arXiv subjects

Avi Kenny

Publications and source records attributed to Avi Kenny.

9 recordsLinked to original sources

arXiv2026

A Review of Methods and Practices for Missing Data in Sequential Multiple Assignment Randomized Trials (SMARTs): An Ancillary Study of a Scoping Review

Background: Missing data poses an acute threat to sequential multiple assignment randomized trial (SMART) analyses because of the sequential treatment structure and response-dependent re-randomization. Objectives: This study aimed to (1) review the current statistical methods for handling missing data in SMARTs, and (2) characterize how missing data is reported and handled in published SMARTs. Methods: We conducted a narrative review of statistical methods developed for missing data in SMARTs. Additionally, we conducted a pre-specified secondary extraction of a previously published scoping review of SMARTs focused on missing data. Extraction captured attrition rates, methods for handling missingness, and planned versus performed missing data analyses. Results: Seven methodological papers were identified; nearly all assume missing at random (MAR), and only one addresses the full set of SMART-specific missingness types. Across 30 published SMARTs, median overall attrition was 18.1% (range 0.6%-56.5%). Methods used to address missing data were described in 80% of the manuscripts; mixed-model methods were most common (30%). Among 14 studies with paired protocols, sensitivity analyses were pre-specified in 2 (14%). Conclusions: SMART-specific methodology for missing data is limited, and a substantial gap exists between available methodology and current SMART practice.

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arXiv2025

Robust and Efficient Semiparametric Inference for the Stepped Wedge Design

Stepped wedge designs (SWDs) are increasingly used to evaluate longitudinal cluster-level interventions but pose substantial challenges for valid inference. Because crossover times are randomized, intervention effects are intrinsically confounded with secular time trends, while heterogeneity across clusters, complex correlation structures, baseline covariate imbalances, and small numbers of clusters further complicate inference. We propose a unified semiparametric framework for estimating possibly time-varying intervention effects in SWDs. Under a semiparametric model on treatment contrast, we develop a nonstandard semiparametric efficiency theory that accommodates correlated observations within clusters, varying cluster-period sizes, and weakly dependent treatment assignments. The resulting estimator is consistent and asymptotically normal even under misspecified covariance structure and control cluster-period means, and is efficient when both are correctly specified. To enable inference with few clusters, we exploit the permutation structure of treatment assignment to propose a standard error estimator that reflects finite-sample variability, with a leave-one-out correction to reduce plug-in bias. The framework also allows incorporation of effect modification and adjustment for imbalanced precision variables through design-based adjustment or double adjustment that additionally incorporates an outcome-based component. Simulations and application to a public health trial demonstrate the robustness and efficiency of the proposed method relative to standard approaches.

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arXiv2025

Factors affecting power in stepped wedge trials when the treatment effect varies with time

Stepped wedge cluster randomized trials (SW-CRTs) have historically been analyzed using immediate treatment (IT) models, which assume the effect of the treatment is immediate after treatment initiation and subsequently remains constant over time. However, recent research has shown that this assumption can lead to severely misleading results if treatment effects vary with exposure time, i.e. time since the intervention started. Models that account for time-varying treatment effects, such as the exposure time indicator (ETI) model, allow researchers to target estimands such as the time-averaged treatment effect (TATE) over an interval of exposure time, or the point treatment effect (PTE) representing a treatment contrast at one time point. However, this increased flexibility results in reduced power. In this paper, we use public power calculation software and simulation to characterize factors affecting SW-CRT power. Key elements include choice of estimand, study design considerations, and analysis model selection. or common SW-CRT designs, the sample size (clusters per sequence or individuals per cluster-period) must be increased substantially, commonly by a factor of 1.5 to 3, but often by much more, to maintain 90\% power when switching from an IT model to an ETI model (targeting the TATE over the study). However, the inflation factor is lower for TATE estimands over shorter periods that exclude longer exposure times. In general, SW-CRT designs (including the "staircase" variant) have much greater power for estimating "short-term effects" relative to "long-term effects". For an ETI model targeting a TATE estimand, substantial power can be gained by adding time points to the start of the study or increasing baseline sample size, but surprisingly little power is gained from adding time points to the end of the study. More restrictive choices for modeling the exposure... [truncated]

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arXiv2024

Repeated sampling of different individuals within the same clusters to improve precision of longitudinal estimators: the DISC design

Background: Longitudinal studies often involve repeated cluster sampling of a population at multiple time points, such as in difference-in-differences (DID) studies. Although cohort designs typically lead to more efficient estimators relative to repeated cross-sectional (RCS) designs, they are often impractical. Methods: We describe the DISC (Different Individuals, Same Clusters) design, a sampling scheme that improves the precision of estimators in these settings. The DISC design represents a hybrid between a cohort and an RCS design, in which the researcher takes a single sample of clusters at baseline, but takes different cross-sectional samples of individuals within clusters at each time point. Results: We show analytically that the DISC design yields DID estimators with much higher precision relative to an RCS design, particularly if cluster effects are present. For example, for a design with two surveys, 40 clusters, and 25 individuals per cluster, the variance of a commonly-used DID treatment effect estimator is 2.3 times higher in the RCS design for an intraclass correlation coefficient (ICC) of 0.05 and 3.8 times higher for an ICC of 0.1. We also present results of a simulation study comparing the RCS and DISC designs, using both a simple DID estimator and a more complex doubly-robust DID (DRDID) estimator that leverages covariate information, and show gains in precision for both estimators when using the DISC design. Additionally, we illustrate DISC sampling using a household survey dataset from South Africa. Conclusions: Use of the DISC design can result in estimators that have substantially lower variance than the analogous estimator resulting from an RCS design.

