Robust Variance Estimation for Covariate-Adjusted Unconditional Treatment Effect in Randomized Clinical Trials with Binary Outcomes.

To improve precision of estimation and power of testing hypothesis for an unconditional treatment effect in randomized clinical trials with binary outcomes, researchers and regulatory agencies recommend using g-computation as a reliable method of covariate adjustment. However, the practical application of g-computation is hindered by the lack of an explicit robust variance formula that can be used for different unconditional treatment effects of interest. To fill this gap, we provide explicit and robust variance estimators for g-computation estimators and demonstrate through simulations that the variance estimators can be reliably applied in practice.

An Improved Matching Practice for Augmenting a Randomized Clinical Trial with External Control.

The use of information from real world to assess the effectiveness of medical products is becoming increasingly popular and more acceptable by regulatory agencies. According to a strategic real-world evidence framework published by U.S. Food and Drug Administration, a hybrid randomized controlled trial that augments internal control arm with real-world data is a pragmatic approach worth more attention. In this paper, we aim to improve on existing matching designs for such a hybrid randomized controlled trial. In particular, we propose to match the entire concurrent randomized clinical trial (RCT) such that (1) the matched external control subjects used to augment the internal control arm are as comparable as possible to the RCT population, (2) every active treatment arm in an RCT with multiple treatments is compared with the same control group, and (3) matching can be conducted and the matched set locked before treatment unblinding to better maintain the data integrity and increase the credibility of the analysis. Besides a weighted estimator, we also introduce a bootstrap method to obtain its variance estimation. The finite sample performance of the proposed method is evaluated by simulations based on data from a real clinical trial.

PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics.

Polygenic risk scores (PRSs) have wide applications in human genetics research, but often include tuning parameters which are difficult to optimize in practice due to limited access to individual-level data. Here, we introduce PUMAS, a novel method to fine-tune PRS models using summary statistics from genome-wide association studies (GWASs). Through extensive simulations, external validations, and analysis of 65 traits, we demonstrate that PUMAS can perform various model-tuning procedures using GWAS summary statistics and effectively benchmark and optimize PRS models under diverse genetic architecture. Furthermore, we show that fine-tuned PRSs will significantly improve statistical power in downstream association analysis.

Cox regression with survival-time-dependent missing covariate values.

Analysis with time-to-event data in clinical and epidemiological studies often encounters missing covariate values, and the missing at random assumption is commonly adopted, which assumes that missingness depends on the observed data, including the observed outcome which is the minimum of survival and censoring time. However, it is conceivable that in certain settings, missingness of covariate values is related to the survival time but not to the censoring time. This is especially so when covariate missingness is related to an unmeasured variable affected by the patient's illness and prognosis factors at baseline. If this is the case, then the covariate missingness is not at random as the survival time is censored, and it creates a challenge in data analysis. In this article, we propose an approach to deal with such survival-time-dependent covariate missingness based on the well known Cox proportional hazard model. Our method is based on inverse propensity weighting with the propensity estimated by nonparametric kernel regression. Our estimators are consistent and asymptotically normal, and their finite-sample performance is examined through simulation. An application to a real-data example is included for illustration.

Metastatic Bulk Independently Predicts Outcomes for EGFR Precision Targeting in Colorectal Cancer.

Background: Molecular profiles guide the clinical management of metastatic colorectal cancer (mCRC), particularly related to the use of anti-epidermal growth factor receptor (EGFR) antibodies. Tumor sidedness has also been implicated in resistance to these therapies, but has largely been studied in the first-line setting. We examined the role of tumor sidedness and disease bulk in predicting clinical outcomes to anti-EGFR therapy in the treatment-refractory setting. Methods: We identified a retrospective cohort of 62 patients with KRAS wild-type mCRC who received anti-EGFR therapy in the late-line setting. Response was assessed per RECIST 1.1, with bulky disease defined as any single lesion >35 mm in longest cross-sectional diameter or nodal short axis. Primary sidedness was defined in relation to the splenic flexure. Results: Patients with right-sided primary tumors at time of late-line EGFR therapy presented with increased tumor bulk and worsened overall survival (OS) relative to left-sided primary tumors. Tumor bulk, defined as either a categorical or continuous variable, predicted worsened progression-free survival (PFS) and OS, which persisted when controlling for differences in the primary tumor location. Within the right-sided cohort, no objective responses were observed for bulky disease or during treatment with anti-EGFR monotherapy. The nonbulky cohort experienced clinical benefit with anti-EGFR monotherapy, showing similar PFS and an improved response rate compared with sequential chemotherapy. Conclusions: In an effort to expand understanding of the role of primary sidedness in clinical response to anti-EGFR therapy, we identified sidedness and tumor bulk as potential predictive biomarkers of clinical response in late-line mCRC. Future prospective studies of EGFR targeting should consider tumor bulk in addition to molecular profiling in the identification of populations most likely to achieve meaningful clinical benefit.

The Presence of Cyclooxygenase 2, Tumor-Associated Macrophages, and Collagen Alignment as Prognostic Markers for Invasive Breast Carcinoma Patients.

Inflammation, and the organization of collagen in the breast tumor microenvironment, is an important mediator of breast tumor progression. However, a direct link between markers of inflammation, collagen organization, and patient outcome has yet to be established. A tumor microarray of 371 invasive breast carcinoma biopsy specimens was analyzed for expression of inflammatory markers, including cyclooxygenase 2 (COX-2), macrophages, and several collagen features in the tumor nest (TN) or the tumor-associated stroma (TS). The tumor microarray cohort included females, aged 18 to 80 years, with a median follow-up of 8.4 years. High expression of COX-2 (TN), CD68 (TS), and CD163 (TN and TS) predicted worse patient overall survival (OS). This notion was strengthened by the finding from the multivariate analysis that high numbers of CD163+ macrophages in the TS is an independent prognostic factor. Overall collagen deposition was associated with high stromal expression of COX-2 and CD163; however, total collagen deposition was not a predictor for OS. Conversely, local collagen density, alignment and perpendicular alignment to the tumor boundary (tumor-associated collagen signature-3) were predictors of OS. These results suggest that in invasive carcinoma, the localization of inflammatory cells and aligned collagen orientation predict poor patient survival. Additional clinical studies may help validate whether therapy with selective COX-2 inhibitors alters expression of CD68 and CD163 inflammatory markers.

Identification and Quantification of Acylated Homoserine Lactones in Shewanella baltica, the Specific Spoilage Organism of Pseudosciaena crocea, by Ultrahigh-Performance Liquid Chromatography Coupled to Triple Quadrupole Mass Spectrometry.

Bacteria communicate with one another using chemical signal molecules called autoinducers, and the most prevalent autoinducers used by Gram-negative bacteria are N-acylated homoserine lactones (AHLs). However, AHLs are hardly detected in Shewanella baltica, the specific spoilage organism of Pseudosciaena crocea. In this study, we applied ultrahigh-performance liquid chromatography coupled to triple quadrupole tandem mass spectrometry to determine AHLs. This method enabled the quantification of up to 11 AHLs within 5.6 min with excellent sensitivity (ng/mL level) and linearity (r2 > 0.99), which further detected 9 AHLs produced by S. baltica. Furthermore, by using a biofilm formation assay and statistical analysis, the biofilm-inducing activity of AHL in S. baltica was first revealed. Our results elucidated the physiological role of AHL in S. baltica and provided a satisfactory method to detect AHLs and a statistical model to predict food spoilage properties.