Simulation-based Estimation of Mean and Standard Deviation for Meta-analysis via Approximate Bayesian Computation (ABC)

Background: When conducting a meta-analysis of a continuous outcome, estimated means and standard deviations from the selected studies are required in order to obtain an overall estimate of the mean effect and its confidence interval. If these quantities are not directly reported in the publications, they need to must be estimated from other reported summary statistics, such as the median, the minimum, the maximum, and quartiles. Methods: We propose a simulation-based estimation approach using the Approximate Bayesian Computation (ABC) technique for estimating mean and standard deviation based on various sets of summary statistics found in published studies. We conduct a simulation study to compare the proposed ABC method with the existing methods of Hozo et al. (2005), Bland (2015), and Wan et al. (2014). Results: In the estimation of the standard deviation, our ABC method performs best in skewed or heavy-tailed distributions. The average relative error (ARE) approaches zero as sample size increases. In the normal distribution, our ABC performs well. However, the Wan et al. method is best since it is based on the normal distribution assumption. When the distribution is skewed or heavy-tailed, the ARE of Wan et al. moves away from zero even as sample size increases. In the estimation of the mean, our ABC method is best since the AREs converge to zero. Conclusion: ABC is a flexible method for estimating the study-specific mean and standard deviation for meta-analysis, especially with underlying skewed or heavy-tailed distributions. The ABC method can be applied using other reported summary statistics such as the posterior mean and 95% credible interval when Bayesian analysis has been employed

Wavelet methods and statistical applications: network security and bioinformatics

Wavelet methods possess versatile properties for statistical applications. We would like to explore the advantages of using wavelets in the analyses in two different research areas. First of all, we develop an integrated tool for online detection of network anomalies. We consider statistical change point detection algorithms, for both local changes in the variance and for jumps detection, and propose modified versions of these algorithms based on moving window techniques. We investigate performances on simulated data and on network traffic data with several superimposed attacks. All detection methods are based on wavelet packets transformations. We also propose a Bayesian model for the analysis of high-throughput data where the outcome of interest has a natural ordering. The method provides a unified approach for identifying relevant markers and predicting class memberships. This is accomplished by building a stochastic search variable selection method into an ordinal model. We apply the methodology to the analysis of proteomic studies in prostate cancer. We explore wavelet-based techniques to remove noise from the protein mass spectra. The goal is to identify protein markers associated with prostate-specific antigen (PSA) level, an ordinal diagnostic measure currently used to stratify patients into different risk groups

ABCMETAapp: R Shiny Application for Simulation-based Estimation of Mean and Standard Deviation for Meta-analysis via Approximate Bayesian Computation (ABC)

Background and Objective: In meta-analysis based on continuous outcome, estimated means and corresponding standard deviations from the selected studies are key inputs to obtain a pooled estimate of the mean and its confidence interval. We often encounter the situation that these quantities are not directly reported in the literatures. Instead, other summary statistics are reported such as median, minimum, maximum, quartiles, and study sample size. Based on available summary statistics, we need to estimate estimates of mean and standard deviation for meta-analysis. Methods: We developed a R Shiny code based on approximate Bayesian computation (ABC), ABCMETA, to deal with this situation. Results: In this article, we present an interactive and user-friendly R Shiny application for implementing the proposed method (named ABCMETAapp). In ABCMETAapp, users can choose an underlying outcome distribution other than the normal distribution when the distribution of the outcome variable is skewed or heavy tailed. We show how to run ABCMETAapp with examples. Conclusions: ABCMETAapp provides a R Shiny implementation. This method is more flexible than the existing analytical methods since estimation can be based on five different distribution (Normal, Lognormal, Exponential, Weibull, and Beta) for the outcome variable

Identifying Biomarkers from Mass Spectrometry Data with Ordinal Outcome

In recent years, there has been an increased interest in using protein mass spectroscopy to identify molecular markers that discriminate diseased from healthy individuals. Existing methods are tailored towards classifying observations into nominal categories. Sometimes, however, the outcome of interest may be measured on an ordered scale. Ignoring this natural ordering results in some loss of information. In this paper, we propose a Bayesian model for the analysis of mass spectrometry data with ordered outcome. The method provides a unified approach for identifying relevant markers and predicting class membership. This is accomplished by building a stochastic search variable selection method within an ordinal outcome model. We apply the methodology to mass spectrometry data on ovarian cancer cases and healthy individuals. We also utilize wavelet-based techniques to remove noise from the mass spectra prior to analysis. We identify protein markers associated with being healthy, having low grade ovarian cancer, or being a high grade case. For comparison, we repeated the analysis using conventional classification procedures and found improved predictive accuracy with our method

