A consistent nonparametric test of the effect of dementia duration on mortality

TL;DR

This paper develops a nonparametric test to assess the impact of dementia duration on mortality, accounting for duration dependence in a semi-Markovian multi-state model, and applies it to a large German dataset.

Contribution

It introduces a consistent nonparametric test for duration effects in semi-Markov models, with theoretical validation and application to dementia mortality data.

Findings

Significant effect of dementia duration on mortality was found.

The test remains robust across different bandwidth choices.

The model accounts for right-censoring in the data.

Abstract

A continuous-time multi-state history is semi-Markovian, if an intensity to migrate from one state into another, depends on the duration in the first state. Such duration can be formalised as covariate, entering the intensity process of the transition counts. We derive the integrated intensity process, prove its predictability and the martingale property of the residual. In particular, we verify the usual conditions for the respective filtration. As a consequence, according to Nielsen and Linton (1995), a kernel estimator of the transition intensity, including the duration dependence, converges point-wise at a slow rate, compared to the Markovian kernel estimator, i.e when ignoring dependence. By using the rate discrepancy, we follow Gozalo (1993) and show that the (properly scaled) maximal difference of the two kernel estimators on a random grid of points is asymptotically chi-square-1-distributed. As a data example, for a sample of 130,000 German women observed over a period of nine years, we model the mortality after dementia onset, potentially dependent on the disease duration. As usual, the models under both hypotheses need to be enlarged to allow for independent right-censoring. We find a significant effect of dementia duration, nearly independent of the bandwidth.