RECENT CASE NOTES

<p><i>Notes</i>: H-sites and L-sites refer to high-tide-zone sites and low-tide-zone sites, respectively.</p><p>Results of <i>t</i>-test for effects of different stands on aboveground net primary production (ANPP), soil pH, total carbon (TC), organic and inorganic carbon (SOC and SIC, 0–100 cm), and SMBC.</p

Data_Sheet_1_Exercise or lie down? The impact of fitness app use on users' wellbeing.docx

IntroductionThe use of fitness apps is becoming more and more widespread, and its impact on people's well-being has received more and more attention.MethodsThe relationship between fitness app use and users' well-being and the influence mechanism was explored using structural equation modeling with upward social comparison as the mediating variable and self-control as the moderating variable.ResultsThe questionnaire survey of 1,452 fitness app users over 18 years old shows that: (1) fitness app use is associated with users' well-being; (2) upward social comparison plays a mediating role in the relationship between fitness app use and users' well-being; (3) self-control has a moderating effect on the relationship between fitness app use and users' well-being.DiscussionSelf-control plays a significant moderating role between social comparison and well-being, upward social comparison can improve the well-being of high self-control users but reduce the well-being of low self-control users.</p

Joint Inference for Competing Risks Survival Data

<p>This article develops joint inferential methods for the cause-specific hazard function and the cumulative incidence function of a specific type of failure to assess the effects of a variable on the time to the type of failure of interest in the presence of competing risks. Joint inference for the two functions are needed in practice because (i) they describe different characteristics of a given type of failure, (ii) they do not uniquely determine each other, and (iii) the effects of a variable on the two functions can be different and one often does not know which effects are to be expected. We study both the group comparison problem and the regression problem. We also discuss joint inference for other related functions. Our simulation shows that our joint tests can be considerably more powerful than the Bonferroni method, which has important practical implications to the analysis and design of clinical studies with competing risks data. We illustrate our method using a Hodgkin disease data and a lymphoma data. Supplementary materials for this article are available online.</p

Classification tree of ING4, Cul1, BRG1 and Bim biomarkers for dysplastic nevi and melanoma.

<p>Nevi, dysplastic nevi; PM+MM, primary and metastatic melanoma.</p

Discrimination of melanoma from dysplastic nevi via multiple logistic regressions.

<p>Discrimination of melanoma from dysplastic nevi via multiple logistic regressions.</p

Architecture and performance of ANN. (a)

<p>ANN architecture. The network consisted of three layers: Input (boxes 1–12), hidden (circles 1–6) and output (group 1: dysplastic nevi, group 2: melanoma) layer, respectively. (b) ANN predicted-by-observed performance chart. The box plots represent the predicted-pseudo-probabilities for the output category; dysplastic nevi (blue) and melanoma (green) plotted against the known clinical status for dysplastic nevi and melanoma. (c) The ROC curve for dysplastic nevi and melanoma separately.</p

ROC curve for 4-markers (ING4-Cul1-BRG1-Bim, purple curve), 3-markers (ING4-Cul1- BRG1, green curve) and 2-marker (ING4-Cul1, blue curve).

<p>ROC curve for 4-markers (ING4-Cul1-BRG1-Bim, purple curve), 3-markers (ING4-Cul1- BRG1, green curve) and 2-marker (ING4-Cul1, blue curve).</p

Discrimination of melanoma from dysplastic nevi using individual marker via univariate logistic regression analysis.

<p>0, negative; 1, weak; 2, moderate; 3, strong.</p

Diagnostic accuracy for melanoma via sensitivity and specificity of each individual marker.

<p>Diagnostic accuracy for melanoma via sensitivity and specificity of each individual marker.</p

Classification tree for ING4, Cul1 and BRMS1 biomarkers for dysplastic nevi and metastatic melanoma.

<p>Nevi, dysplastic nevi; MM, metastatic melanoma.</p