Search

A linking procedure establishes a "bridge" between the scores from different patient-reported outcome (PRO) instruments that measure similar constructs. After developing a linking relationship however, it is critical to evaluate whether this relationship can be generalized to different groups. Our study aims to validate a published crosswalk for score conversion between the Brief Symptom Inventory Depression subscale and the Patient-Reported Outcomes Measurement Information System Depression 8a using an independent sample. Data were from a sample of young men who have sex with men (MSM), which differs in terms of participant age, race, and ethnicity from the sample used to develop the existing crosswalk. The validity of the newly derived crosswalk was evaluated in terms of the correlation, mean difference and standard deviation between the observed and the linked scores. The two crosswalks were further compared to evaluate if the difference was within an acceptable range. More than half of the item parameters obtained from the two samples were found to overlap in their confidence intervals. Differences between each pair of scores in the two crosswalks was within three T-score points, well within the range of each crosswalk score's standard error. This study concludes that an existing crosswalk is replicable on a sample that differs from that used for crosswalk development, but future research should continue to examine the generalizability of the linked parameters and evaluate the reproducibility of this crosswalk to other populations.

Multiparametric assays for risk stratification are widely used in the management of breast cancer, with applications being developed for a number of other cancer settings. Recent data from multiple sources suggests that different tests may provide different risk estimates at the individual patient level. There is an increasing need for robust methods to support cost effective comparisons of test performance in multiple settings. The derivation of similar risk classifications using genes comprising the following multi-parametric tests Oncotype DX® (Genomic Health.), Prosigna™ (NanoString Technologies, Inc.), MammaPrint® (Agendia Inc.) was performed using different computational approaches. Results were compared to the actual test results. Two widely used approaches were applied, firstly computational "modelling" of test results using published algorithms and secondly a "training" approach which used reference results from the commercially supplied tests. We demonstrate the potential for errors to arise when using a "modelling" approach without reference to real world test results. Simultaneously we show that a "training" approach can provide a highly cost-effective solution to the development of real-world comparisons between different multigene signatures. Comparisons between existing multiparametric tests is challenging, and evidence on discordance between tests in risk stratification presents further dilemmas. We present an approach, modelled in breast cancer, which can provide health care providers and researchers with the potential to perform robust and meaningful comparisons between multigene tests in a cost-effective manner. We demonstrate that whilst viable estimates of gene signatures can be derived from modelling approaches, in our study using a training approach allowed a close approximation to true signature results.

We present a statistical framework for estimation and application of sample allele frequency spectra from New-Generation Sequencing (NGS) data. In this method, we first estimate the allele frequency spectrum using maximum likelihood. In contrast to previous methods, the likelihood function is calculated using a dynamic programming algorithm and numerically optimized using analytical derivatives. We then use a bayesian method for estimating the sample allele frequency in a single site, and show how the method can be used for genotype calling and SNP calling. We also show how the method can be extended to various other cases including cases with deviations from Hardy-Weinberg equilibrium. We evaluate the statistical properties of the methods using simulations and by application to a real data set.