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Updated: Jun 2, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Published on: October 23, 2020

Confidence interval construction for multivariable truncated spline logistic model (MTSLM).

Afiqah Saffa Suriaslan¹, I Nyoman Budiantara¹, Vita Ratnasari¹

¹Departement of Statistics, Institut Teknologi Sepuluh Nopember, Kampus ITS-Sukolilo, Surabaya 60111, Indonesia.

|June 1, 2026

Summary

This summary is machine-generated.

This study introduces confidence intervals for the Multivariable Truncated Spline Logistic Model (MTSLM), enhancing nonlinear relationship modeling. The MTSLM method offers reliable predictor estimates for Human Development Index (HDI) analysis.

Keywords:

Convidence interval Human development Index Logistic regression Pivotal quantity Truncated spline

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Area of Science:

Statistics
Econometrics
Public Health

Background:

Conventional logistic regression struggles with nonlinear relationships.
Flexible modeling is crucial for accurately assessing complex predictor effects.
The Multivariable Truncated Spline Logistic Model (MTSLM) offers a potential solution.

Purpose of the Study:

To develop and validate confidence intervals for the MTSLM.
To assess the model's performance in capturing nonlinear relationships.
To apply the MTSLM to analyze factors influencing the Human Development Index (HDI).

Main Methods:

Construction of confidence intervals within the MTSLM framework.
Utilizing truncated spline functions for flexible nonlinear modeling.
Validation through a simulation study and empirical analysis of Indonesian HDI data.

Main Results:

The MTSLM successfully models nonlinear effects of predictors like unemployment and school enrollment.
Confidence intervals provide insights into the reliability of predictor effects (e.g., clean water access, poverty).
The MTSLM demonstrates superior performance compared to the binary logistic model.

Conclusions:

The MTSLM provides accurate and reliable estimates for predictors influencing HDI.
The developed confidence intervals enhance the interpretability of nonlinear effects.
MTSLM is a valuable tool for analyzing complex socioeconomic and infrastructural factors impacting development indicators.