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arXiv2024

Analysis of Stepped-Wedge Cluster Randomized Trials when treatment effects vary by exposure time or calendar time

Stepped-wedge cluster randomized trials (SW-CRTs) are traditionally analyzed with models that assume an immediate and sustained treatment effect. Previous work has shown that making such an assumption in the analysis of SW-CRTs when the true underlying treatment effect varies by exposure time can produce severely misleading estimates. Alternatively, the true underlying treatment effect might vary by calendar time. Comparatively less work has examined treatment effect structure misspecification in this setting. Here, we evaluate the behavior of the mixed effects model-based immediate treatment effect, exposure time-averaged treatment effect, and calendar time-averaged treatment effect estimators in different scenarios where they are misspecified for the true underlying treatment effect structure. We show that the immediate treatment effect estimator is relatively robust to bias when estimating a true underlying calendar time-averaged treatment effect estimand. However, when there is a true underlying calendar (exposure) time-varying treatment effect, misspecifying an analysis with an exposure (calendar) time-averaged treatment effect estimator can yield severely misleading estimates and even converge to a value of the opposite sign of the true calendar (exposure) time-averaged treatment effect estimand. In this article, we highlight the two different time scales on which treatment effects can vary in SW-CRTs and clarify potential vulnerabilities that may arise when considering different types of time-varying treatment effects in a SW design. Accordingly, we emphasize the need for researchers to carefully consider whether the treatment effect may vary as a function of exposure time and/or calendar time in the analysis of SW-CRTs.

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arXiv2024

A discrete-time survival model to handle interval-censored covariates, with applications to HIV cohort studies

Methods are lacking to handle the problem of survival analysis in the presence of an interval-censored covariate, specifically the case in which the conditional hazard of the primary event of interest depends on the occurrence of a secondary event, the observation time of which is subject to interval censoring. We propose and study a flexible class of discrete-time parametric survival models that handle the censoring problem through simultaneous modeling of the interval-censored secondary event, the outcome, and the censoring mechanism. We apply this model to the research question that motivated the methodology, estimating the effect of HIV status on all-cause mortality in a prospective cohort study in South Africa. Our model has applicability for many open questions, including estimating the impact of policy decisions on population level HIV-related outcomes and determining causes of morbidity and mortality for which the HIV positive population may be at increased risk. Examples include determining how the large-scale transition from efavirenz-based to dolutegravir-based first-line ART impacted mortality for people living with HIV and determining whether HIV status is associated with increased risk of stroke, diabetes, hypertension, and other non-communicable diseases.

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arXiv2024

Investigating symptom duration using current status data: a case study of post-acute COVID-19 syndrome

For infectious diseases, characterizing symptom duration is of clinical and public health importance. Symptom duration may be assessed by surveying infected individuals and querying symptom status at the time of survey response. For example, in a SARS-CoV-2 testing program at the University of Washington, participants were surveyed at least $28$ days after testing positive and asked to report current symptom status. This study design yielded current status data: outcome measurements for each respondent consisted only of the time of survey response and a binary indicator of whether symptoms had resolved by that time. Such study design benefits from limited risk of recall bias, but analyzing the resulting data necessitates tailored statistical tools. Here, we review methods for current status data and describe a novel application of modern nonparametric techniques to this setting. The proposed approach is valid under weaker assumptions compared to existing methods, allows use of flexible machine learning tools, and handles potential survey nonresponse. From the university study, under an assumption that the survey response time is conditionally independent of symptom resolution time within strata of measured covariates, we estimate that 19% of participants experienced ongoing symptoms 30 days after testing positive, decreasing to 7% at 90 days. We assess the sensitivity of these results to deviations from conditional independence, finding the estimates to be more sensitive to assumption violations at 30 days compared to 90 days. Female sex, fatigue during acute infection, and higher viral load were associated with slower symptom resolution.

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arXiv2024

SimEngine: A Modular Framework for Statistical Simulations in R

This article describes SimEngine, an open-source R package for structuring, maintaining, running, and debugging statistical simulations on both local and cluster-based computing environments. Several R packages exist for structuring simulations, but SimEngine is the only package specifically designed for running simulations in parallel via job schedulers on high-performance cluster computing systems. The package provides structure and functionality for common simulation tasks, such as setting simulation levels, managing seeds for random number generation, and calculating summary metrics (such as bias and confidence interval coverage). SimEngine also brings several unique features, such as automatic calculation of Monte Carlo error and information-sharing across simulation replicates. We provide an overview of the package and demonstrate some of its advanced functionality.

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arXiv2021

Analysis of stepped wedge cluster randomized trials in the presence of a time-varying treatment effect

Stepped wedge cluster randomized controlled trials are typically analyzed using models that assume the full effect of the treatment is achieved instantaneously. We provide an analytical framework for scenarios in which the treatment effect varies as a function of exposure time (time since the start of treatment) and define the "effect curve" as the magnitude of the treatment effect on the linear predictor scale as a function of exposure time. The "time-averaged treatment effect", (TATE) and "long-term treatment effect" (LTE) are summaries of this curve. We analytically derive the expectation of the estimator resulting from a model that assumes an immediate treatment effect and show that it can be expressed as a weighted sum of the time-specific treatment effects corresponding to the observed exposure times. Surprisingly, although the weights sum to one, some of the weights can be negative. This implies that the estimator may be severely misleading and can even converge to a value of the opposite sign of the true TATE or LTE. We describe several models that can be used to simultaneously estimate the entire effect curve, the TATE, and the LTE, some of which make assumptions about the shape of the effect curve. We evaluate these models in a simulation study to examine the operating characteristics of the resulting estimators and apply them to two real datasets.

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