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A statistical model of breast cancer tumour growth with estimation of screening sensitivity as a function of mammographic density
We describe a new approach for estimating breast cancer tumour growth which builds on recently described continuous tumour growth models and estimates mammographic screening sensitivity as a function of tumour size and mammographic density. (Source: Statistical Methods in Medical Research)
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Abrahamsson, L., Humphreys, K. Tags: Regular Articles Source Type: research

Statistical methods for multivariate meta-analysis of diagnostic tests: An overview and tutorial
In this article, we present an overview and tutorial of statistical methods for meta-analysis of diagnostic tests under two scenarios: (1) when the reference test can be considered a gold standard and (2) when the reference test cannot be considered a gold standard. In the first scenario, we first review the conventional summary receiver operating characteristics approach and a bivariate approach using linear mixed models. Both approaches require direct calculations of study-specific sensitivities and specificities. We next discuss the hierarchical summary receiver operating characteristics curve approach for jointly model...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Ma, X., Nie, L., Cole, S. R., Chu, H. Tags: Regular Articles Source Type: research

Addressing missing covariates for the regression analysis of competing risks: Prognostic modelling for triaging patients diagnosed with prostate cancer
Competing risks arise in medical research when subjects are exposed to various types or causes of death. Data from large cohort studies usually exhibit subsets of regressors that are missing for some study subjects. Furthermore, such studies often give rise to censored data. In this article, a carefully formulated likelihood-based technique for the regression analysis of right-censored competing risks data when two of the covariates are discrete and partially missing is developed. The approach envisaged here comprises two models: one describes the covariate effects on both long-term incidence and conditional latencies for ...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Escarela, G., Ruiz-de-Chavez, J., Castillo-Morales, A. Tags: Regular Articles Source Type: research

On analyzing ordinal data when responses and covariates are both missing at random
In many occasions, particularly in biomedical studies, data are unavailable for some responses and covariates. This leads to biased inference in the analysis when a substantial proportion of responses or a covariate or both are missing. Except a few situations, methods for missing data have earlier been considered either for missing response or for missing covariates, but comparatively little attention has been directed to account for both missing responses and missing covariates, which is partly attributable to complexity in modeling and computation. This seems to be important as the precise impact of substantial missing ...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Rana, S., Roy, S., Das, K. Tags: Regular Articles Source Type: research

Expectation maximization-based likelihood inference for flexible cure rate models with Weibull lifetimes
Recently, a flexible cure rate survival model has been developed by assuming the number of competing causes of the event of interest to follow the Conway–Maxwell–Poisson distribution. This model includes some of the well-known cure rate models discussed in the literature as special cases. Data obtained from cancer clinical trials are often right censored and expectation maximization algorithm can be used in this case to efficiently estimate the model parameters based on right censored data. In this paper, we consider the competing cause scenario and assuming the time-to-event to follow the Weibull distribution,...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Balakrishnan, N., Pal, S. Tags: Regular Articles Source Type: research

MCAR is not necessary for the complete cases to constitute a simple random subsample of the target sample
Missing data is the norm rather than the exception in complex epidemiological studies. Complete-case analyses, which discard all subjects with some data values missing, are known to be valid under the very restrictive assumption that the response mechanism is missing completely at random (MCAR). While conditions weaker than MCAR are known under which estimators of regression coefficients are unbiased, one often comes across the view in the literature that MCAR is necessary for the complete cases to form a simple random subsample of the target sample. In this paper, we explain why this is not the case, and we distill an ass...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Galati, J. C., Seaton, K. A. Tags: Regular Articles Source Type: research

Joint modeling of longitudinal data and discrete-time survival outcome
A predictive joint shared parameter model is proposed for discrete time-to-event and longitudinal data. A discrete survival model with frailty and a generalized linear mixed model for the longitudinal data are joined to predict the probability of events. This joint model focuses on predicting discrete time-to-event outcome, taking advantage of repeated measurements. We show that the probability of an event in a time window can be more precisely predicted by incorporating the longitudinal measurements. The model was investigated by comparison with a two-step model and a discrete-time survival model. Results from both a stud...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Qiu, F., Stein, C. M., Elston, R. C., for the Tuberculosis Research Unit (TBRU) Tags: Regular Articles Source Type: research

Multiple-stage sampling procedure for covariate-adjusted response-adaptive designs
Covariate-adjusted response-adaptive (CARA) design becomes an important statistical tool for evaluating and comparing the performance of treatments when targeted medicine and adaptive therapy become important medical innovations. Due to the nature of the adaptive therapies of interest and how subjects accrue to a sampling procedure, it is of interest how to control the sample size sequentially such that the estimates of treatment effects have satisfactory precision in addition to its asymptotic properties. In this paper, we apply a multiple-stage sequential sampling method to CARA design in such a way that the control of t...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Park, E., Chang, Y.-c. I. Tags: Regular Articles Source Type: research

