Tackling non-ignorable dropout in the presence of time varying confounding
We explore the sensitivity of time varying confounding adjusted estimates to different dropout mechanisms. We extend the Heckman correction to two time points and explore selection models to investigate situations where the dropout process is driven by unobserved variables and the outcome respective...
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| Veröffentlicht in: | Journal of the Royal Statistical Society Series C: Applied Statistics 2016-11, Vol.65 (5), p.775-795 |
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| container_title | Journal of the Royal Statistical Society Series C: Applied Statistics |
| container_volume | 65 |
| creator | Doretti, Marco Geneletti, Sara Stanghellini, Elena |
| description | We explore the sensitivity of time varying confounding adjusted estimates to different dropout mechanisms. We extend the Heckman correction to two time points and explore selection models to investigate situations where the dropout process is driven by unobserved variables and the outcome respectively. The analysis is embedded in a Bayesian framework which provides several advantages. These include fitting a hierarchical structure to processes that repeat over time and avoiding exclusion restrictions in the case of the Heckman correction. We adopt the decision theoretic approach to causal inference which makes explicit the no-regime-dropout dependence assumption. We apply our methods to data from the 'Counterweight programme' pilot: a UK protocol to address obesity in primary care. A simulation study is also implemented. |
| doi_str_mv | 10.1111/rssc.12154 |
| format | Article |
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A simulation study is also implemented.</description><subject>Applied statistics</subject><subject>Bayesian analysis</subject><subject>Causal inference</subject><subject>Dropouts</subject><subject>Estimates</subject><subject>Fittings</subject><subject>Heckman correction</subject><subject>Non-ignorable dropout</subject><subject>Obesity</subject><subject>Pilots</subject><subject>Selection models</subject><subject>Simulation</subject><subject>Statistics</subject><subject>Structural hierarchy</subject><subject>Studies</subject><subject>Time varying confounding</subject><issn>0035-9254</issn><issn>1467-9876</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp9kMtLJDEQxoMo7Kh72bvQ4EWEHvPspI8y-Jb1reAlJOm09tiTjEn3rvPfb8Z2PXiwLlXw_b6i6gPgF4JjlGovxGjGCCNGV8AI0YLnpeDFKhhBSFheYkZ_gPUYpzAVgnQETm-VeWkb95Q57_LmyfmgdGuzKvi577uscVn3bLN5sNE6YzNfZ10zs9kfFRZLl_Gu9r2r0rwJ1mrVRvvzo2-Au8OD28lxfn5xdDLZP88NESXNucYa1oTY2jAsKsNKpDjHNSsg1BgbgTmvsdGGMK01VBwZC2HBi4oozWhFNsDOsHce_GtvYydnTTS2bZWzvo8SCcoEoZCjhG5_Qae-Dy5dlyjMWclFQRK1O1Am-BiDreU8NLP0oERQLmOVy1jle6wJRgP8t2nt4htSXt_cTP57tgbPNHY-fHooLQQSjCU9H_QmdvbtU1fhRRaccCYffh_JK47E2f3lozwm_wB50pLj</recordid><startdate>201611</startdate><enddate>201611</enddate><creator>Doretti, Marco</creator><creator>Geneletti, Sara</creator><creator>Stanghellini, Elena</creator><general>Blackwell Publishing Ltd</general><general>John Wiley & Sons Ltd</general><general>Oxford University Press</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8BJ</scope><scope>8FD</scope><scope>FQK</scope><scope>JBE</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201611</creationdate><title>Tackling non-ignorable dropout in the presence of time varying confounding</title><author>Doretti, Marco ; Geneletti, Sara ; Stanghellini, Elena</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3894-7b2b0f33efc528dc591a772f5600b22c8277f2cbc35bbb0a71ce00676d3ab54d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Applied statistics</topic><topic>Bayesian analysis</topic><topic>Causal inference</topic><topic>Dropouts</topic><topic>Estimates</topic><topic>Fittings</topic><topic>Heckman correction</topic><topic>Non-ignorable dropout</topic><topic>Obesity</topic><topic>Pilots</topic><topic>Selection models</topic><topic>Simulation</topic><topic>Statistics</topic><topic>Structural hierarchy</topic><topic>Studies</topic><topic>Time varying confounding</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Doretti, Marco</creatorcontrib><creatorcontrib>Geneletti, Sara</creatorcontrib><creatorcontrib>Stanghellini, Elena</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Technology Research Database</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Journal of the Royal Statistical Society Series C: Applied Statistics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Doretti, Marco</au><au>Geneletti, Sara</au><au>Stanghellini, Elena</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Tackling non-ignorable dropout in the presence of time varying confounding</atitle><jtitle>Journal of the Royal Statistical Society Series C: Applied Statistics</jtitle><addtitle>J. R. Stat. Soc. C</addtitle><date>2016-11</date><risdate>2016</risdate><volume>65</volume><issue>5</issue><spage>775</spage><epage>795</epage><pages>775-795</pages><issn>0035-9254</issn><eissn>1467-9876</eissn><abstract>We explore the sensitivity of time varying confounding adjusted estimates to different dropout mechanisms. We extend the Heckman correction to two time points and explore selection models to investigate situations where the dropout process is driven by unobserved variables and the outcome respectively. The analysis is embedded in a Bayesian framework which provides several advantages. These include fitting a hierarchical structure to processes that repeat over time and avoiding exclusion restrictions in the case of the Heckman correction. We adopt the decision theoretic approach to causal inference which makes explicit the no-regime-dropout dependence assumption. 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| identifier | ISSN: 0035-9254 |
| ispartof | Journal of the Royal Statistical Society Series C: Applied Statistics, 2016-11, Vol.65 (5), p.775-795 |
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| source | Wiley Online Library Journals Frontfile Complete; Business Source Complete; JSTOR Mathematics & Statistics; Jstor Complete Legacy; Oxford University Press Journals All Titles (1996-Current) |
| subjects | Applied statistics Bayesian analysis Causal inference Dropouts Estimates Fittings Heckman correction Non-ignorable dropout Obesity Pilots Selection models Simulation Statistics Structural hierarchy Studies Time varying confounding |
| title | Tackling non-ignorable dropout in the presence of time varying confounding |
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