Simulation study comparing analytical methods for single-item longitudinal patient-reported outcomes data

PurposeEfficient analytical methods are necessary to make reproducible inferences on single-item longitudinal ordinal patient-reported outcome (PRO) data. A thorough simulation study was performed to compare the performance of the semiparametric probabilistic index models (PIM) with a longitudinal analysis using parametric cumulative logit mixed models (CLMM).MethodsIn the setting of a control and intervention arm, we compared the power of the PIM and CLMM to detect differences in PRO adverse event (AE) between these groups using several existing and novel summary scores of PROs. For each scenario, PRO data were simulated using copula multinomial models. Comparisons were also exemplified using clinical trial data.ResultsOn average, CLMM provided substantially greater power than the PIM to detect differences in PRO-AEs between the groups when the baseline-adjusted method was used, and a small advantage in power when using the baseline symptom as a covariate.ConclusionAlthough the CLMM showed the best performance among analytical methods, it relies on assumptions difficult to verify and that might not be fulfilled in the real world, therefore our recommendation is the use of PIM models with baseline symptom as a covariate

The relationship between lifetime exposure to potentially traumatic events, peritraumatic dissociation, and PTSD in a sample of sexually assaulted women in Sao Paulo, Brazil

Sexually assaulted women represent a particularly high-risk group for developing post-traumatic stress disorder (PTSD). Potentially traumatic events (PTEs) and peritraumatic dissociation (PD) are known risk factors for PTSD. However, little is known about how previous trauma affects PD and how this relationship affects PTSD. We aimed to investigate whether PD acts as a mediator between PTEs and PTSD severity in a sample of recently sexually assaulted women in Sao Paulo, Brazil. Seventy-four sexually assaulted women aged 18–44 completed questionnaires and structured interviews on PTSD, PD, and PTEs. We examined direct and indirect effects of variables using causal mediation analysis. Lifetime exposure to PTEs was a risk factor for PD, but PD was not a risk factor for PTSD symptom severity. Also, PD was not a mediator between PTEs and PTSD severity. We provided recommendations on how to further explore the relationship between lifetime traumatic exposure, PTSD, and peritraumatic dissociation

Is platelet-lymphocyte ratio (PLR) a predictor of thrombosis and together with circulating tumor cells capable to determine recurrence-free survival in patients with gastric cancer?

Introduction: Cancer-associated thrombosis (CAT) is a major cause of morbidity and mortality in oncology patients. There are no accurate risk assessment tools to predict venous thromboembolism (VTE). Circulating tumor cells (CTCs), circulating tumor microemboli (CTM), and high platelet-lymphocyte ratio (PLR) may predispose to VTE. Objective: To evaluate correlations of CTCs, CTM, and PLR with VTE and recurrence-free survival (RFS) in gastric cancer patients. Material and Methods: Patients with gastric cancer (localized and metastatic disease) were recruited (March 2016 to April 2017). CTCs were analysed by ISET at two timepoints: before neoadjuvant treatment (CTC1) and after surgery/before adjuvant therapy (CTC2) for patients with localized disease, and before first-line chemotherapy (CTC1) and after 6 months (CTC2) for patients with metastases. VTE incidence was determined retrospectively. RFS was estimated by Kaplan-Meier analysis. Results: We evaluated 93 patients. According to Khorana scores, 63 (67.7%) patients were at intermediate and 30 (32.3%) were at high risk for VTE. VTE incidence was 20.4% and CTM were found in 39.8%. VTE developed in 7/37 (18.9%) CTM-positive and in 11/50 (22%) CTM-negative patients (p=0.93). When PLR >288, VTE occurred in 7/14 patients (p=0.005). PLR also associated with poor RFS (p<0.0001). CTC2 was associated with poor RFS (p<0.0001). CTC2, PLR and VTE were independent prognostic factors for RFS (p=0.005, 0.043, and <0.0001, respectively). Conclusion: PLR is a prognostic indicator for VTE and RFS in gastric cancer patients. Neither CTC, nor CTM improved risk stratification for VTE in our studied population. PLR, CTC2, and VTE were independent prognostic factors for RFS

Dynamic Risk Prediction of Treatment Discontinuation Using Patient-Reported Outcomes Data in the Phase III NSABP B-35 Trial.

Predicting an individual's risk of treatment discontinuation is critical for the implementation of precision chemoprevention. We developed partly conditional survival models to predict discontinuation of tamoxifen or anastrozole using patient-reported outcome (PRO) data from postmenopausal women with ductal carcinoma in situ (DCIS) enrolled in the NSABP B-35 clinical trial. In a secondary analysis of the NSABP B-35 clinical trial PRO data, we proposed two models for treatment discontinuation within each treatment arm (anastrozole or tamoxifen treated patients) using partly conditional Cox-type models with time-dependent covariates. A 70/30 split of the sample was used for the training and validation datasets. The predictive performance of the models was evaluated using calibration and discrimination measures based on the Brier score and area under the curve (AUC) from time-dependent receiver operating characteristics curves. The predictive models stratified high-risk versus low-risk early discontinuation at a 6-month horizon. For anastrozole-treated patients, predictive factors included baseline body mass index (BMI) and longitudinal patient-reported symptoms such as insomnia, joint pain, hot flashes, headaches, gynecologic symptoms, and vaginal discharge, all collected up to 12 months (Brier score 0.039, AUC 0.76, 95%CI 0.57-0.95). As for tamoxifen-treated patients, predictive factors included baseline BMI, and time-dependent covariates: cognitive problems, feelings of happiness, calmness, weight problems, and pain (Brier score 0.032, AUC 0.78, 95%CI 0.65-0.91). A real-time calculator based on these models was developed in Shiny to create a web-based application with a future goal to aid healthcare professionals in decision-making