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Games can be rich environments for learning and can elicit evidence of students' conceptual understanding and inquiry processes. Illuminating students' content-specific gameplay decisions, or methods of completing game tasks related to a certain domain, requires a context that is open-ended enough for students to make choices that demonstrate their thinking. Doing this also requires rich log data and methods of Game Learning Analytics (GLA) that are granular enough to look at the specific choices most relevant to that context and domain. This paper presents research done on student exploration of high school level Mendelian genetics in a multiplayer online game called "The Radix Endeavor." The study uses three approaches to identify content-specific gameplay decisions and distinguish players utilizing different methods, looking at actions and tool use, play patterns and player types, and tool input patterns. In the context of the selected game quest, the three approaches were found to yield insights into different ways that students complete tasks in genetics, suggesting the potential for a set of more generalized guiding questions in the GLA field that could be adopted by learning games designers and data scientists to convey information about content-specific gameplay decisions in learning games.

OBJECTIVE:Determine the contribution of joint and skin improvements to health-related quality of life (HRQoL) in patients with psoriatic arthritis (PsA). METHODS:SPIRIT-P1 and SPIRIT-P2 are phase 3 trials investigating ixekizumab, an interleukin-17A antagonist, in the treatment of patients with active PsA. Patients were randomised to ixekizumab or placebo. Outcomes included the Disease Activity Index for Psoriatic Arthritis (DAPSA), the Psoriasis Area and Severity Index (PASI), the European Quality of Life-Five Dimensions (EQ-5D) Visual Analogue Score (VAS), the 36-Item Short-Form Health Survey (SF-36) and the Work Productivity and Activity Impairment (WPAI) Questionnaire. The contribution of joint and skin improvements to HRQoL was modelled using a smoothing spline method and depicted with response surface graphics. RESULTS:In this integrated analysis, 402 patients with PsA had baseline psoriasis of ≥3% of body surface area. We applied response surface modelling to this patient data set to investigate the relationship between DAPSA, PASI and HRQoL improvements at week 24. The greatest improvement in EQ-5D VAS was associated with the largest per cent improvements in both DAPSA and PASI together, rather than DAPSA or PASI alone. Similar observations were made in domains of SF-36 and WPAI. CONCLUSION:Optimal improvements in patients' HRQoL were dependent on successful treatment of both joint and skin symptoms.

PurposePrognostic models for overall survival (OS) for patients with metastatic castration-resistant prostate cancer (mCRPC) are dated and do not reflect significant advances in treatment options available for these patients. This work developed and validated an updated prognostic model to predict OS in patients receiving first-line chemotherapy.MethodsData from a phase III trial of 1,050 patients with mCRPC were used (Cancer and Leukemia Group B CALGB-90401 [Alliance]). The data were randomly split into training and testing sets. A separate phase III trial served as an independent validation set. Adaptive least absolute shrinkage and selection operator selected eight factors prognostic for OS. A predictive score was computed from the regression coefficients and used to classify patients into low- and high-risk groups. The model was assessed for its predictive accuracy using the time-dependent area under the curve (tAUC).ResultsThe model included Eastern Cooperative Oncology Group performance status, disease site, lactate dehydrogenase, opioid analgesic use, albumin, hemoglobin, prostate-specific antigen, and alkaline phosphatase. Median OS values in the high- and low-risk groups, respectively, in the testing set were 17 and 30 months (hazard ratio [HR], 2.2; P < .001); in the validation set they were 14 and 26 months (HR, 2.9; P < .001). The tAUCs were 0.73 (95% CI, 0.70 to 0.73) and 0.76 (95% CI, 0.72 to 0.76) in the testing and validation sets, respectively.ConclusionAn updated prognostic model for OS in patients with mCRPC receiving first-line chemotherapy was developed and validated on an external set. This model can be used to predict OS, as well as to better select patients to participate in trials on the basis of their prognosis.

BackgroundSeveral prognostic models for overall survival (OS) have been developed and validated in men with metastatic castration-resistant prostate cancer (mCRPC) who receive first-line chemotherapy. We sought to develop and validate a prognostic model to predict OS in men who had progressed after first-line chemotherapy and were selected to receive second-line chemotherapy.MethodsData from a phase III trial in men with mCRPC who had developed progressive disease after first-line chemotherapy (TROPIC trial) were used. The TROPIC was randomly split into training (n = 507) and testing (n = 248) sets. Another dataset consisting of 488 men previously treated with docetaxel (SPARC trial) was used for external validation. Adaptive least absolute shrinkage and selection operator selected nine prognostic factors of OS. A prognostic score was computed from the regression coefficients. The model was assessed on the testing and validation sets for its predictive accuracy using the time-dependent area under the curve (tAUC).ResultsThe nine prognostic variables in the final model were Eastern Cooperative Oncology Group performance status, time since last docetaxel use, measurable disease, presence of visceral disease, pain, duration of hormonal use, hemoglobin, prostate specific antigen, and alkaline phosphatase. The tAUCs for this model were 0.73 (95% confidence interval [CI] = 0.72 to 0.74) and 0.70 (95% CI = 0.68 to 0.72) for the testing and validation sets, respectively.ConclusionsA prognostic model of OS in the postdocetaxel, second-line chemotherapy, mCRPC setting was developed and externally validated. This model incorporates novel prognostic factors and can be used to provide predicted probabilities for individual patients and to select patients to participate in clinical trials on the basis of their prognosis. Prospective validation is needed.

