{"id":{"repo_id":"east-anglia","oai_identifier":"oai:ueaeprints.uea.ac.uk:59244"},"canonical_url":"https://search.dev.ndltd.org/etd/east-anglia/oai:ueaeprints.uea.ac.uk:59244","repository":{"repo_id":"east-anglia","name":"University of East Anglia","base_url":"https://ueaeprints.uea.ac.uk/cgi/oai2"},"display":{"title":"Some statistical problems in sequential meta-analysis","abstract":"The objective of meta-analysis is to combine results from several independent studies in order to make evidence more generalisable and provide evidence base for decision making. However, recent studies show that the magnitude of effect size estimates reported in many areas of research have significantly changed over time. These temporal trends can be dramatic and even lead to the loss or gain of the statistical significance of the cumulative treatment effect (Kulinskaya and Koricheva, 2010). Standard sequential methods including cumulative meta-analysis, sequential meta-analysis, the use of quality control charts and penalised z-test have been proposed for monitoring the trends in meta-analysis. But these methods are only effective when monitoring in fixed effect model (FEM) of meta-analysis. For random-effects model (REM), the analysis incorporates the heterogeneity variance, t2 and its estimation creates complications. This thesis proposes the use of a truncated CUSUM-type test (Gombay method) for sequential monitoring in REM, and also examines the effect of accumulating evidence in meta-analysis. Simulations show that the use of Gombay method with critical values derived from asymptotic theory does not control the Type I error. However, the test with bootstrap-based critical values (retrospective Gombay sequential bootstrap test for REM) leads to a reduction of the difference between the true and nominal levels, and thus constitutes a good approach for monitoring REM. Application of the proposed method is illustrated using two meta-analytic examples from medicine. Two kinds of bias associated with accumulating evidence, termed \\sequential decision bias\" and \\sequential design bias\" are identified. It was demonstrated analytically and by simulations that both types of sequential biases are non negligible. Simulations also show that sequential biases increase with increased heterogeneity.","abstract_html":"The objective of meta-analysis is to combine results from several independent studies in order to make evidence more generalisable and provide evidence base for decision making. However, recent studies show that the magnitude of effect size estimates reported in many areas of research have significantly changed over time. These temporal trends can be dramatic and even lead to the loss or gain of the statistical significance of the cumulative treatment effect (Kulinskaya and Koricheva, 2010). Standard sequential methods including cumulative meta-analysis, sequential meta-analysis, the use of quality control charts and penalised z-test have been proposed for monitoring the trends in meta-analysis. But these methods are only effective when monitoring in fixed effect model (FEM) of meta-analysis. For random-effects model (REM), the analysis incorporates the heterogeneity variance, t2 and its estimation creates complications. This thesis proposes the use of a truncated CUSUM-type test (Gombay method) for sequential monitoring in REM, and also examines the effect of accumulating evidence in meta-analysis. Simulations show that the use of Gombay method with critical values derived from asymptotic theory does not control the Type I error. However, the test with bootstrap-based critical values (retrospective Gombay sequential bootstrap test for REM) leads to a reduction of the difference between the true and nominal levels, and thus constitutes a good approach for monitoring REM. Application of the proposed method is illustrated using two meta-analytic examples from medicine. Two kinds of bias associated with accumulating evidence, termed \\sequential decision bias" and \\sequential design bias" are identified. It was demonstrated analytically and by simulations that both types of sequential biases are non negligible. Simulations also show that sequential biases increase with increased heterogeneity.","abstract_has_math":false,"creators":["Dogo, Samson Henry"],"institution":"University of East Anglia","degree_name":"phd","degree_level":"doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-04","date_published":"2016-04","updated_at":"2026-07-24T02:12:16Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Dogo, Samson Henry"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-04"]},{"key":"dc:date.issued","label":"Date","values":["2016-04"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["School of Computing Sciences"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of East Anglia"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://ueaeprints.uea.ac.uk/id/eprint/59244/"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["phd"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://ueaeprints.uea.ac.uk/id/eprint/59244/1/PhD_Thesis_of_S_H_Dogo.