Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles.

BackgroundTechnological advances have enabled the analysis of very small amounts of DNA in forensic cases. However, the DNA profiles from such evidence are frequently incomplete and can contain contributions from multiple individuals. The complexity of such samples confounds the assessment of the statistical weight of such evidence. One approach to account for this uncertainty is to use a likelihood ratio framework to compare the probability of the evidence profile under different scenarios. While researchers favor the likelihood ratio framework, few open-source software solutions with a graphical user interface implementing these calculations are available for practicing forensic scientists.ResultsTo address this need, we developed Lab Retriever, an open-source, freely available program that forensic scientists can use to calculate likelihood ratios for complex DNA profiles. Lab Retriever adds a graphical user interface, written primarily in JavaScript, on top of a C++ implementation of the previously published R code of Balding. We redesigned parts of the original Balding algorithm to improve computational speed. In addition to incorporating a probability of allelic drop-out and other critical parameters, Lab Retriever computes likelihood ratios for hypotheses that can include up to four unknown contributors to a mixed sample. These computations are completed nearly instantaneously on a modern PC or Mac computer.ConclusionsLab Retriever provides a practical software solution to forensic scientists who wish to assess the statistical weight of evidence for complex DNA profiles. Executable versions of the program are freely available for Mac OSX and Windows operating systems

Patient-reported outcomes with first-line durvalumab plus platinum-etoposide versus platinum-etoposide in extensive-stage small-cell lung cancer (CASPIAN): a randomized, controlled, open-label, phase III study

Objectives In the phase III CASPIAN study, first-line durvalumab plus etoposide in combination with either cisplatin or carboplatin (EP) significantly improved overall survival (primary endpoint) versus EP alone in patients with extensive-stage small-cell lung cancer (ES-SCLC) at the interim analysis. Here we report patient-reported outcomes (PROs). Materials and methods Treatment-naïve patients with ES-SCLC received 4 cycles of durvalumab plus EP every 3 weeks followed by maintenance durvalumab every 4 weeks until progression, or up to 6 cycles of EP every 3 weeks. PROs, assessed with the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-Core 30 (QLQ-C30) version 3 and its lung cancer module, the Quality of Life Questionnaire-Lung Cancer 13 (QLQ-LC13), were prespecified secondary endpoints. Changes from baseline to disease progression or 12 months in prespecified key disease-related symptoms (cough, dyspnea, chest pain, fatigue, appetite loss) were analyzed with a mixed model for repeated measures. Time to deterioration (TTD) of symptoms, functioning, and global health status/quality of life (QoL) from randomization was analyzed. Results In the durvalumab plus EP and EP arms, 261 and 260 patients were PRO-evaluable. Patients in both arms experienced numerically reduced symptom burden over 12 months or until progression for key symptoms. For the improvements from baseline in appetite loss, the between-arm difference was statistically significant, favoring durvalumab plus EP (difference, −4.5; 99% CI: −9.04, −0.04; nominal p = 0.009). Patients experienced longer TTD with durvalumab plus EP versus EP for all symptoms (hazard ratio [95% CI] for key symptoms: cough 0.78 [0.600‒1.026]; dyspnea 0.79 [0.625‒1.006]; chest pain 0.76 [0.575‒0.996]; fatigue 0.82 [0.653‒1.027]; appetite loss 0.70 [0.542‒0.899]), functioning, and global health status/QoL. Conclusion Addition of durvalumab to first-line EP maintained QoL and delayed worsening of patient-reported symptoms, functioning, and global health status/QoL compared with EP

Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles

BACKGROUND: Technological advances have enabled the analysis of very small amounts of DNA in forensic cases. However, the DNA profiles from such evidence are frequently incomplete and can contain contributions from multiple individuals. The complexity of such samples confounds the assessment of the statistical weight of such evidence. One approach to account for this uncertainty is to use a likelihood ratio framework to compare the probability of the evidence profile under different scenarios. While researchers favor the likelihood ratio framework, few open-source software solutions with a graphical user interface implementing these calculations are available for practicing forensic scientists. RESULTS: To address this need, we developed Lab Retriever, an open-source, freely available program that forensic scientists can use to calculate likelihood ratios for complex DNA profiles. Lab Retriever adds a graphical user interface, written primarily in JavaScript, on top of a C++ implementation of the previously published R code of Balding. We redesigned parts of the original Balding algorithm to improve computational speed. In addition to incorporating a probability of allelic drop-out and other critical parameters, Lab Retriever computes likelihood ratios for hypotheses that can include up to four unknown contributors to a mixed sample. These computations are completed nearly instantaneously on a modern PC or Mac computer. CONCLUSIONS: Lab Retriever provides a practical software solution to forensic scientists who wish to assess the statistical weight of evidence for complex DNA profiles. Executable versions of the program are freely available for Mac OSX and Windows operating systems. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0740-8) contains supplementary material, which is available to authorized users

Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles.

BackgroundTechnological advances have enabled the analysis of very small amounts of DNA in forensic cases. However, the DNA profiles from such evidence are frequently incomplete and can contain contributions from multiple individuals. The complexity of such samples confounds the assessment of the statistical weight of such evidence. One approach to account for this uncertainty is to use a likelihood ratio framework to compare the probability of the evidence profile under different scenarios. While researchers favor the likelihood ratio framework, few open-source software solutions with a graphical user interface implementing these calculations are available for practicing forensic scientists.ResultsTo address this need, we developed Lab Retriever, an open-source, freely available program that forensic scientists can use to calculate likelihood ratios for complex DNA profiles. Lab Retriever adds a graphical user interface, written primarily in JavaScript, on top of a C++ implementation of the previously published R code of Balding. We redesigned parts of the original Balding algorithm to improve computational speed. In addition to incorporating a probability of allelic drop-out and other critical parameters, Lab Retriever computes likelihood ratios for hypotheses that can include up to four unknown contributors to a mixed sample. These computations are completed nearly instantaneously on a modern PC or Mac computer.ConclusionsLab Retriever provides a practical software solution to forensic scientists who wish to assess the statistical weight of evidence for complex DNA profiles. Executable versions of the program are freely available for Mac OSX and Windows operating systems

Additional file 2: of Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles

Description of the drop-in model used by Lab Retriever. (PDF 90 kb