Search

Supplemental material, sj-pdf-1-smm-10.1177_09622802231163331 for A multistate approach for the study of interventions on an intermediate time-to-event in health disparities research by Linda Valeri, Cecile Proust-Lima, Weijia Fan, Jarvis T. Chen and Helene Jacqmin-Gadda in Statistical Methods in Medical Research

Supplemental material, sj-pdf-1-smm-10.1177_09622802231163331 for A multistate approach for the study of interventions on an intermediate time-to-event in health disparities research by Linda Valeri, Cecile Proust-Lima, Weijia Fan, Jarvis T. Chen and Helene Jacqmin-Gadda in Statistical Methods in Medical Research

International audience; Dementia is a major public health issue worldwide and chronic use of benzodiazepine, which is very frequent in northern countries, was found to be a risk factor of dementia. This work aims at evaluating the impact of a reduction in chronic use of benzodiazepine on the future burden of dementia in France. Using estimations of dementia incidence and of benzodiazepine use and nation-wide projections of mortality and population sizes, a Monte Carlo approach based on an illness-death model provided projections of several indicators of dementia burden. With no change in benzodiazepine consumption, the prevalence of dementia between age 65 and 99 in France in 2040 was estimated at 2.16 millions (95% confidence interval (CI) 1.93–2.38), with a life expectancy without dementia at 65 years equal to 25.0 years (24.7–25.3) for women and 23.8 years (23.5–24.2) for men. Assuming a disappearance of chronic use of benzodiazepine in 2020, the prevalence would be reduced by about 6.6% in 2040 and the life expectancy without dementia would increase by 0.99 (0.93–1.06) year among women and 0.56 (0.50–0.62) among men. To conclude, a modest but significant reduction in future dementia burden could be obtained by applying current recommendation for duration of benzodiazepine use.

International audience

Figure S1. Multiple Correspondence Analysis of Social, Physical, and Intellectual Engagement Variables. Figure S2. Flowchart describing the baseline Paquid population. Figure S3. Schematic of the joint latent class mixed model of cognitive change and dementia. Supplementary Methods. (DOCX 268 kb)

This article studies a Newton-like method already used by several authors but which has not been thouroughly studied yet. We call it the robust-variance scoring (RVS) algorithm because the main version of the algorithm that we consider replaces minus the Hessian of the loglikelihood used in the Newton-Raphson algorithm by a matrix $G$ which is an estimate of the variance of the score under the true probability, which uses only the individual scores. Thus an iteration of this algorithm requires much less computations than an iteration of the Newton-Raphson algorithm. Moreover this estimate of the variance of the score estimates the information matrix at maximum. We have also studied a convergence criterion which has the nice interpretation of estimating the ratio of the approximation error over the statistical error; thus it can be used for stopping the iterative process whatever the problem. A simulation study confirms that the RVS algorithm is faster than the Marquardt algorithm (a robust version of the Newton-Raphson algorithm); this happens because the number of iterations needed by the RVS algorithm is barely larger than that needed by the Marquardt algorithm while the computation time for each iteration is much shorter. Also the coverage rates using the matrix $G$ are satisfactory., Comment: 27 pages, 2 Tables

International audience; We propose a joint model for cognitive decline and risk of dementia to describe the pre-diagnosis phase of dementia. We aim to estimate the time when the cognitive evolution of subjects in the pre-dementia phase becomes distinguishable from normal evolution and to study whether the shape of cognitive decline depends on educational level. The model combines a piecewise polynomial mixed model with a random change point for the evolution of the cognitive test and a log-normal model depending on the random change point for the time to dementia. Parameters are estimated by maximum likelihood using a Newton-Raphson-like algorithm. The expected cognitive evolution given age to dementia is then derived and the marginal distribution of dementia is estimated to check the log-normal assumption.