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The objective of meta-analysis is to combine results from several independent studies in order to make evidence more generalisable and provide evidence base for decision making. However, recent studies show that the magnitude of effect size estimates reported in many areas of research have significantly changed over time. These temporal trends can be dramatic and even lead to the loss or gain of the statistical significance of the cumulative treatment effect (Kulinskaya and Koricheva, 2010). Standard sequential methods including cumulative meta-analysis, sequential meta-analysis, the use of quality control charts and penalised z-test have been proposed for monitoring the trends in meta-analysis. But these methods are only effective when monitoring in fixed effect model (FEM) of meta-analysis. For random-effects model (REM), the analysis incorporates the heterogeneity variance, t2 and its estimation creates complications. This thesis proposes the use of a truncated CUSUM-type test (Gombay method) for sequential monitoring in REM, and also examines the effect of accumulating evidence in meta-analysis. Simulations show that the use of Gombay method with critical values derived from asymptotic theory does not control the Type I error. However, the test with bootstrap-based critical values (retrospective Gombay sequential bootstrap test for REM) leads to a reduction of the difference between the true and nominal levels, and thus constitutes a good approach for monitoring REM. Application of the proposed method is illustrated using two meta-analytic examples from medicine. Two kinds of bias associated with accumulating evidence, termed \\sequential decision bias\" and \\sequential design bias\" are identified. It was demonstrated analytically and by simulations that both types of sequential biases are non negligible. Simulations also show that sequential biases increase with increased heterogeneity."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Some statistical problems in sequential meta-analysis"]}]}],"canonical_facts":{"dc:creator":["Dogo, Samson Henry"],"dc:date":["2016-04"],"dc:date.issued":["2016-04"],"dc:description.abstract":["The objective of meta-analysis is to combine results from several independent studies in order to make evidence more generalisable and provide evidence base for decision making. However, recent studies show that the magnitude of effect size estimates reported in many areas of research have significantly changed over time. These temporal trends can be dramatic and even lead to the loss or gain of the statistical significance of the cumulative treatment effect (Kulinskaya and Koricheva, 2010). Standard sequential methods including cumulative meta-analysis, sequential meta-analysis, the use of quality control charts and penalised z-test have been proposed for monitoring the trends in meta-analysis. But these methods are only effective when monitoring in fixed effect model (FEM) of meta-analysis. For random-effects model (REM), the analysis incorporates the heterogeneity variance, t2 and its estimation creates complications. This thesis proposes the use of a truncated CUSUM-type test (Gombay method) for sequential monitoring in REM, and also examines the effect of accumulating evidence in meta-analysis. Simulations show that the use of Gombay method with critical values derived from asymptotic theory does not control the Type I error. However, the test with bootstrap-based critical values (retrospective Gombay sequential bootstrap test for REM) leads to a reduction of the difference between the true and nominal levels, and thus constitutes a good approach for monitoring REM. Application of the proposed method is illustrated using two meta-analytic examples from medicine. Two kinds of bias associated with accumulating evidence, termed \\sequential decision bias\" and \\sequential design bias\" are identified. It was demonstrated analytically and by simulations that both types of sequential biases are non negligible. Simulations also show that sequential biases increase with increased heterogeneity."],"dc:format":["application/pdf"],"dc:identifier.uri":["https://ueaeprints.uea.ac.uk/id/eprint/59244/1/PhD_Thesis_of_S_H_Dogo.pdf"],"dc:language":["en"],"dc:publisher.department":["School of Computing Sciences"],"dc:publisher.institution":["University of East Anglia"],"dc:relation.isreferencedby":["https://ueaeprints.uea.ac.uk/id/eprint/59244/"],"dc:title":["Some statistical problems in sequential meta-analysis"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["doctoral"],"dc:type.qualificationname":["phd"]},"updated_at":"2026-07-24T02:12:16Z"}