Semiparametric Inference for Nonmonotone Missing-Not-at-Random Data: The No Self-Censoring Model

Analysis of Missingness Scenarios for Observational Health Data

Despite the extensive literature on missing data theory and cautionary articles emphasizing the importance of realistic analysis for healthcare data, a critical gap persists in incorporating domain knowledge into the missing data methods. In this paper, we argue that the remedy is to identify the key scenarios that lead to data missingness and investigate their theoretical implications. Based on this proposal, we first introduce an analysis framework where we investigate how different observation agents, such as physicians, influence the data availability and then scrutinize each scenario with respect to the steps in the missing data analysis. We apply this framework to the case study of observational data in healthcare facilities. We identify ten fundamental missingness scenarios and show how they influence the identification step for missing data graphical models, inverse probability weighting estimation, and exponential tilting sensitivity analysis. To emphasize how domain-informed analysis can improve method reliability, we conduct simulation studies under the influence of various missingness scenarios. We compare the results of three common methods in medical data analysis: complete-case analysis, Missforest imputation, and inverse probability weighting estimation. The experiments are conducted for two objectives: variable mean estimation and classification accuracy. We advocate for our analysis approach as a reference for the observational health data analysis. Beyond that, we also posit that the proposed analysis framework is applicable to other medical domains.

Online psychoeducation and digital assessments as a first step of treatment for borderline personality disorder: A protocol for a pilot randomized controlled trial

BACKGROUND

Treatment trials for borderline personality disorder (BPD) have consistently demonstrated that approaches that are diagnostically tailored are superior to those which are not. Currently, gold standard treatments for BPD are highly intensive, lengthy, and specialized, leading to a critical gap between the supply and demand of effective, evidence-based treatment for patients who receive a diagnosis of BPD. Psychoeducation, which is a common component of most treatments known to be effective, is a low-cost, low-burden intervention proven to relieve symptoms. The present study builds on psychoeducation research, assessing online video prescriptions as a means of disseminating information patients need to know about their diagnosis and care.

METHODS

This article presents the study protocol for a safety, feasibility, and preliminary efficacy trial of psychoeducational video prescriptions and online assessment with feedback for newly diagnosed individuals with BPD. We aim to recruit 100 adults recently diagnosed with BPD to be randomly assigned to receive videos about BPD or videos about non-BPD mental health topics that are matched in length in the first step of the study. All participants will complete daily surveys about their emotions, interpersonal interactions, and behaviors, as well as self-report assessments and cognitive tests at 4 different time points. Half of the participants in the intervention group will receive feedback on their symptom ratings and cognitive test performance to assess whether there is incremental value in tailoring this online set of interventions with individualized feedback unique to each participant. This study aims to assess the effects of BPD-focused psychoeducational videos with and without personalized feedback, on BPD and depressive symptom severity as well as core mechanisms of the disorder such as loneliness, rejection sensitivity, cognitive control difficulties, and self-clarity. Results will inform efforts to progress to a larger, more definitive trial.

TRIAL REGISTRATION

Clinical trials registration: The protocol is registered with ClinicalTrials.gov NCT05358925.

A self-censoring model for multivariate nonignorable nonmonotone missing data

We introduce an itemwise modeling approach called "self-censoring" for multivariate nonignorable nonmonotone missing data, where the missingness process of each outcome can be affected by its own value and associated with missingness indicators of other outcomes, while conditionally independent of the other outcomes. The self-censoring model complements previous graphical approaches for the analysis of multivariate nonignorable missing data. It is identified under a completeness condition stating that any variability in one outcome can be captured by variability in the other outcomes among complete cases. For estimation, we propose a suite of semiparametric estimators including doubly robust estimators that deliver valid inferences under partial misspecification of the full-data distribution. We also provide a novel and flexible global sensitivity analysis procedure anchored at the self-censoring. We evaluate the performance of the proposed methods with simulations and apply them to analyze a study about the effect of highly active antiretroviral therapy on preterm delivery of HIV-positive mothers.

Implementing and validating a home-infusion central-line-associated bloodstream infection surveillance definition

OBJECTIVE

Central-line-associated bloodstream infection (CLABSI) surveillance in home infusion therapy is necessary to track efforts to reduce infections, but a standardized, validated, and feasible definition is lacking. We tested the validity of a home-infusion CLABSI surveillance definition and the feasibility and acceptability of its implementation.

DESIGN

Mixed-methods study including validation of CLABSI cases and semistructured interviews with staff applying these approaches.

SETTING

This study was conducted in 5 large home-infusion agencies in a CLABSI prevention collaborative across 14 states and the District of Columbia.

PARTICIPANTS

Staff performing home-infusion CLABSI surveillance.

METHODS

From May 2021 to May 2022, agencies implemented a home-infusion CLABSI surveillance definition, using 3 approaches to secondary bloodstream infections (BSIs): National Healthcare Safety Program (NHSN) criteria, modified NHSN criteria (only applying the 4 most common NHSN-defined secondary BSIs), and all home-infusion-onset bacteremia (HiOB). Data on all positive blood cultures were sent to an infection preventionist for validation. Surveillance staff underwent semistructured interviews focused on their perceptions of the definition 1 and 3-4 months after implementation.

RESULTS

Interrater reliability scores overall ranged from κ = 0.65 for the modified NHSN criteria to κ = 0.68 for the NHSN criteria to κ = 0.72 for the HiOB criteria. For the NHSN criteria, the agency-determined rate was 0.21 per 1,000 central-line (CL) days, and the validator-determined rate was 0.20 per 1,000 CL days. Overall, implementing a standardized definition was thought to be a positive change that would be generalizable and feasible though time-consuming and labor intensive.

