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Open research questions in Statistical Methods and Bayesian Inference

197 gap statements mined from Statistical Methods and Bayesian Inference papers in our 7.9M-paper local library, which holds 1,843 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

What the literature leaves open

  • The uptake of missing-not-at-random (MNAR) sensitivity methods in registry-PROM practice is unknown. There is a lack of a pragmatic workflow for handling missing PROM data in registry studies.

    A pragmatic workflow for handling missing PROM data in registry studies: a methodological scoping review and simulated worked example · 2026 · DOI

  • Identifying an appropriate covariance structure for modeling longitudinal changes in polysubstance cravings is a challenge. The study notes the importance of considering the complexity of covariance structures in repeated-measures designs. The study highlights the need to account for baseline behavioral and psychiatric characteristics in analyzing craving severity.

    Model selection in linear mixed-effects analysis of craving outcomes in a randomized controlled trial of medications for opioid use disorders · 2026 · DOI

  • However, the robustness of this pseudo-likelihood-based approach under missing at random (MAR) remains unclear.

    Robustness of the Pairwise‐Fitting Approach Under Missing at Random Dropout: A Case and Simulation Study · 2026 · DOI

  • While previous research has addressed the impact of measurement error in broader multilevel contexts, its effects on statistical power-particularly concerning cross-level interaction effects-have not been thoroughly explored.

    Shedding some light on the relationship between measurement error and statistical power in multilevel models applied to intensive longitudinal designs · 2026 · DOI

  • The main result indicates that the small sample bias of the OLS estimator of the autoregressive effect is a consequence of limited information and correcting for this bias without more information always induces a bias-variance trade-off.

    Unravelling the small sample bias in AR (1) models: The pros and cons of available bias correction methods · 2026 · DOI

  • This partial-pooling structure shares information across stages and stabilizes estimation where data are sparse, while a masking mechanism accommodates covariates structurally unavailable at particular stages without discarding records or imputing values.

    Using Bayesian Sequential Logistic Regression to Model the Organ Transplant Selection Process · 2026

  • Existing approaches for bidimensional integration are scarce and predominantly unsupervised, overlooking the valuable insights offered by covariates.

    BARRR: Bidimensional Augmented Reduced Rank Regression for Multi-View Multi-Cohort Data · 2026

  • Although the basic configuration of the BW‐CFM is well understood, several configurations of the model that accommodate different patterns of Person‐level relationships remain unexplored.

    Variations on the Common Fate Model for Dyadic Analysis: Recommendations for Model Selection · 2025 · DOI

  • However, the impact of measurement structure misspecification in DSEMs, especially under varying reliability conditions and model complexities, remains underexplored.

    Measurement Model Misspecification in Dynamic Structural Equation Models: Power, Reliability, and Other Considerations · 2025 · DOI

  • Probit DSEM was proposed to accommodate categorical ILD, but its properties have not yet been thoroughly studied.

    Sensitivity of Probit Dynamic Structural Equation Models to Non-Normality of the Latent Variable Assumed to Underlie Categorical Responses · 2025 · DOI

  • Recently, a robust median-based Bayesian (RMB) approach for growth curve modeling (GCM) was proposed to offer a novel way to handle nonnormal longitudinal data, yet its effectiveness with missing data has not been fully investigated.

    A Comparative Evaluation of a Conditional Median-Based Bayesian Growth Curve Modeling Approach with Missing Data · 2025 · DOI

  • The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied.

    Dynamic Structural Equation Models with Missing Data: Data Requirements on N and T · 2024 · DOI

  • Appropriate handling of missing values is crucial in clinical research; however, it remains insufficiently explored in the context of MRTs.

    Handling of outcome missing data dependent on measured or unmeasured background factors in micro-randomized trial: Simulation and application study · 2024 · DOI

  • The study highlights the need for more research on the effectiveness of different imputation methods and forecasting models for air pollution data.

    Analysis of Imputation Methods for Missing Not at Random (MNAR) Data: A Comparative Study of Air Pollution Data in Bangkok, Thailand · 2026 · DOI

  • The methodology tested imputation at missing rates up to 70%, but whether KNN maintains effectiveness at even higher missing rates (>70%) remains unexplored.