Sensitivity analysis of incomplete longitudinal data departing from the missing at random assumption: Methodology and application in a clinical trial with drop-outs
Statistical analyses of longitudinal data with drop-outs based on direct likelihood, and using all the available data, provide unbiased and fully efficient estimates under some assumptions about the drop-out mechanism. Unfortunately, these assumptions can never be tested from the data. Thus, sensitivity analyses should be routinely performed to assess the robustness of inferences to departures from these assumptions. However, each specific scientific context requires different considerations when setting up such an analysis, no standard method exists and this is still an active area of research. We propose a flexible proce...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Moreno-Betancur, M., Chavance, M. Tags: Regular Articles Source Type: research

Predictions in an illness-death model
Multi-state models allow subjects to move among a finite number of states during a follow-up period. Most often, the objects of study are the transition intensities. The impact of covariates on them can also be studied by specifying regression models. Thus, estimation in multi-state models is usually focused on the transition intensities (or the cumulative transition intensities) and on the regression parameters. However, from a clinical or epidemiological point of view, other quantities could provide additional information and may be more relevant to answer practical questions. For example, given a set of covariates for a...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Touraine, C., Helmer, C., Joly, P. Tags: Regular Articles Source Type: research

A systematic selection method for the development of cancer staging systems
The tumor–node–metastasis (TNM) staging system has been the anchor of cancer diagnosis, treatment, and prognosis for many years. For meaningful clinical use, an orderly, progressive condensation of the T and N categories into an overall staging system needs to be defined, usually with respect to a time-to-event outcome. This can be considered as a cutpoint selection problem for a censored response partitioned with respect to two ordered categorical covariates and their interaction. The aim is to select the best grouping of the TN categories. A novel bootstrap cutpoint/model selection method is proposed for this...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Lin, Y., Chappell, R., Gönen, M. Tags: Regular Articles Source Type: research

Detection of spatial variations in temporal trends with a quadratic function
Methods for the assessment of spatial variations in temporal trends (SVTT) are important tools for disease surveillance, which can help governments to formulate programs to prevent diseases, and measure the progress, impact, and efficacy of preventive efforts already in operation. The linear SVTT method is designed to detect areas with unusual different disease linear trends. In some situations, however, its estimation trend procedure can lead to wrong conclusions. In this article, the quadratic SVTT method is proposed as alternative of the linear SVTT method. The quadratic method provides better estimates of the real tren...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Moraga, P., Kulldorff, M. Tags: Regular Articles Source Type: research

Estimation of regression quantiles in complex surveys with data missing at random: An application to birthweight determinants
The estimation of population parameters using complex survey data requires careful statistical modelling to account for the design features. This is further complicated by unit and item nonresponse for which a number of methods have been developed in order to reduce estimation bias. In this paper, we address some issues that arise when the target of the inference (i.e. the analysis model or model of interest) is the conditional quantile of a continuous outcome. Survey design variables are duly included in the analysis and a bootstrap variance estimation approach is proposed. Missing data are multiply imputed by means of ch...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Geraci, M. Tags: Regular Articles Source Type: research

Projecting adverse event incidence rates using empirical Bayes methodology
Although there is considerable interest in adverse events observed in clinical trials, projecting adverse event incidence rates in an extended period can be of interest when the trial duration is limited compared to clinical practice. A naïve method for making projections might involve modeling the observed rates into the future for each adverse event. However, such an approach overlooks the information that can be borrowed across all the adverse event data. We propose a method that weights each projection using a shrinkage factor; the adverse event-specific shrinkage is a probability, based on empirical Bayes meth...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Ma, G., Ganju, J., Huang, J. Tags: Regular Articles Source Type: research

Linear combination methods to improve diagnostic/prognostic accuracy on future observations
Multiple diagnostic tests or biomarkers can be combined to improve diagnostic accuracy. The problem of finding the optimal linear combinations of biomarkers to maximise the area under the receiver operating characteristic curve has been extensively addressed in the literature. The purpose of this article is threefold: (1) to provide an extensive review of the existing methods for biomarker combination; (2) to propose a new combination method, namely, the nonparametric stepwise approach; (3) to use leave-one-pair-out cross-validation method, instead of re-substitution method, which is overoptimistic and hence might lead to ...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Kang, L., Liu, A., Tian, L. Tags: Regular Articles Source Type: research