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A comparison of two methods of estimating propensity scores after multiple imputation
In many observational studies, analysts estimate treatment effects using propensity scores, e.g. by matching or sub-classifying on the scores. When some values of the covariates are missing, analysts can use multiple imputation to fill in the missing data, estimate propensity scores based on the m completed datasets, and use the propensity scores to estimate treatment effects. We compare two approaches to implement this process. In the first, the analyst estimates the treatment effect using propensity score matching within each completed data set, and averages the m treatment effect estimates. In the second approach, the a...
Source: Statistical Methods in Medical Research - February 16, 2016 Category: Statistics Authors: Mitra, R., Reiter, J. P. Tags: Articles Source Type: research

A transformation class for spatio-temporal survival data with a cure fraction
We propose a hierarchical Bayesian methodology to model spatially or spatio-temporal clustered survival data with possibility of cure. A flexible continuous transformation class of survival curves indexed by a single parameter is used. This transformation model is a larger class of models containing two special cases of the well-known existing models: the proportional hazard and the proportional odds models. The survival curve is modeled as a function of a baseline cumulative distribution function, cure rates, and spatio-temporal frailties. The cure rates are modeled through a covariate link specification and the spatial f...
Source: Statistical Methods in Medical Research - February 16, 2016 Category: Statistics Authors: Hurtado Rua, S. M., Dey, D. K. Tags: Articles Source Type: research

A comparative investigation of methods for longitudinal data with limits of detection through a case study
In conclusion, the antiretroviral treatment was associated with a significant decrease in viral load after controlling the effects of other covariates. A simulation study with finite sample size shows MCEM is the least biased method and the estimates are least sensitive to the censoring mechanism. (Source: Statistical Methods in Medical Research)
Source: Statistical Methods in Medical Research - February 16, 2016 Category: Statistics Authors: Fu, P., Hughes, J., Zeng, G., Hanook, S., Orem, J., Mwanda, O., Remick, S. Tags: Articles Source Type: research

Analyzing repeated measures semi-continuous data, with application to an alcohol dependence study
Two-part random effects models (Olsen and Schafer,1 Tooze et al.2) have been applied to repeated measures of semi-continuous data, characterized by a mixture of a substantial proportion of zero values and a skewed distribution of positive values. In the original formulation of this model, the natural logarithm of the positive values is assumed to follow a normal distribution with a constant variance parameter. In this article, we review and consider three extensions of this model, allowing the positive values to follow (a) a generalized gamma distribution, (b) a log-skew-normal distribution, and (c) a normal distribution a...
Source: Statistical Methods in Medical Research - February 16, 2016 Category: Statistics Authors: Liu, L., Strawderman, R. L., Johnson, B. A., O'Quigley, J. M. Tags: Articles Source Type: research

A new statistical decision rule for single-arm phase II oncology trials
Most single-arm phase II clinical trials compare the efficacy of a new treatment with historical controls through statistical hypothesis testing. One major problem with such a comparison is that the efficacy of the historical control is treated as a known constant, whereas in reality, it is never precisely known. This partially explains why many "Go" decisions made in single-arm phase II trials are shown to be incorrect in phase III trials. In this paper, we propose a new decision rule for an improved transitional decision for single-arm phase II oncology clinical trials with binary endpoints. This new decision rule is joi...
Source: Statistical Methods in Medical Research - February 16, 2016 Category: Statistics Authors: Chen, Y., Chen, Z., Mori, M. Tags: Articles Source Type: research

Binomial regression with a misclassified covariate and outcome
We present a novel Bayesian approach in binomial regression when both the outcome variable and one binary covariate are subject to misclassification. Extensive simulation results under various scenarios and a real clinical example are given to illustrate the proposed approach. This approach is motivated and applied to a dataset from the Baylor Alzheimer's Disease and Memory Disorders Center. (Source: Statistical Methods in Medical Research)
Source: Statistical Methods in Medical Research - February 16, 2016 Category: Statistics Authors: Luo, S., Chan, W., Detry, M. A., Massman, P. J., Doody, R. S. Tags: Articles Source Type: research

Response-adaptive designs for continuous treatment responses in phase III clinical trials: A review
A variety of response-adaptive randomization procedures have been proposed in literature assuming binary outcomes. However, the list is not so long for continuous outcomes though many real clinical trials deal with continuous treatment responses. In this paper, we attempt to explore the available procedures together with a comparison of their performances. Some real-life adaptive trial is also reviewed. (Source: Statistical Methods in Medical Research)
Source: Statistical Methods in Medical Research - February 16, 2016 Category: Statistics Authors: Biswas, A., Bhattacharya, R. Tags: Articles Source Type: research

Group sequential control of overall toxicity incidents in clinical trials - non-Bayesian and Bayesian approaches
In some small clinical trials, toxicity is not a primary endpoint; however, it often has dire effects on patients’ quality of life and is even life-threatening. For such clinical trials, rigorous control of the overall incidence of adverse events is desirable, while simultaneously collecting safety information. In this article, we propose group sequential toxicity monitoring strategies to control overall toxicity incidents below a certain level as opposed to performing hypothesis testing, which can be incorporated into an existing study design based on the primary endpoint. We consider two sequential methods: a non-B...
Source: Statistical Methods in Medical Research - February 16, 2016 Category: Statistics Authors: Yu, J., Hutson, A. D., Siddiqui, A. H., Kedron, M. A. Tags: Articles Source Type: research

