बायोमेट्रिक्स और बायोस्टैटिस्टिक्स जर्नल

A diagnostic test in a screening study detects a clinical condition of interest in its asymptomatic stage. Evaluating the diagnostic test in a screening study is a challenging task since a diagnostic test is evaluated based on its analytical and clinical performance. In order to evaluate clinical performance of a diagnostic test in a screening study, it is crucial to investigate clinical outcome studies such as survival studies. Furthermore, there are important biases to be considered in a screening study. In this paper, statistical issues associated with screening studies are discussed, and statistical adjustment for screening-related bias, which is called length bias, is presented. Both Vardi’s bias-adjusted nonparametric maximum likelihood estimator and linear combination estimators have shown to adjust length bias successfully, and generate bias-adjusted survival curve close to the true survival curve. Finally, some practical issues associated with early detection are also presented.

In randomized trials in which the outcome requires considerable follow-up, participants may die before the trial is complete. In such cases, for the individuals who die before follow-up is complete, the outcome is not simply missing, but is undefined. Some authors refer to this situation as one in which the outcome is “truncated by death� [1,2], to distinguish this scenario from cases in which the outcome is merely missing because of inadequate data collection. In these settings, a crude comparison of the outcome between those who survived in each treatment arm may give misleading results, because we no longer preserve randomization by conditioning on a post-treatment event (survival) and thus the crude comparison is not a comparison for the same population comparing different treatments, but a comparison of different populations.

Since centuries the external ear which is known as the pinna or the auricle has been used as a means of identification. It has been studied and described as a part of procedures to establish the identity of criminals and victims of crimes and accidents. Not only the auricle itself showed potential for establishing the identity of criminals, but also its prints. When perpetrators of crimes listen at, for instance, a door or window before breaking and entering, oils and waxes leave prints that can be made visible using techniques similar to those used when lifting fingerprints. These prints appear characteristic for the ears that made them.

The sample size formulae given in elementary biostatistics textbooks deal only with simple situations: estimation of one, or a comparison of at most two, mean(s) or proportion(s). While many specialized textbooks give sample formulae/tables for analyses involving odds and rate ratios, few deal explicitly with statistical considerations for slopes (regression coefficients), for analyses involving confounding variables or with the fact that most analyses rely on some type of generalized linear model. Thus, the investigator is typically forced to use “black-box� computer programs or tables, or to borrow from tables in the social sciences, where the emphasis is on correlation coefficients. The concern in the – usually very separate – modules or standalone software programs is more with user friendly input and output. The emphasis on numerical exactness is particularly unfortunate, given the rough, prospective, and thus uncertain, nature of the exercise, and that different textbooks and software may give different sample sizes for the same design. In addition, some programs focus on required numbers per group, others on an overall number. We present users with a single universal (though sometimes approximate) formula that explicitly isolates the impacts of the various factors one from another, and gives some insight into the determinants for each factor. Equally important, it shows how seemingly very different types of analyses, from the elementary to the complex, can be accommodated within a common framework by viewing them as special cases of the generalized linear model.

Family datasets could provide good resources for association studies as an initial investigation or a replication study. The current family-based association tests analyze data only from full sibships of a nuclear family or extended pedigrees of related nuclear families. In order to fully exert all possible information in the family data, we propose a “Pedigrees with Half-sibs Association Test� (PHAST) to accommodate half-siblings if they are available. PHAST adopts the idea of transmission score from the Pedigree Disequilibrium Test (PDT) to construct the test statistic. The difference is that it utilizes the identity-by-descent (IBD) information of the marker between sibling pairs (full or half sibs) to construct an allelic transmission statistic. If parental genotypes are missing, EM algorithm is used to infer parental genotypes and compute transmission scores for all possible scenarios. The computer simulation results suggested that our new method has valid type I error rates under varied family structures. Our method could have more power than PDT and FBAT when the sample size of half-sibs increases, especially the families without parental genotypes. In conclusion, our method can serve as an alternative method of the existing family-based association tests. Furthermore, it can relax the ascertainment criteria for studying late onset diseases since limited siblings are available.

This paper considers the problem of undertaking fully Bayesian inference for both the parameters and structure of a vector autoregressive model on the basis of time course data in the ``p>> n scenario’’. The autoregressive matrix is assumed to be sparse, but of unknown structure. The resulting algorithm for dynamic Bayesian network inference is shown to be highly effective, and is applied to the problem of dynamic network inference from time course microarray data using a dataset concerned with the transient response of budding yeast to telomere damage.

Diagnostic test studies are receiving increasing attention, but are rather challenging to identify efficiently and reliably. Health care professionals seek information regarding the best available evidence on test accuracy, and there is also a growing requirement to understand the measures of diagnostic tests. An outcome of epidemiological studies, diagnostic tests, and comparative therapeutic trails are often presented in the form of 2 x 2 tables. The analysis from these tables and its significance must be interpreted correctly, so as to answer the clinical research questions of the studies. This article discusses about the measures which could be derived from a 2x2 table format, for a diagnostic test situation, where the interest is to observe the relationship between two qualitative (nominal) variables.