytic expressions for these variances. For a fixed number of diseased subjects, differential sampling increases the variance of the (corrected) estimates, all other things being equal. However, differential sampling also increases the number with disease for fixed total study size, which decreases the variance of the sensitivity and area under the ROC curve estimates, all other things being equal. The balance of these two effects determines the gain in efficiency when using enrichment and corrected estimates. These principles are illustrated with a simulation study motivated by the Digital Mammographic Imaging Screening Trial study, a trial of digital versus screen film mammography. Copyright © 2011 John Wiley & Sons, Ltd.

In this study, we developed a novel adaptive dose-finding approach for inclusion of correlated bivariate binary and continuous outcomes in designing phase I oncology trials. For this approach, binary toxicity and continuous efficacy outcomes are modeled jointly with a factorization model. The basic strategy of the proposed approach is based primarily on the Bayesian method. We based the dose escalation/de-escalation decision rules on the posterior distributions of both toxicity and efficacy outcomes. We compared the operating characteristics of the proposed and existing methods through simulation studies under various scenarios. We found that the recommendation rate of the true recommended dose (RD) in the proposed method was more favorable than that in the existing method when the true RD was relatively at the tail end among the tested doses. It was similar to that of the existing method when the true RD was relatively at the top end. Copyright © 2011 John Wiley & Sons, Ltd.

A problem occurring in chronic disease modeling is the estimation of transition probabilities of moving from one state of a categorical risk factor to another. Transitions could be obtained from a cohort study, but often such data may not be available. However, under the assumption that transitions remain stable over time, age specific cross-sectional prevalence data could be used instead. Problems that then arise are parameter identifiability and the fact that age dependent cross-sectional data are often noisy or are given in age intervals. In this paper we propose a method to estimate so-called net annual transition probabilities from cross-sectional data, including their uncertainties. Net transitions only describe the net inflow or outflow into a certain risk factor state at a certain age. Our approach consists of two steps: first, smooth the data using multinomial P-splines, second, from these data estimate net transition probabilities. This second step can be formulated as a transportation problem, which is solved using the simplex algorithm from linear programming theory. A sensible specification of the cost matrix is crucial to get meaningful results. Uncertainties are assessed by parametric bootstrapping. We illustrate our method using data on body mass index. We conclude that this method provides a flexible way of estimating net transitions and that the use of net transitions has implications for model dynamics, for example when modeling interventions. Copyright © 2011 John Wiley & Sons, Ltd.

This paper aims to develop a mixture model to study heterogeneous longitudinal data on the treatment effect of heroin use from a California Civil Addict Program. Each component of the mixture is characterized by a varying coefficient mixed effect model. We use the Bayesian P-splines approach to approximate the varying coefficient functions. We develop Markov chain Monte Carlo algorithms to estimate the smooth functions, unknown parameters, and latent variables in the model. We use modified deviance information criterion to determine the number of components in the mixture. A simulation study demonstrates that the modified deviance information criterion selects the correct number of components and the estimation of unknown quantities is accurate. We apply the proposed model to the heroin treatment study. Furthermore, we identify heterogeneous longitudinal patterns. Copyright © 2011 John Wiley & Sons, Ltd.

The US lung allocation policy estimates each individual's urgency and transplant benefit in defining a lung allocation score (LAS). Transplant benefit, as defined by the Organ Procurement and Transplantation Network Thoracic Committee, is the days of life gained over the following year if transplanted versus not transplanted. Urgency is measured by days of life during the next year without transplant. In both definitions, accurate estimation of wait list days lived, or a wait list restricted mean lifetime, is required. Risk factors are available to estimate patient urgency when listed, with more urgent patients removed from the wait list upon death or transplant. As a patient progresses, priority for transplant (censoring) changes accordingly. Therefore, it is crucial to adjust for dependent censoring in modeling days of life. We develop a model for the restricted mean as a function of covariates, by using pseudo-observations that account for dependent censoring linked to a series of longitudinal measures (LAS). Simulation results show that our method performs well in situations comparable with the LAS setting. Applying wait list and post-transplant model results that account for dependent censoring to wait list patients, we obtain estimates of transplant benefit that are larger for many of the more urgent patients in need of transplant. The difference in LAS for an individual, when properly accounting for dependent censoring, has high impact on the priority and timing of an organ offer for these patients. Copyright © 2012 John Wiley & Sons, Ltd.

Difference-in-differences with matching is a popular method to measure the impact of an intervention in health and social sciences. This method requires baseline data, that is, data before interventions, which are not always available in reality. Instead, panel data with two time periods are often collected after interventions begin. In this paper, a simple matching method is proposed to measure the impact of an intervention using two-period panel data after the intervention. The method is illustrated by the measurement of the effect of health insurance in Vietnam using household panel data. Copyright © 2011 John Wiley & Sons, Ltd.

The assessment of the dose–response relationship is important but not straightforward when the therapeutic agent is administered repeatedly with dose-modification in each patient and a continuous response is measured repeatedly. We recently proposed an autoregressive linear mixed effects model for such data in which the current response is regressed on the previous response, fixed effects, and random effects. The model represents profiles approaching each patient's asymptote, takes into account the past dose history, and provides a dose–response relationship of the asymptote as a summary measure. In an autoregressive model, intermittent missing data mean the missing values in previous responses as covariates. We previously provided the marginal (unconditional on the previous response) form of the proposed model to deal with intermittent missing data. Irregular timings of dose-modification or measurement can also be treated as equally spaced data with intermittent missing values by selecting an adequately small unit of time. The likelihood is, however, expressed by matrices whose sizes depend on the number of observations for a patient, and the computational burden is large. In this study, we propose a state space form of the autoregressive linear mixed effects model to calculate the marginal likelihood without using large matrices. The regression coefficients of the fixed effects can be concentrated out of the likelihood in this model by the same way of a linear mixed effects model. As an illustration of the approach, we analyzed immunologic data from a clinical trial for multiple sclerosis patients and estimated the dose–response curves for each patient and the population mean. Copyright © 2011 John Wiley & Sons, Ltd.