{"doi":"10.1177/0962280216628902","title":"Responsiveness-informed multiple imputation and inverse probability-weighting in cohort studies with missing data that are non-monotone or not missing at random","abstract":"<jats:p>Population-based cohort studies are invaluable to health research because of the breadth of data collection over time, and the representativeness of their samples. However, they are especially prone to missing data, which can compromise the validity of analyses when data are not missing at random. Having many waves of data collection presents opportunity for participants’ responsiveness to be observed over time, which may be informative about missing data mechanisms and thus useful as an auxiliary variable. Modern approaches to handling missing data such as multiple imputation and maximum likelihood can be difficult to implement with the large numbers of auxiliary variables and large amounts of non-monotone missing data that occur in cohort studies. Inverse probability-weighting can be easier to implement but conventional wisdom has stated that it cannot be applied to non-monotone missing data. This paper describes two methods of applying inverse probability-weighting to non-monotone missing data, and explores the potential value of including measures of responsiveness in either inverse probability-weighting or multiple imputation. Simulation studies are used to compare methods and demonstrate that responsiveness in longitudinal studies can be used to mitigate bias induced by missing data, even when data are not missing at random.</jats:p>","journal":"Statistical Methods in Medical Research","year":2018,"id":18977,"datarank":0.9541020510554679,"base_score":2.995732273553991,"endowment":2.995732273553991,"self_citation_contribution":0.4493598410330987,"citation_network_contribution":0.5047422100223692,"self_endowment_contribution":0.4493598410330987,"citer_contribution":0.5047422100223692,"corpus_percentile":null,"corpus_rank":null,"citation_count":19,"citer_count":16,"citers_with_citation_signal":13,"citers_with_endowment":13,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":129964,"name":"James C Doidge","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":2.995732273553991,"endowment":2.995732273553991,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"26984909","pmcid":null,"openalex_id":"https://openalex.org/W2322733324","authors":[],"funders":[],"total_grants":0,"fwci":3.6235,"citation_percentile":0.9294192,"influential_citations":0,"citation_trend":[{"year":2016,"count":3},{"year":2017,"count":3},{"year":2018,"count":3},{"year":2019,"count":1},{"year":2020,"count":3},{"year":2022,"count":1},{"year":2024,"count":3},{"year":2025,"count":2}],"oa_status":"green","license":"https://journals.sagepub.com/page/policies/text-and-data-mining-license","oa_locations":[{"url":"https://discovery.ucl.ac.uk/1517206/1/Doidge.pdf","host_type":"repository"},{"url":"https://discovery.ucl.ac.uk/1517206/1/Doidge.pdf","host_type":"GREEN"},{"url":"https://discovery.ucl.ac.uk/1517206/1/Doidge.pdf","host_type":"repository"},{"url":"https://journals.sagepub.com/doi/pdf/10.1177/0962280216628902","host_type":"publisher"},{"url":"https://journals.sagepub.com/doi/full-xml/10.1177/0962280216628902","host_type":"publisher"},{"url":"https://discovery.ucl.ac.uk/id/eprint/1517206/","host_type":"repository"},{"url":"https://doi.org/10.1177/0962280216628902","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/26984909","host_type":"repository"},{"url":"https://find.library.unisa.edu.au/discovery/fulldisplay/alma9916033110901831/61USOUTHAUS_INST:ROR","host_type":"repository"}],"fields_of_study":["Advanced Causal Inference Techniques","Statistical Methods and Bayesian Inference","Health disparities and outcomes","Computer Science","Medicine","Biostatistics","Cohort Studies","Computer Simulation","Data Interpretation, Statistical","Humans","Likelihood Functions","Longitudinal Studies","Models, Statistical","Probability"],"mesh_terms":["Computer Simulation","Data Interpretation, Statistical","Humans","Longitudinal Studies","Probability","Models, Statistical","Cohort Studies","Likelihood Functions","Biostatistics"],"keywords":["Missing data","Inverse probability weighting","Imputation (statistics)","Representativeness heuristic","Weighting","Statistics","Inverse probability","Computer science","Data mining","Mathematics","Econometrics","Population","Bayesian probability","Posterior probability","Estimator","Medicine","Cohort studies","Longitudinal Studies","Missing At Random","Multiple Imputation","Loss To Follow-up","Not Missing At Random","Non-monotone Missing Data","Cohort Attrition","Inverse Probability-weighting"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-04T00:58:53.965104Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}