Background Fried et al described a frailty phenotype measured in the Cardiovascular Health Study (CHS). This phenotype is manifest when 3 or more of the following are present: low grip strength, low energy, slowed waking speed, low physical activity, or unintentional weight loss. We sought to approximate frailty phenotype using only administrative claims data in order to enable frailty to be assessed without physical performance measures. Study Design We used the CHS cohort data linked to participants Medicare claims. The reference standard was the frailty phenotype measured at visits 5 and 9. With penalized logistic regression, we developed a parsimonious index for predicting the frailty phenotype using a linear combination of diagnoses, operationalized with claims data. We assessed the predictive validity of frailty index by examining how well it predicted common aging-related outcomes including hospitalization, disability and death. Results There were 4,454 CHS participants from 4 clinical sites. 84% were white, 58% were women and their mean age was 72 years at enrollment. Approximately 11% of the cohort was frail. The model had an area under the ROC curve of 0.75 to concurrently predict a frailty phenotype. This Claims-based Frailty Index (CFI) significantly predicted death [Odds-Ratio 1.84], time to death [Hazard-Ratio 1.71], number of hospital admissions [Incidence Rate-Ratio 1.74], and nursing home admission [OR 1.47] in models adjusted for age and sex. Conclusions Claims data alone can be used to classify individuals as frail and non-frail. The CFI might be used in research with large datasets for confounding adjustment or risk prediction. The indicator might also be used for emergency preparedness for identification of regions enriched with frail individuals.

Summary Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest—namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial—ENTHUSE M1—in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39–4·62, p<0·0001; reference model: 2·56, 1·85–3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified...

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This paper set out to investigate the ways training and development impacts on firm innovative performance as well as the moderating role of knowledge transfer received on training and development and firm innovative performance, using a sample of 229 foreign subsidiaries of multinational enterprises (MNEs) operating in China. We adopt hierarchical linear modeling, and the results of the study reveal that both on-the-job training and development and off-the-job training and development have an influence on the firm innovative performance. Also, the empirical results of the study demonstrate that the moderating effect of tacit and explicit knowledge received on the relationship between both on-the-job training and development and off-the-job training and development and firm innovative performance are stronger. This paper shows that continues updating of skills, ability and knowledge of the workforce through on-the job training and development and off-the job training development programs might boost creativity and innovation of business organizations.

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Although there is a general recognition that human resource management (HRM) practices are a key factor in a firm’s innovation performance, from the extant literature we see that empirical investigation does not always offer evidence to support this assertion. This paper proposes that knowledge transfer positively moderates the link between HRM practices and firm innovative performance in foreign multinational enterprises (MNEs) subsidiaries operating in China. Specifically, a tacit knowledge approach affects knowledge transfer in HRM practice as well as firm innovative performance. With hierarchical linear modeling, our results demonstrate that: (1) HRM practices can have a positive effect on firm innovative performance; (2) knowledge transfer can positively moderate the effects of HRM practices and firm innovative performance; and (3) the positive effects of knowledge received on HRM practices and firm innovative performance will be strengthened with a tacit knowledge approach. This paper shows that through the adaptation and application of a tacit knowledge approach, executives can create an atmosphere for interaction between the workforce and senior colleagues, forming an inter-intra network chain to share intangible knowledge that is mostly derived from the culture and experience.

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