CONCLUSIONS

The home-infusion CLABSI surveillance definition was valid and feasible to implement.

Multiple imputation for non-monotone missing not at random data using the no self-censoring model

Although approaches for handling missing data from longitudinal studies are well-developed when the patterns of missingness are monotone, fewer methods are available for non-monotone missingness. Moreover, the conventional missing at random assumption-a natural benchmark for monotone missingness-does not model realistic beliefs about the non-monotone missingness processes (Robins and Gill, 1997). This has provided the impetus for alternative non-monotone missing not at random mechanisms. The "no self-censoring" model is such a mechanism and assumes the probability an outcome variable is missing is independent of its value when conditioning on all other possibly missing outcome variables and their missingness indicators. As an alternative to "weighting" methods that become computationally demanding with increasing number of outcome variables, we propose a multiple imputation approach under no self-censoring. We focus on the case of binary outcomes and present results of simulation and asymptotic studies to investigate the performance of the proposed imputation approach. We describe a related approach to sensitivity analysis to departure from no self-censoring. We discuss the relationship between missing at random and no self-censoring and prove that one is not a special case of the other. Finally, we discuss extensions to non-binary data settings. The proposed methods are illustrated with application to a substance use disorder clinical trial.

Methods for handling missing binary data in substance use disorder trials

Missing data are a ubiquitous problem in longitudinal substance use disorder (SUD) clinical trials. In particular, the rates of missingness are often high and study participants often intermittently skip their scheduled outcome assessments, leading to so-called "non-monotone" missing data patterns. Moreover, when the primary outcome is a measure of substance use, study investigators often have strong prior beliefs based on their clinical experience that those participants with missing data are more likely to be using substances at those occasions, i.e., data are missing not at random (MNAR). Although approaches for handling missing data are well-developed when the missing data patterns are monotone, arising primarily from study participants withdrawing from the trial prematurely, fewer methods are available for non-monotone missingness. In this paper we review some conventional, as well as more novel, methods for handling non-monotone missingness in SUD trials when the repeatedly measured outcome variable is binary (e.g., denoting presence/absence of substance use). We compare and contrast the different approaches using data from a longitudinal clinical trial of four psychosocial treatments from the Collaborative Cocaine Treatment Study. We conclude by making some recommendations to the SUD research community concerning how more principled methods for handling missing data can be incorporated in the analysis and reporting of trial results.

Controlling the chaos: Information management in home-infusion central-line-associated bloodstream infection (CLABSI) surveillance

Objectives

Access to patient information may affect how home-infusion surveillance staff identify central-line-associated bloodstream infections (CLABSIs). We characterized information hazards in home-infusion CLABSI surveillance and identified possible strategies to mitigate information hazards.

Design

Qualitative study using semistructured interviews.

Setting and participants

The study included 21 clinical staff members involved in CLABSI surveillance at 5 large home-infusion agencies covering 13 states and the District of Columbia. Methods: Interviews were conducted by 1 researcher. Transcripts were coded by 2 researchers; consensus was reached by discussion.

Results

Data revealed the following barriers: information overload, information underload, information scatter, information conflict, and erroneous information. Respondents identified 5 strategies to mitigate information chaos: (1) engage information technology in developing reports; (2) develop streamlined processes for acquiring and sharing data among staff; (3) enable staff access to hospital electronic health records; (4) use a single, validated, home-infusion CLABSI surveillance definition; and (5) develop relationships between home-infusion surveillance staff and inpatient healthcare workers.

Conclusions

Information chaos occurs in home-infusion CLABSI surveillance and may affect the development of accurate CLABSI rates in home-infusion therapy. Implementing strategies to minimize information chaos will enhance intra- and interteam collaborations in addition to improving patient-related outcomes.

Analysis of local sensitivity to nonignorability with missing outcomes and predictors

The ISNI (index of sensitivity to local nonignorability) method quantifies local sensitivity of parametric inferences to nonignorable missingness in an outcome variable. Here we extend ISNI to the situations where both outcomes and predictors can be missing and where the missingness mechanism can be either parametric or semi-parametric. We define the quantity MinNI (minimum nonignorability) to be an approximation to the norm of the smallest value of the transformed nonignorability that gives a nonnegligible displacement of the estimate of the parameter of interest. We illustrate our method in a complete data set from which we synthetically delete observations according to various patterns. We then apply the method to real-data examples involving the normal linear model and conditional logistic regression.

A task analysis of central line-associated bloodstream infection (CLABSI) surveillance in home infusion therapy

BACKGROUND

Barriers for home infusion therapy central line associated bloodstream infection (CLABSI) surveillance have not been elucidated and are needed to identify how to support home infusion CLABSI surveillance. We aimed to (1) perform a goal-directed task analysis of home infusion CLABSI surveillance, and (2) describe barriers to, facilitators for, and suggested strategies for successful home infusion CLABSI surveillance.

METHODS

We conducted semi-structured interviews with team members involved in CLABSI surveillance at 5 large home infusion agencies to explore work systems used by members of the agency for home infusion CLABSI surveillance. We analyzed the transcribed interviews qualitatively for themes.

RESULTS

Twenty-one interviews revealed 8 steps for performing CLABSI surveillance in home infusion therapy. Major barriers identified included the need for training of the surveillance staff, lack of a standardized definition, inadequate information technology support, struggles communicating with hospitals, inadequate time, and insufficient clinician engagement and leadership support.

DISCUSSION

Staff performing home infusion CLABSI surveillance need health system resources, particularly leadership and front-line engagement, access to data, information technology support, training, dedicated time, and reports to perform tasks.

CONCLUSIONS

Building home infusion CLABSI surveillance programs will require support from home infusion leadership.