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Robust inference for mixed censored and binary response models with missing covariates
In biomedical and epidemiological studies, often outcomes obtained are of mixed discrete and continuous in nature. Furthermore, due to some technical inconvenience or else, continuous responses are censored and also a few covariates cease to be observed completely. In this paper, we develop a model to tackle these complex situations. Our methodology is developed in a more general framework and provides a full-scale robust analysis of such complex models. The proposed robust maximum likelihood estimators of the model parameters are resistant to potential outliers in the data. We discuss the asymptotic properties of the robu...
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Sarkar, A., Das, K., Sinha, S. K. Tags: Articles Source Type: research

Power considerations for trials of two experimental arms versus a standard active control or placebo
The power of the two-experimental arm trial depends on three choices: (1) when one arm is dropped (if at all); (2) the final testing procedure, assuming no dropping; and (3) the sampling ratio for the three arms. Multiple-arm designs require critical values which were calculated using Mathematica. Power calculations were exact based on probabilities from binomial distributions. The "drop the loser" strategy is optimal for the primary endpoint. The equal sized two treated arm trial gives reasonable power for the primary as well as good power to select the best treated arm. The best power was provided by the 3:3:4 sampling, ...
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Hasselblad, V. Tags: Articles Source Type: research

A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies
Background Recent literature on the comparison of machine learning methods has raised questions about the neutrality, unbiasedness and utility of many comparative studies. Reporting of results on favourable datasets and sampling error in the estimated performance measures based on single samples are thought to be the major sources of bias in such comparisons. Better performance in one or a few instances does not necessarily imply so on an average or on a population level and simulation studies may be a better alternative for objectively comparing the performances of machine learning algorithms. Methods We compare the clas...
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Khondoker, M., Dobson, R., Skirrow, C., Simmons, A., Stahl, D. Tags: Articles Source Type: research

Estimation of half-life periods in nonlinear data with fractional polynomials
Regression models are frequently used to model the functional relationship between an interesting outcome parameter and one or more potentially relevant explanatory variables. Objectives can be to set up as a prognostic model, for example, or an estimation model for a certain parameter of interest. Determining half-life periods can be viewed as a particular application of such an estimation model. However, specific to these modelling problems is that time-dependent active agent concentrations can be nonlinear. Concurrently, a major limitation to common regression approaches is the assumed linear relation of the investigate...
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Mayer, B., Keller, F., Syrovets, T., Wittau, M. Tags: Articles Source Type: research

Measuring and estimating treatment effect on dichotomous outcome of a population
In different studies for treatment effect on dichotomous outcome of a certain population, one uses different regression models, leading to different measures of the treatment effect. In observational studies, the common measures of the treatment effect are: the conditional risk difference based on a linear model, the conditional risk ratio based on a log-linear model, and the conditional odds ratio based on a logistic model; in randomized trials, the common measures are: the marginal risk difference based on a linear model, the marginal risk ratio based on a log-linear model, and the marginal odds ratio based on a logistic...
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Wang, X., Jin, Y., Yin, L. Tags: Articles Source Type: research

MOVER-R confidence intervals for ratios and products of two independently estimated quantities
(Source: Statistical Methods in Medical Research)
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Newcombe, R. G. Tags: Note Source Type: research

A Bayesian network meta-analysis for binary outcome: how to do it
This study presents an overview of conceptual and practical issues of a network meta-analysis (NMA), particularly focusing on its application to randomised controlled trials with a binary outcome of interest. We start from general considerations on NMA to specifically appraise how to collect study data, structure the analytical network and specify the requirements for different models and parameter interpretations, with the ultimate goal of providing physicians and clinician-investigators a practical tool to understand pros and cons of NMA. Specifically, we outline the key steps, from the literature search to sensitivity a...
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Greco, T., Landoni, G., Biondi-Zoccai, G., D'Ascenzo, F., Zangrillo, A. Tags: Article Source Type: research

Variable selection in semi-parametric models
We propose Bayesian variable selection methods in semi-parametric models in the framework of partially linear Gaussian and problit regressions. Reproducing kernels are utilized to evaluate possibly non-linear joint effect of a set of variables. Indicator variables are introduced into the reproducing kernels for the inclusion or exclusion of a variable. Different scenarios based on posterior probabilities of including a variable are proposed to select important variables. Simulations are used to demonstrate and evaluate the methods. It was found that the proposed methods can efficiently select the correct variables regardle...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Zhang, H., Maity, A., Arshad, H., Holloway, J., Karmaus, W. Tags: Regular Articles Source Type: research

