Cargando…

Current Practices in Missing Data Handling for Interrupted Time Series Studies Performed on Individual-Level Data: A Scoping Review in Health Research

OBJECTIVE: Missing data can produce biased estimates in interrupted time series (ITS) analyses. We reviewed recent ITS investigations on health topics for determining 1) the data management strategies and statistical analysis performed, 2) how often missing data were considered and, if so, how they...

Descripción completa

Detalles Bibliográficos
Autores principales:	Bazo-Alvarez, Juan Carlos, Morris, Tim P, Carpenter, James R, Petersen, Irene
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Dove 2021
Materias:	Review
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8316757/ https://www.ncbi.nlm.nih.gov/pubmed/34326669 http://dx.doi.org/10.2147/CLEP.S314020

Ejemplares similares

Handling Missing Values in Interrupted Time Series Analysis of Longitudinal Individual-Level Data
por: Bazo-Alvarez, Juan Carlos, et al.
Publicado: (2020)

Health indicator recording in UK primary care electronic health records: key implications for handling missing data
por: Petersen, Irene, et al.
Publicado: (2019)

Weighted multiple imputation of ethnicity data that are missing not at random in primary care databases
por: Pham, Tra My, et al.
Publicado: (2017)

The prevention and handling of the missing data
por: Kang, Hyun
Publicado: (2013)

A four-step strategy for handling missing outcome data in randomised trials affected by a pandemic
por: Cro, Suzie, et al.
Publicado: (2020)

A framework for handling missing accelerometer outcome data in trials
por: Tackney, Mia S., et al.
Publicado: (2021)

Methods to handle missing values and missing individuals
por: Bonander, Carl, et al.
Publicado: (2018)

Strategies for handling missing data in randomised trials
por: White, Ian R
Publicado: (2011)

Handling missing data in ranked set sampling
por: Bouza-Herrera, Carlos N
Publicado: (2013)

Comparison of Methods for Handling Missing Covariate Data
por: Johansson, Åsa M., et al.
Publicado: (2013)

Common Methods for Handling Missing Data in Marginal Structural Models: What Works and Why
por: Leyrat, Clémence, et al.
Publicado: (2020)

How are missing data in covariates handled in observational time-to-event studies in oncology? A systematic review
por: Carroll, Orlagh U., et al.
Publicado: (2020)

Methods, Applications and Challenges in the Analysis of Interrupted Time Series Data: A Scoping Review
por: Ewusie, Joycelyne E, et al.
Publicado: (2020)

Strategies for Handling Missing Data in Electronic Health Record Derived Data
por: Wells, Brian J., et al.
Publicado: (2013)

An Efficient and Effective Model to Handle Missing Data in Classification
por: Mehrabani-Zeinabad, Kamran, et al.
Publicado: (2020)

Approaches to handling missing or “problematic” pharmacology data: Pharmacokinetics
por: Irby, Donald J., et al.
Publicado: (2021)

Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies
por: Do, Kieu Trinh, et al.
Publicado: (2018)

Handling of missing data with multiple imputation in observational studies that address causal questions: protocol for a scoping review
por: Mainzer, Rheanna, et al.
Publicado: (2023)

Enabling network inference methods to handle missing data and outliers
por: Folch-Fortuny, Abel, et al.
Publicado: (2015)

A hierarchical Bayesian approach for handling missing classification data
por: Ketz, Alison C., et al.
Publicado: (2019)

The current practice of handling and reporting missing outcome data in eight widely used PROMs in RCT publications: a review of the current literature
por: Rombach, Ines, et al.
Publicado: (2016)

A New Approach to Handle Missing Covariate Data in Twin Research: With an Application to Educational Achievement Data
por: Schwabe, Inga, et al.
Publicado: (2015)

Handling Missing Data in Health Economics and Outcomes Research (HEOR): A Systematic Review and Practical Recommendations
por: Mukherjee, Kumar, et al.
Publicado: (2023)

Methods, applications, interpretations and challenges of interrupted time series (ITS) data: protocol for a scoping review
por: Ewusie, Joycelyne E, et al.
Publicado: (2017)

Handling of Missing Outcome Data in Traumatic Brain Injury Research: A Systematic Review
por: Richter, Sophie, et al.
Publicado: (2019)

Handling missing data in propensity score estimation in comparative effectiveness evaluations: a systematic review
por: Malla, Lucas, et al.
Publicado: (2017)

The current practice of handling and reporting missing outcome data in eight widely-used PROMS in RCT publications: are we doing well?
por: Rombach, Ines, et al.
Publicado: (2015)

A review of the handling of missing longitudinal outcome data in clinical trials
por: Powney, Matthew, et al.
Publicado: (2014)

Handling missing data in RCTs; a review of the top medical journals
por: Bell, Melanie L, et al.
Publicado: (2014)

A heuristic approach to handling missing data in biologics manufacturing databases
por: Mante, Jeanet, et al.
Publicado: (2019)

Some Classes of Logarithmic-Type Imputation Techniques for Handling Missing Data
por: Pandey, Awadhesh K., et al.
Publicado: (2021)

Missing data: A statistical framework for practice
por: Carpenter, James R., et al.
Publicado: (2021)

Handling intercurrent events and missing data in non-inferiority trials using the estimand framework: A tuberculosis case study
por: Rehal, Sunita, et al.
Publicado: (2023)

Handling Missing Data in the Short Form–12 Health Survey (SF-12): Concordance of Real Patient Data and Data Estimated by Missing Data Imputation Procedures
por: Wirtz, Markus A., et al.
Publicado: (2020)

Accelerated longitudinal designs: An overview of modelling, power, costs and handling missing data
por: Galbraith, Sally, et al.
Publicado: (2016)

Should multiple imputation be the method of choice for handling missing data in randomized trials?
por: Sullivan, Thomas R, et al.
Publicado: (2016)

Multiple imputation for handling missing outcome data when estimating the relative risk
por: Sullivan, Thomas R., et al.
Publicado: (2017)

The Use of Multiple Imputation to Handle Missing Data in Secondary Datasets: Suggested Approaches when Missing Data Results from the Survey Structure
por: Jo, Soojung
Publicado: (2022)

Missing-Data Handling Methods for Lifelogs-Based Wellness Index Estimation: Comparative Analysis With Panel Data
por: Kim, Ki-Hun, et al.
Publicado: (2020)

Imputation and missing indicators for handling missing data in the development and deployment of clinical prediction models: A simulation study
por: Sisk, Rose, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_dfcf7251l1ldc5684qjaqv2skf