Intensive longitudinal data (ILD) are commonly used to study within-person processes, and psychometric methods for computing within-person reliability with ILD have recently appeared in the methodological literature. The dense, frequent nature of data collection in ILD often leads to copious amounts of missing data-recent reviews find that 30%-40% missing data rates are typical. However, given the nascent state of psychometrics for ILD, the intersection of psychometrics and missing ILD has yet to be explored despite the pervasiveness of missing ILD in empirical studies. In addition, given that ILD are frequently used to study sensitive topics such as mental health and substance use, there is a heightened risk of missing not at random (MNAR) data. That is, the reason data are missing is associated with what the value would have been (e.g., depression responses are missing when a person is momentarily too depressed to respond). The goal of this article is to (a) clarify how missing values affect estimates of within-person reliability with ILD and (b) to extend one recently proposed method (the measurement error autoregressive model) with a Diggle-Kenward selection process to evaluate sensitivity to a potential MNAR mechanism. Simulations in the article find that the accuracy of within-person reliability estimates from standard approaches can deteriorate when missing data are present, but the proposed model can better recover population within-person reliability-even with a large amount of MNAR data-under the conditions and missingness mechanisms studied. Limitations and extensions to other methods for computing within-person reliability are also discussed. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Missing data are pervasive in psychological and educational assessments. Naive remedies, including listwise deletion and item-mean imputation, often degrade research validity and misinform subsequent decisions. Recent advances in artificial neural networks have demonstrated their efficacy in prediction-related tasks by using observed features to infer unknown values. Building on this potential, we propose the columnwise neural imputation (COLNI) algorithm to impute missing ordinal responses in psychometric data. Simulation studies demonstrated that, when benchmarked against conventional methods, COLNI more accurately recovered item means, inter-item correlations, and person and item parameters under the multidimensional graded response model. We further evaluated COLNI using data from the Short Dark Triad test, confirming its effectiveness in a multidimensional empirical setting. We conclude with implementation guidelines and avenues for refining and extending this artificial neural network-based imputation approach in future research. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
There is an increasing call for individualized treatment rules, which leverage individual patient characteristics to recommend treatments or interventions, tailoring recommendations based on their covariates. This is particularly of interest for the care of conditions such as depression, for which many treatment options are available with similar average effectiveness but with large heterogeneity in individual responses. In parallel, there has been a growing interest in machine learning methods for causal inference and variable selection. We compared several strategies for variable adjustment in a dynamic marginal structural modeling approach to estimating an optimal individualized treatment rule and investigated the performance of outcome adaptive lasso, group lasso and doubly robust estimation, double-index propensity score, the high-dimensional balancing propensity score, and the causal ball lasso as variable selection methods for the propensity score. Our results demonstrate that these all provided similar unbiased estimates. However, methods differed in their ability to exclude extraneous variables and in computational burden. We found statistical efficiency is gained when variable selection approaches were for the propensity score were used and by including variables in the outcome model. We applied all methods to determine the optimal treatment rule, treating with either selective serotonin reuptake inhibitors or serotonin and norepinephrine reuptake inhibitors, for unipolar depression in individuals aged 13 years and older. This analysis, which used electronic health records from 74,058 Kaiser Permanente Washington patients with a new antidepressant dispensing between 2008 and 2018, suggested tailoring treatment based on baseline symptom severity did not impact symptom severity 6 months later. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