A flexible semiparametric modeling approach for doubly censored data with an application to prostate cancer
Doubly censored data often arise in medical studies of disease progression involving two related events for which both an originating and a terminating event are interval-censored. Although regression modeling for such doubly censored data may be complicated, we propose a simple semiparametric regression modeling strategy based on jackknife pseudo-observations obtained using nonparametric estimators of the survival function. Inference is carried out via generalized estimating equations. Simulations studies show that the proposed method produces virtually unbiased covariate effect estimates, even for moderate sample sizes. ...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Han, S., Andrei, A.-C., Tsui, K.-W. Tags: Regular Articles Source Type: research

Binomial confidence intervals for testing non-inferiority or superiority: a practitioners dilemma
In testing for non-inferiority or superiority in a single arm study, the confidence interval of a single binomial proportion is frequently used. A number of such intervals are proposed in the literature and implemented in standard software packages. Unfortunately, use of different intervals leads to conflicting conclusions. Practitioners thus face a serious dilemma in deciding which one to depend on. Is there a way to resolve this dilemma? We address this question by investigating the performances of ten commonly used intervals of a single binomial proportion, in the light of two criteria, viz., coverage and expected lengt...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Pradhan, V., Evans, J. C., Banerjee, T. Tags: Regular Articles Source Type: research

Assessing calibration of prognostic risk scores
We describe a model-based framework for the assessment of calibration in the binary setting that provides natural extensions to the survival data setting. We show that Poisson regression models can be used to easily assess calibration in prognostic models. In addition, we show that a calibration test suggested for use in survival data has poor performance. Finally, we apply these methods to the problem of external validation of a risk score developed for the general population when assessed in a special patient population (i.e. patients with particular comorbidities, such as rheumatoid arthritis). (Source: Statistical Meth...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Crowson, C. S., Atkinson, E. J., Therneau, T. M. Tags: Regular Articles Source Type: research

Advanced colorectal neoplasia risk stratification by penalized logistic regression
Colorectal cancer is the second leading cause of death from cancer in the United States. To facilitate the efficiency of colorectal cancer screening, there is a need to stratify risk for colorectal cancer among the 90% of US residents who are considered "average risk." In this article, we investigate such risk stratification rules for advanced colorectal neoplasia (colorectal cancer and advanced, precancerous polyps). We use a recently completed large cohort study of subjects who underwent a first screening colonoscopy. Logistic regression models have been used in the literature to estimate the risk of advanced colorectal ...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Lin, Y., Yu, M., Wang, S., Chappell, R., Imperiale, T. F. Tags: Regular Articles Source Type: research

A flexible joint modeling framework for longitudinal and time-to-event data with overdispersion
We combine conjugate and normal random effects in a joint model for outcomes, at least one of which is non-Gaussian, with particular emphasis on cases in which one of the outcomes is of survival type. Conjugate random effects are used to relax the often-restrictive mean-variance prescription in the non-Gaussian outcome, while normal random effects account for not only the correlation induced by repeated measurements from the same subject but also the association between the different outcomes. Using a case study in chronic heart failure, we show that model fit can be improved, even resulting in impact on significance tests...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Njagi, E. N., Molenberghs, G., Rizopoulos, D., Verbeke, G., Kenward, M. G., Dendale, P., Willekens, K. Tags: Regular Articles Source Type: research

Risk prediction for myocardial infarction via generalized functional regression models
In this paper, we propose a generalized functional linear regression model for a binary outcome indicating the presence/absence of a cardiac disease with multivariate functional data among the relevant predictors. In particular, the motivating aim is the analysis of electrocardiographic traces of patients whose pre-hospital electrocardiogram (ECG) has been sent to 118 Dispatch Center of Milan (the Italian free-toll number for emergencies) by life support personnel of the basic rescue units. The statistical analysis starts with a preprocessing of ECGs treated as multivariate functional data. The signals are reconstructed fr...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Ieva, F., Paganoni, A. M. Tags: Regular Articles Source Type: research

Point success rate for patient therapeutic response prediction by continuous biomarker scores
Various predictive diagnostic tests are highly demanded to guide optimal treatments for individual patients, as individual patients with the same disease such as cancer frequently exhibit dramatically different therapeutic responses to multiple available treatment options. A large number of clinical trials have thus been performed to test the predictive ability and utility of various therapeutic biomarker tests. However, in these trial designs the conventional optimization criteria such as positive predictive value or negative predictive value cannot reflect each patient’s true chance of success associated with conti...
Source: Statistical Methods in Medical Research - August 25, 2016 Category: Statistics Authors: Ma, Z., Kim, Y., Hu, F., Lee, J. K. Tags: Regular Articles Source Type: research