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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Published on: October 23, 2020

Determining disease prevalence from incidence and survival using simulation techniques.

Simon Crouch¹, Alex Smith¹, Dan Painter¹

¹Epidemiology & Cancer Statistics Group, Department of Health Sciences, University of York, YO10 5DD, UK.

Cancer Epidemiology

|March 25, 2014

Summary

This study introduces a flexible Monte Carlo simulation method for calculating disease prevalence and its precision using incidence and survival data. The approach also determines the distribution of subject-level covariates in prevalent populations.

Keywords:

Incidence Monte-Carlo Prevalence Prevalence distribution Simulation Survival

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Area of Science:

Epidemiology
Biostatistics
Computational Biology

Background:

Accurate disease prevalence estimation is crucial for public health planning and resource allocation.
Traditional methods for prevalence estimation often lack flexibility and do not readily incorporate covariate data.

Purpose of the Study:

To develop and validate a novel Monte Carlo simulation technique for estimating disease prevalence and its precision.
To enable the calculation of covariate distributions within prevalent disease populations.

Main Methods:

Modeling disease incidence as a marked stochastic process and performing simulations.
Calculating the probability of remaining in the prevalent sub-population using bootstrapped survival curves.
Applying the algorithm to estimate prevalence and covariate distributions using Haematological Malignancy Research Network data for acute myeloid leukaemia.

Main Results:

The Monte Carlo simulation method provides flexible prevalence estimates with associated precision.
The distribution of subject-level covariates within the prevalent population can be accurately determined.
The method successfully estimated the prevalence of acute myeloid leukaemia and its precision.

Conclusions:

Monte Carlo simulation offers a more adaptable approach to prevalence estimation compared to traditional methods.
The technique automatically provides precision estimates and allows for the analysis of covariate distributions.
The method effectively handles temporal changes in disease incidence and survival rates.