Efficacy and outcomes of ramucirumab and docetaxel in patients with metastatic non-small cell lung cancer after disease progression on immune checkpoint inhibitor therapy: Results of a monocentric, retrospective analysis

Current first-line standard therapy for metastatic non-small cell lung cancer without driver mutations involves chemotherapy and immunotherapy combination. Prior to the advent of immune checkpoint inhibition, REVEL, a randomized phase III trial demonstrated improved progression-free and overall survival with ramucirumab and docetaxel (ram+doc) in patients who failed platinum-based first-line therapy. Long-term outcomes related to second-line ramucirumab and docetaxel after first-line immunotherapy exposure remain unknown. We analyzed outcomes for 35 patients from our center whom received ramucirumab and docetaxel following disease progression on chemotherapy and immunotherapy combination. Median progression-free survival among patients who received ram+doc after exposure to immunotherapy was 6.6 months (95% CI = 5.5 to 14.9 months; p<0.0001), and median overall survival was 20.9 months (95% CI = 13.4 months to infinity; p<0.0001). These outcomes suggest that there may a synergistic benefit to combining chemotherapy with anti-angiogenic therapy after immunotherapy exposure. Future analyses should be evaluated prospectively and among a larger patient subset

Pathologic response rates after neoadjuvant therapy for sarcoma: A single institution study

(1) Background: Pathologic necrosis of soft tissue sarcomas (STS) has been used to determine treatment response, but its relationship to neoadjuvant treatments remains indeterminate. In this retrospective, single institution study, we hypothesized that neoadjuvant chemoradiation (NA-CRT) yields higher rates of pathologic complete response (pCR) than neoadjuvant radiation (NA-XRT) or chemotherapy (NA-CT) alone. (2) Methods: Patients with extremity STS between 2011–2020 who received neoadjuvant treatment were included. pCR was defined as percent necrosis of the surgical specimen greater than or equal to 90%. (3) Results: 79 patients were analyzed. 51.9% of the population were male with a mean age of 58.4 years. 49.4% identified as Non-Hispanic White. Twenty-six (32.9%) patients achieved pCR while 53 (67.1%) did not. NA-CT (OR 15.82, 95% CI = 2.58–96.9, p = 0.003 in univariate (UVA) and OR 24.7, 95% CI = 2.88–211.2, p = 0.003 in multivariate (MVA), respectively) and NA-XRT (OR 5.73, 95% CI = 1.51–21.8, p = 0.010 in UVA and OR 7.95, 95% CI = 1.87–33.7, p = 0.005 in MVA, respectively) was significantly associated with non-pCR when compared to NA-CRT. The analysis also demonstrated that grade 3 tumors, when using grade 2 as reference, also had significantly higher odds of achieving pCR (OR 0.23, 95% CI = 0.06–0.80, p = 0.022 in UVA and OR 0.16, 95% CI = 0.04–0.70, p = 0.015 in MVA, respectively). (4) Conclusion: NA-CRT yields superior pCR compared to other neoadjuvant regimens. This extends to higher grade tumors

Allelic based gene-gene interactions in rheumatoid arthritis

The detection of gene-gene interaction is an important approach to understand the etiology of rheumatoid arthritis (RA). The goal of this study is to identify gene-gene interaction of SNPs at the allelic level contributing to RA using real data sets (Problem 1) of North American Rheumatoid Arthritis Consortium (NARAC) provided by Genetic Analysis Workshop 16 (GAW16). We applied our novel method that can detect the interaction by a definition of nonrandom association of alleles that occurs when the contribution to RA of a particular allele inherited in one gene depends on a particular allele inherited at other unlinked genes. Starting with 639 single-nucleotide polymorphisms (SNPs) from 26 candidate genes, we identified ten two-way interacting genes and one case of three-way interacting genes. SNP rs2476601 on PTPN22 interacts with rs2306772 on SLC22A4, which interacts with rs881372 on TRAF1 and rs2900180 on C5, respectively. SNP rs2900180 on C5 interacts with rs2242720 on RUNX1, which interacts with rs881375 on TRAF1. Furthermore, rs2476601 on PTPN22 also interacts with three SNPs (rs2905325, rs1476482, and rs2106549) in linkage disequilibrium (LD) on IL6. The other three SNPs (rs2961280, rs2961283, and rs2905308) in LD on IL6 interact with two SNPs (rs477515 and rs2516049) on HLA-DRB1. SNPs rs660895 and rs532098 on HLA-DRB1 interact with rs2834779 and four SNPs in LD on RUNX1. Three-way interacting genes of rs10229203 on IL6, rs4816502 on RUNX1, and rs10818500 on C5 were also detected