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Statistics in Medicine

Xiaodong Luo, Junshan Qiu, Steven Bai, Hong Tian
To analyze prioritized outcomes, Buyse (2010) and Pocock et al. (2012) proposed the win loss approach. In this paper, we first study the relationship between the win loss approach and the traditional survival analysis on the time to the first event. We then propose the weighted win loss statistics to improve the efficiency of the unweighted methods. A closed-form variance estimator of the weighted win loss statistics is derived to facilitate hypothesis testing and study design. We also calculated the contribution index to better interpret the results of the weighted win loss approach...
March 26, 2017: Statistics in Medicine
Olga M Kuznetsova, Victoria Plamadeala Johnson
The paper discusses three methods for expanding the biased coin randomization (BCR) to unequal allocation while preserving the unconditional allocation ratio at every step. The first method originally proposed in the contexts of BCR and minimization is based on mapping from an equal allocation multi-arm BCR. Despite the improvement proposed in this paper to ensure tighter adherence to the targeted unequal allocation, this method still distributes the probability mass at least as wide as the permuted block randomization (PBR)...
March 23, 2017: Statistics in Medicine
Nabihah Tayob, Susan Murray
The lung allocation system has reduced the number of waitlist deaths by ranking transplant candidates on the basis of a lung allocation score that requires estimation of the current 1-year restricted mean waitlist survival (urgency). Fewer waitlist deaths and the systematic removal of candidates from the waitlist for transplantation present statistical challenges that must be addressed when using recent waitlist data. Multiple overlapping 1-year follow-up windows are used in a restricted mean model that estimates patient urgency on the basis of updated risk factors at the start of the window...
March 21, 2017: Statistics in Medicine
Jaakko Nevalainen, Hannu Oja, Somnath Datta
Clustered data are often encountered in biomedical studies, and to date, a number of approaches have been proposed to analyze such data. However, the phenomenon of informative cluster size (ICS) is a challenging problem, and its presence has an impact on the choice of a correct analysis methodology. For example, Dutta and Datta (2015, Biometrics) presented a number of marginal distributions that could be tested. Depending on the nature and degree of informativeness of the cluster size, these marginal distributions may differ, as do the choices of the appropriate test...
March 21, 2017: Statistics in Medicine
Wen Li, Cong Chen, Xiaoyun Li, Robert A Beckman
A personalized medicine may benefit a subpopulation with certain predictive biomarker signatures or certain disease types. However, there is great uncertainty about drug activity in a subpopulation when designing a confirmatory trial in practice, and it is logical to take a two-stage approach with the study unless credible external information is available for decision-making purpose. The first stage deselects (or prunes) non-performing subpopulations at an interim analysis, and the second stage pools the remaining subpopulations in the final analysis...
March 17, 2017: Statistics in Medicine
Harvey Goldstein, Katie Harron, Mario Cortina-Borja
With increasing availability of large datasets derived from administrative and other sources, there is an increasing demand for the successful linking of these to provide rich sources of data for further analysis. Variation in the quality of identifiers used to carry out linkage means that existing approaches are often based upon 'probabilistic' models, which are based on a number of assumptions, and can make heavy computational demands. In this paper, we suggest a new approach to classifying record pairs in linkage, based upon weights (scores) derived using a scaling algorithm...
March 16, 2017: Statistics in Medicine
Renaud Tissier, Roula Tsonaka, Simon P Mooijaart, Eline Slagboom, Jeanine J Houwing-Duistermaat
The case-control design is often used to test associations between the case-control status and genetic variants. In addition to this primary phenotype, a number of additional traits, known as secondary phenotypes, are routinely recorded, and typically, associations between genetic factors and these secondary traits are studied too. Analysing secondary phenotypes in case-control studies may lead to biased genetic effect estimates, especially when the marker tested is associated with the primary phenotype and when the primary and secondary phenotypes tested are correlated...
March 16, 2017: Statistics in Medicine
Graham M Wheeler, Michael J Sweeting, Adrian P Mander
Phase I trials of anti-cancer therapies aim to identify a maximum tolerated dose (MTD), defined as the dose that causes unacceptable toxicity in a target proportion of patients. Both rule-based and model-based methods have been proposed for MTD recommendation. The escalation with overdose control (EWOC) approach is a model-based design where the dose assigned to the next patient is one that, given all available data, has a posterior probability of exceeding the MTD equal to a pre-specified value known as the feasibility bound...
March 15, 2017: Statistics in Medicine
Adam R Brentnall, Peter Sasieni, Jack Cuzick
When one arm in a trial has a worse early endpoint such as recurrence, a data-monitoring committee might recommend that all participants are offered the apparently superior treatment. The resultant crossover makes it difficult to measure differences between arms thereafter, including for longer-term endpoints such as mortality and disease-specific mortality. In this paper, we consider estimators of the efficacy of treatment on those who would not cross over if randomised to the apparently inferior arm. Binomial and proportional hazards maximum likelihood estimators are developed...
March 15, 2017: Statistics in Medicine
Rainer Puhr, Georg Heinze, Mariana Nold, Lara Lusa, Angelika Geroldinger
Firth's logistic regression has become a standard approach for the analysis of binary outcomes with small samples. Whereas it reduces the bias in maximum likelihood estimates of coefficients, bias towards one-half is introduced in the predicted probabilities. The stronger the imbalance of the outcome, the more severe is the bias in the predicted probabilities. We propose two simple modifications of Firth's logistic regression resulting in unbiased predicted probabilities. The first corrects the predicted probabilities by a post hoc adjustment of the intercept...
