Conditional power, predictive power and probability of success in clinical trials with continuous, binary and time-to-event endpoints

Assessment of study success using conditional power (CP), the predictive power of success (PPoS) and probability of success (PoS) is becoming increasingly common for resource optimization and adaption of trials in clinical investigation. Determination of these measures is often a non-trivial mathematical task. Further, the terminologies used across the literature are not consistent, and there is no consolidated presentation on this. Lastly, certain types of trials received more attention where others (e.g., single-arm trial with time-to-event endpoints) were completely ignored. We attempted to fill these gaps. This paper first provides a detailed derivation of CP, PPoS and PoS in a general setting with normally distributed test statistics and normal prior. Subsequently, expressions for these measures are obtained for continuous, binary, and time-to-event endpoints in single-arm and two-arm trial settings. We have discussed both clinical success and trial success. Importantly, we have derived the expressions for CP, PPoS and PoS in a single-arm trial with a time-to-event endpoint that was never addressed in the literature to our knowledge. In that discussion, we have also shown that commonly recommended 1/d consistently under-estimates the variance of log(median) and alternative expression for variance was derived. We have also presented the PPoS calculation for the binomial endpoint with a beta prior. Examples are given along with the comparison of CP and PPoS. Expressions presented in this paper are implemented in LongCART package in R. An R shiny app is also available at https://ppos.herokuapp.com/ .

The Role of Parents in Blended Learning Towards Student Character Values in The Covid-19 Pandemic

The Covid-19 pandemic hampers the cultivation of character values ​​in students, therefore it is necessary for the role of parents in Blended Learning to instill character values. This research is a quantitative research with sampling technique using stratified proportional random sampling. The results of the Shapiro-Wilk normality test showed that the significance value of the parent's role was 0.157 > 0.05. The significance value for the student character is 0.280 > 0.05 so that the data is normally distributed. Test for linearity sig 0.305 > 0.05 so that the variables X and Y are linear. The results of the hypothesis test are 0.000 <0.05, and the Pearson Correlation value is 0.600, then Ha is accepted and H0 is rejected. So it can be concluded that there is a strong relationship between the role of parents in blended learning and the cultivation of character values ​​during the Covid-19 pandemic.

PERAN KONTROL DIRI DALAM MOTIVASI BERPRESTASI DAN PERFEKSIONISME TERHADAP PROKRASTINASI AKADEMIK

This reseach aims to examined the role of Self-Control as a mediating variable in the effect of Academic Perfection on Academic Procrastination. Quantitative method was used with data collection techniques by online questionnaire’s. Population in this research were students of Economics Education Faculty of Economics UNJ on 2013-2016 all concentration which they currently doing their S1 thesis. 145 people to be the sample were counted with proportional random sampling techniques. Techniques of data analysis used test for requirement analysis, test for hyphothesis and path analysis. The result of test for requirements analysis showed that data was normal and linearly distributed. Test for hypothesis result concluded all of them were accepted and path analysis result showed Self-Control was able to mediate dependents and independents variables. The ability of Achievement Motivation, Perfectionism and Self-Control to define Academic Procrastination are 31,4%, while the rest were influenced by other variables that are not used. From this research concluded that were need for developing interpersonal skill to avoid procrastination behavior specially on academic realm.

Characteristic spatial scales of SARS-CoV-2 pandemics: lessons from mass rapid antigen testing in Slovakia

Mass antigen testing in Slovakia conducted in October and November 2020 is a source of important data. We perform its statistical analysis and study epidemic geographical patterns. We observe exponentially distributed test positivity and exponential trends in its geographical distribution, and its approximately 10 km spatial characteristic correlation length. A small correlation between positivity in two consecutive testing rounds appeared on the municipalities level but it significantly increased on the counties level. Recent 7-day PCR tests incidence per capita served as a good proxy for antigen test positivity. Positivity of non-residents was higher than of residents when mass testing was offered only in municipalities with the highest positivity in previous rounds. Reduction in positivity in repeated testing increased with the positivity in the earlier round. Our results contribute to better understanding of pandemic data, and aid an assessment of mass testing efficiency, and planning of mitigation measures.

Virtual shaft: Robust coupling by bidirectional and distributed prediction of coupling values

Shifting automotive powertrain development tasks to earlier phases (frontloading) increases efficiency by utilizingtest-benches as opposed to prototype vehicles (road-to-rig approach). The coupling of distributed test-benches by a virtualized shaft connection is required to reproduce interactions of automotive powertrain components. A coupling algorithm simulates a rigid connection by synchronizing the torque and speed of two distributed test-bench’s electric motors. System dead-times lead to limited stability and reduced bandwidth of the coupling algorithm. In this study, a method for a stable bidirectional coupling of speed and torque of both subsystems is described analytically and verified by simulation. All component models are calibrated based on measurements using state-of-the-art test-bench equipment. A distributed prediction algorithm is proposed for the dead-time compensation. Four Kalman predictors estimate the coupling values of both subsystems at wall-clock-time without measurement and communication latencies. A detailed drive cycle analysis is performed through simulation. This enables a Virtual Shaft Algorithm to achieve a higher bandwidth and an improved coupling robustness.