Sample size determination for disease prevalence studies with partially validated data
Disease prevalence is an important topic in medical research, and its study is based on data that are obtained by classifying subjects according to whether a disease has been contracted. Classification can be conducted with high-cost gold standard tests or low-cost screening tests, but the latter are subject to the misclassification of subjects. As a compromise between the two, many research studies use partially validated datasets in which all data points are classified by fallible tests, and some of the data points are validated in the sense that they are also classified by the completely accurate gold-standard test. In ...
Source: Statistical Methods in Medical Research - February 16, 2016 Category: Statistics Authors: Qiu, S.-F., Poon, W.-Y., Tang, M.-L. Tags: Articles Source Type: research

Modeling fecundity in the presence of a sterile fraction using a semi-parametric transformation model for grouped survival data
The analysis of fecundity data is challenging and requires consideration of both highly timed and interrelated biologic processes in the context of essential behaviors such as sexual intercourse during the fertile window. Understanding human fecundity is further complicated by presence of a sterile population, i.e. couples unable to achieve pregnancy. Modeling techniques conducted to date have largely relied upon discrete time-to-pregnancy survival or day-specific probability models to estimate the determinants of time-to-pregnancy or acute effects, respectively. We developed a class of semi-parametric grouped transformati...
Source: Statistical Methods in Medical Research - February 16, 2016 Category: Statistics Authors: McLain, A. C., Sundaram, R., Buck Louis, G. M. Tags: Articles Source Type: research

A semi-parametric approach to the frequency of occurrence under a simple crossover trial
To analyze the frequency of occurrence for an event of interest in a crossover design, we propose a semi-parametric approach. We develop two point estimators and four interval estimators in closed forms for the treatment effect under a random effects multiplicative risk model. Using Monte Carlo simulations, we evaluate these estimators and compare the four interval estimators with the classical interval estimator suggested elsewhere in a variety of situations. We note that the point estimator using the ratio of two arithmetic averages of mean frequencies under a multiplicative risk model can be comparable to the point esti...
Source: Statistical Methods in Medical Research - February 16, 2016 Category: Statistics Authors: Lui, K.-J., Chang, K.-C. Tags: Articles Source Type: research

The channel capacity of a diagnostic test as a function of test sensitivity and test specificity
We apply the information theory concept of "channel capacity" to diagnostic test performance and derive an expression for channel capacity in terms of test sensitivity and test specificity. The expected value of the amount of information a diagnostic test will provide is equal to the "mutual information" between the test result and the disease state. For the case in which only two test results and two disease states are considered, mutual information, I(D;R), is a function of sensitivity, specificity, and the pretest probability of disease. The channel capacity of the test is the maximal value of I(D;R) for a given sensiti...
Source: Statistical Methods in Medical Research - November 26, 2015 Category: Statistics Authors: Benish, W. A. Tags: Articles Source Type: research

Unscaled Bayes factors for multiple hypothesis testing in microarray experiments
In this study, we approach multiple hypothesis testing based on both Bayes factors and p-values, regarding multiple hypothesis testing as a multiple model selection problem. To obtain the Bayes factors we assume default priors that are typically improper. In this case, the Bayes factor is usually undetermined due to the ratio of prior pseudo-constants. We show that ignoring prior pseudo-constants leads to unscaled Bayes factor which do not invalidate the inferential procedure in multiple hypothesis testing, because they are used within a comparative scheme. In fact, using partial information from the p-values, we are able ...
Source: Statistical Methods in Medical Research - November 26, 2015 Category: Statistics Authors: Bertolino, F., Cabras, S., Castellanos, M. E., Racugno, W. Tags: Articles Source Type: research

A comparison of power analysis methods for evaluating effects of a predictor on slopes in longitudinal designs with missing data
In many longitudinal studies, evaluating the effect of a binary or continuous predictor variable on the rate of change of the outcome, i.e. slope, is often of primary interest. Sample size determination of these studies, however, is complicated by the expectation that missing data will occur due to missed visits, early drop out, and staggered entry. Despite the availability of methods for assessing power in longitudinal studies with missing data, the impact on power of the magnitude and distribution of missing data in the study population remain poorly understood. As a result, simple but erroneous alterations of the sample...
Source: Statistical Methods in Medical Research - November 26, 2015 Category: Statistics Authors: Wang, C., Hall, C. B., Kim, M. Tags: Articles Source Type: research

Consistent causal effect estimation under dual misspecification and implications for confounder selection procedures
In a previously published article in this journal, Vansteeland et al. [Stat Methods Med Res. Epub ahead of print 12 November 2010. DOI: 10.1177/0962280210387717] address confounder selection in the context of causal effect estimation in observational studies. They discuss several selection strategies and propose a procedure whose performance is guided by the quality of the exposure effect estimator. The authors note that when a particular linearity condition is met, consistent estimation of the target parameter can be achieved even under dual misspecification of models for the association of confounders with exposure and o...
Source: Statistical Methods in Medical Research - November 26, 2015 Category: Statistics Authors: Gruber, S., van der Laan, M. J. Tags: Articles Source Type: research