    Analysis of Imputation Methods for Missing Not at Random (MNAR) Data: A Comparative Study of Air Pollution Data in Bangkok, Thailand · 2026 · DOI

  • Existing methods cannot accommodate participants with unknown survival status or those who drop out for reasons unrelated to mortality. There is a need for a method that can jointly estimate causal effects on multivariate outcomes and account for different types of incomplete data.

    Bayesian Inference for Cluster‐Randomized Trials With Multivariate Outcomes Subject to Both Truncation by Death and Missingness · 2026 · DOI

  • The lack of a protocol to address heterogeneity in cluster sizes and structures in cluster-randomised trials. The limitations of currently recommended methods in addressing this challenge.

    Cluster trials inference with CARE · 2026 · DOI

  • Assessing model fit for multiply imputed data is challenging due to the difference in fit across imputed datasets. The study faces the challenge of deriving a relationship between the proposed estimators and existing methods. The performance of the estimators under MNAR is a challenge that requires further research.

    Estimators of the AIC and BIC in multiply imputed data · 2026 · DOI

  • Future studies can investigate the performance of the estimators under MNAR. The derivation of the relationship between the proposed estimators and existing methods can be generalized to models for categorical data. Further research can explore the application of the new estimators in various fields.

    Estimators of the AIC and BIC in multiply imputed data · 2026 · DOI

  • Future research should investigate the performance of record linkage algorithms in different scenarios. Future research should develop new methods for record linkage that take into account the linkage mechanism and the amount of overlap between the files.

    Analysis of Linked Files: A Missing Data Perspective · 2026 · DOI

  • Further research can be conducted to apply the proposed methods to other survival models. The study can be extended to include more complex models and larger datasets.

    Jackknife-based diagnostics for non-monotonic hazard survival model with interval-censored data · 2026 · DOI

  • While methods such as local polynomial regression, Gaussian processes and generalized additive models (GAMs) exist outside of psychology, they are rarely applied within the field because they have not yet been reviewed accessibly and evaluated within the context of ILD.

    Modelling non‐linear psychological processes: Reviewing and evaluating non‐parametric approaches and their applicability to intensive longitudinal data · 2025 · DOI

  • Development of new imputation methods that can handle multilevel data with non-normal distributions.

    Adapting tree-based multiple imputation methods for multilevel data? A simulation study · 2026 · DOI

  • Existing maximum likelihood estimation procedures for the negative binomial distribution may fail or produce unstable estimates when the true data-generating process is Poisson. The phenomenon of over-dispersion is frequently observed, and a negative binomial distribution with two parameters is more appropriate.

    From Poisson observations to fitted negative binomial distribution · 2026 · DOI

  • The increasing availability of large-scale epidemiological studies, such as the UK Biobank, poses important modelling challenges, including mixed data types, high dimensionality, and structured missingness.

    MIDFA: Scalable Bayesian Factor Analysis for Mixed and Incomplete Data · 2026 · DOI

Most-cited papers in Statistical Methods and Bayesian Inference

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Open questions raised by papers listed on this page include: (1) The uptake of missing-not-at-random (MNAR) sensitivity methods in registry-PROM practice is unknown (“A pragmatic workflow for handling missing PROM data in registry studies: a…”, 2026); (2) The robustness of this pseudo-likelihood-based approach under missing at random (MAR) remains unclear (“Robustness of the Pairwise‐Fitting Approach Under Missing at Random Dropout: A…”, 2026); (3) While previous research has addressed the impact of measurement error in broader multilevel contexts, its effects on statistical power-particularly concerning cross-level interaction effects-have not been thoroughly explored (“Shedding some light on the relationship between measurement error and…”, 2026); (4) The methodology tested imputation at missing rates up to 70%, but whether KNN maintains effectiveness at even higher missing rates (>70%) remains unexplored (“Analysis of Imputation Methods for Missing Not at Random (MNAR) Data: A…”, 2026); (5) There is a need for a method that can jointly estimate causal effects on multivariate outcomes and account for different types of incomplete data (“Bayesian Inference for Cluster‐Randomized Trials With Multivariate Outcomes…”, 2026). 197 gap statements have been mined from Statistical Methods and Bayesian Inference papers in our 7.9M-paper local library, which holds 1,843 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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