Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Indonesian Journal of Applied Statistics Indonesian Journal of Statistics and Its Applications
Nemes, Szilard
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science Mathematics Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
HAZARD RATES AND RESTRICTED MEAN SURVIVAL TIME Nemes, Szilard
Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i3.520

Abstract

Restricted Mean Survival Time (RMST) is well-established, but underutilized measure that can be interpreted as the average event-free survival time up to a pre-specified time point. In the last decade RMST received substantial attention and was advocated as an alternative for the Hazard Rate when the proportionality assumption is not met. Currently studies with time-to-evet outcomes routinely report survival curves and hazard rates. Research planning assumes extraction of comparative effect measures and variances that facilitates sample size calculations. Here we assessed the possibility of extracting clinically meaningful effect size estimates for RMST based research plans from studies that report survival curves and hazard rates. This assessment was based on simulations using Exponential and Weibull distributions. The simulations suggest that under certain conditions meaningful RMST effect size estimates can be extrapolated form published hazard rates. However, in cases when the proportionality assumption is in doubt (i.e. when RMST have most utility) extraction of meaningful estimates is not feasible.
Exploring Statistical Power and Mediation Analysis: Understanding the Impact of Antecedent-Mediator-Outcome Relationships Nemes, Szilárd
Indonesian Journal of Applied Statistics Vol 8, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v8i2.93376

Abstract

This paper explores the phenomenon of statistical power stagnation and decline in mediation analysis, specifically focusing on the interplay between the antecedent variable, mediator, and outcome. Mediation analysis is a critical statistical tool used to understand the causal pathways between variables. However, statistical power may not always increase with stronger relationships between the antecedent and mediator, often stagnating or even declining due to variance inflation caused by multicollinearity. We provide a in detail examination of this issue, including key theoretical concepts, the mathematical foundations of variance inflation, and the impact of mediator-antecedent correlations on power. A simulation study further illustrates how varying these correlations affects statistical power, variance estimates, and possible bias in mediation effects. Our findings indicate that while increasing the strength of the relationship between the antecedent and mediator improves mediation detection initially, beyond a certain threshold, it results in inflated variance estimates, leading to decreased precision and power. Variance inflation of the mediated effect is more accentuated than variance inflation of regression coefficients.Keywords: mediation; variance inflation; Sobel test
Co-Authors