March 12, 2017: Statistics in Medicine
Longhai Li, Cindy X Feng, Shi Qiu
An important statistical task in disease mapping problems is to identify divergent regions with unusually high or low risk of disease. Leave-one-out cross-validatory (LOOCV) model assessment is the gold standard for estimating predictive p-values that can flag such divergent regions. However, actual LOOCV is time-consuming because one needs to rerun a Markov chain Monte Carlo analysis for each posterior distribution in which an observation is held out as a test case. This paper introduces a new method, called integrated importance sampling (iIS), for estimating LOOCV predictive p-values with only Markov chain samples drawn from the posterior based on a full data set...
March 12, 2017: Statistics in Medicine
K Maruo, Y Yamaguchi, H Noma, M Gosho
We derived results for inference on parameters of the marginal model of the mixed effect model with the Box-Cox transformation based on the asymptotic theory approach. We also provided a robust variance estimator of the maximum likelihood estimator of the parameters of this model in consideration of the model misspecifications. Using these results, we developed an inference procedure for the difference of the model median between treatment groups at the specified occasion in the context of mixed effects models for repeated measures analysis for randomized clinical trials, which provided interpretable estimates of the treatment effect...
March 10, 2017: Statistics in Medicine
Alok Kumar Dwivedi, Indika Mallawaarachchi, Luis A Alvarado
Experimental studies in biomedical research frequently pose analytical problems related to small sample size. In such studies, there are conflicting findings regarding the choice of parametric and nonparametric analysis, especially with non-normal data. In such instances, some methodologists questioned the validity of parametric tests and suggested nonparametric tests. In contrast, other methodologists found nonparametric tests to be too conservative and less powerful and thus preferred using parametric tests...
March 9, 2017: Statistics in Medicine
Siva Sivaganesan, Peter Müller, Bin Huang
We provide a Bayesian decision theoretic approach to finding subgroups that have elevated treatment effects. Our approach separates the modeling of the response variable from the task of subgroup finding and allows a flexible modeling of the response variable irrespective of potential subgroups of interest. We use Bayesian additive regression trees to model the response variable and use a utility function defined in terms of a candidate subgroup and the predicted response for that subgroup. Subgroups are identified by maximizing the expected utility where the expectation is taken with respect to the posterior predictive distribution of the response, and the maximization is carried out over an a priori specified set of candidate subgroups...
March 9, 2017: Statistics in Medicine
Emma Persson, Ingeborg Waernbaum, Torbjörn Lind
When an initial case-control study is performed, data can be used in a secondary analysis to evaluate the effect of the case-defining event on later outcomes. In this paper, we study the example in which the role of the event is changed from a response variable to a treatment of interest. If the aim is to estimate marginal effects, such as average effects in the population, the sampling scheme needs to be adjusted for. We study estimators of the average effect of the treatment in a secondary analysis of matched and unmatched case-control data where the probability of being a case is known...
March 9, 2017: Statistics in Medicine
Soyoung Kim, Ying Huang
Combining multiple markers can improve classification accuracy compared with using a single marker. In practice, covariates associated with markers or disease outcome can affect the performance of a biomarker or biomarker combination in the population. The covariate-adjusted receiver operating characteristic (ROC) curve has been proposed as a tool to tease out the covariate effect in the evaluation of a single marker; this curve characterizes the classification accuracy solely because of the marker of interest...
March 9, 2017: Statistics in Medicine
Matthew Zawistowski, Jeremy B Sussman, Timothy P Hofer, Douglas Bentley, Rodney A Hayward, Wyndy L Wiitala
Creating accurate risk prediction models from Big Data resources such as Electronic Health Records (EHRs) is a critical step toward achieving precision medicine. A major challenge in developing these tools is accounting for imperfect aspects of EHR data, particularly the potential for misclassified outcomes. Misclassification, the swapping of case and control outcome labels, is well known to bias effect size estimates for regression prediction models. In this paper, we study the effect of misclassification on accuracy assessment for risk prediction models and find that it leads to bias in the area under the curve (AUC) metric from standard ROC analysis...
February 28, 2017: Statistics in Medicine
Brisa N Sánchez, Sehee Kim, Mary D Sammel
Latent variable (LV) models are increasingly being used in environmental epidemiology as a way to summarize multiple environmental exposures and thus minimize statistical concerns that arise in multiple regression. LV models may be especially useful when multivariate exposures are collected repeatedly over time. LV models can accommodate a variety of assumptions but, at the same time, present the user with many choices for model specification particularly in the case of exposure data collected repeatedly over time...
February 27, 2017: Statistics in Medicine
Jongho Im, Eunyong Ahn, Namseon Beck, Jae Kwang Kim, Taesung Park
Correlation coefficient estimates are often attenuated for truncated samples in the sense that the estimates are biased towards zero. Motivated by real data collected in South Sudan, we consider correlation coefficient estimation with singly truncated bivariate data. By considering a linear regression model in which a truncated variable is used as an explanatory variable, a consistent estimator for the regression slope can be obtained from the ordinary least squares method. A consistent estimator of the correlation coefficient is then obtained by multiplying the regression slope estimator by the variance ratio of the two variables...
February 27, 2017: Statistics in Medicine
Yanqin Feng, Ran Duan, Jianguo Sun
Nonparametric comparison of survival functions is one of the most commonly required task in failure time studies such as clinical trials, and for this, many procedures have been developed under various situations. This paper considers a situation that often occurs in practice but has not been discussed much: the comparison based on interval-censored data in the presence of unequal censoring. That is, one observes only interval-censored data, and the distributions of or the mechanisms behind censoring variables may depend on treatments and thus be different for the subjects in different treatment groups...
February 27, 2017: Statistics in Medicine
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