PurposeProstate-specific antigen (PSA) kinetics, and more specifically a ≥ 30% decline in PSA within 3 months after initiation of first-line chemotherapy with docetaxel, are associated with improvement in overall survival (OS) in men with metastatic castration-resistant prostate cancer (mCRPC). The objective of this analysis was to evaluate post-treatment PSA kinetics as surrogates for OS in patients receiving second-line chemotherapy.Patients and methodsData from a phase III trial of patients with mCRPC randomly assigned to cabazitaxel plus prednisone (C + P) or mitoxantrone plus prednisone were used. PSA decline (≥ 30% and ≥ 50%), velocity, and rise within the first 3 months of treatment were evaluated as surrogates for OS. The Prentice criteria, proportion of treatment explained (PTE), and meta-analytic approaches were used as measures of surrogacy.ResultsThe observed hazard ratio (HR) for death for patients treated with C + P was 0.66 (95% CI, 0.55 to 0.79; P < .001). Furthermore, a ≥ 30% decline in PSA was a statistically significant predictor of OS (HR for death, 0.52; 95% CI, 0.43 to 0.64; P < .001). Adjusting for treatment effect, the HR for a ≥ 30% PSA decline was 0.50 (95% CI, 0.40 to 0.62; P < .001), but treatment remained statistically significant, thus failing the third Prentice criterion. The PTE for a ≥ 30% decline in PSA was 0.34 (95% CI, 0.11 to 0.56), indicating a lack of surrogacy for OS. The values of R(2) were < 1, suggesting that PSA decline was not surrogate for OS.ConclusionSurrogacy for any PSA-based end point could not be demonstrated in this analysis. Thus, the benefits of cabazitaxel in mediating a survival benefit are not fully captured by early PSA changes.

A major emphasis in precision medicine is to optimally treat subgroups of patients who may benefit from certain therapeutic agents. And as such, enormous resources and innovative clinical trials designs in oncology are devoted to identifying predictive biomarkers. Predictive biomarkers are ones that will identify patients that are more likely to respond to specific therapies and they are usually discovered through retrospective analysis from large randomized phase II or phase III trials. One important design to consider is the stratified biomarker design, where patients will have their specimens obtained at baseline and the biomarker status will be assessed prior to random assignment. Regardless of their biomarker status, patients will be randomized to either an experimental arm or the standard of care arm. The stratified biomarker design can be used to test for a treatment-biomarker interaction in predicting a time-to event outcome. Many biomarkers, however, are derived from tissues from patients, and their levels may be heterogeneous. As a result, biomarker levels may be measured with error and this would have an adverse impact on the power of a stratified biomarker clinical trial. We present a trial design and an analysis framework for the stratified biomarker design. We show that the naïve test is biased and provide bias-corrected estimators for computing the sample size and the 95% confidence interval when testing for a treatment-biomarker interaction in predicting a time to event outcome. We propose a sample size formula that adjusts for misclassification and apply it in the design of a phase III clinical trial in renal cancer. [ABSTRACT FROM AUTHOR]

There is limited real-world evidence on using ixekizumab in psoriasis patients. Therefore, we characterized patients with psoriasis initiating ixekizumab and report 6-month changes in disease and patient-reported outcomes. Adult patients with psoriasis who initiated ixekizumab and completed a 6-month follow-up visit were enrolled from the Corrona Psoriasis Registry. Disease characteristics and outcomes were assessed at ixekizumab initiation. Outcomes included the mean 6-month change in Psoriasis Area and Severity Index (PASI), body surface area (BSA), Investigator Global Assessment (IGA), and IGA*BSA. From baseline to follow-up in all patients (n = 136), means decreased for IGA*BSA (−45.5) and BSA (−12.4), and a higher % achieved an absolute PASI ≤ 5 (84.6%), BSA 0–3 (72.1%), and IGA 0/1 (50.7%). Within stratified groups, means decreased for PASI <12 for IGA*BSA (−21.1) and BSA (−6.3); PASI≥12 for IGA*BSA (−94.8) and BSA (−24.6); weight <100 kg for IGA*BSA (−45.1) and BSA (−12.4); weight ≥100 kg for IGA*BSA (−46.2) and BSA (−12.3); concomitant PsA for IGA*BSA (−56.0) and BSA (−15.3); and in no concomitant PsA for IGA*BSA (−36.9) and BSA (−10.0). We provide real-world evidence on the benefits of ixekizumab for treating psoriasis, regardless of baseline disease severity, weight, or concomitant PsA. [ABSTRACT FROM AUTHOR]

Psoriasis is characterized by thick and scaly plaques. The Dermatology Life Quality Index (DLQI) and Physician Global Assessment (PGA) can be used to define its severity. To assess the impact of complete clearance of skin versus almost clear skin across various disease measures. Data were collected in a survey of US dermatologists and patients with psoriasis from November 2016–January 2017. Dermatologists completed a 6-point PGA (0 = clear skin, 1 = almost clear skin). Patients completed the DLQI and Work Productivity and Activity Impairment questionnaire (WPAI). Patients with clear and almost clear skin were compared using analysis of covariance for continuous variables, and multivariate logistic regression analysis for categorical variables. Data for 99 and 160 patients with clear and almost clear skin, respectively, were included in the analyses. Patients with clear skin reported less frequent and lower intensity itching, lower total DLQI score (indicating better health-related quality of life), and less impairment of overall work productivity than patients with almost clear skin (all: p < 0.05). Limitations relating to general survey methodology. Patients perceived a meaningful difference between clear and almost clear skin. Clear skin is now a realistic treatment target with newer biologics approved in psoriasis. [ABSTRACT FROM AUTHOR]