{"id":"https://openalex.org/W7140192443","doi":"https://doi.org/10.1016/j.csda.2026.108379","title":"Optimising covariate allocation at design stage using fisher information matrix for non-linear mixed effects models in pharmacometrics","display_name":"Optimising covariate allocation at design stage using fisher information matrix for non-linear mixed effects models in pharmacometrics","publication_year":2026,"publication_date":"2026-03-24","ids":{"openalex":"https://openalex.org/W7140192443","doi":"https://doi.org/10.1016/j.csda.2026.108379"},"language":"en","primary_location":{"id":"doi:10.1016/j.csda.2026.108379","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.csda.2026.108379","pdf_url":null,"source":{"id":"https://openalex.org/S132362803","display_name":"Computational Statistics & Data Analysis","issn_l":"0167-9473","issn":["0167-9473","1872-7352"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics & Data Analysis","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.csda.2026.108379","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Lucie Fayette","orcid":"https://orcid.org/0009-0001-0048-5998"},"institutions":[{"id":"https://openalex.org/I154526488","display_name":"Inserm","ror":"https://ror.org/02vjkv261","country_code":"FR","type":"government","lineage":["https://openalex.org/I154526488"]},{"id":"https://openalex.org/I183367347","display_name":"Ipsen (France)","ror":"https://ror.org/00d801g55","country_code":"FR","type":"company","lineage":["https://openalex.org/I183367347"]},{"id":"https://openalex.org/I204730241","display_name":"Universit\u00e9 Paris Cit\u00e9","ror":"https://ror.org/05f82e368","country_code":"FR","type":"education","lineage":["https://openalex.org/I204730241"]},{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]},{"id":"https://openalex.org/I4210091279","display_name":"Universit\u00e9 Sorbonne Paris Nord","ror":"https://ror.org/0199hds37","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210091279"]},{"id":"https://openalex.org/I4210091437","display_name":"Sorbonne Paris Cit\u00e9","ror":"https://ror.org/001z21q04","country_code":"FR","type":"other","lineage":["https://openalex.org/I4210091437"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Lucie Fayette","raw_affiliation_strings":["Pharmacometrics Ipsen Innovation, Les Ulis, France","Universit\u00e9 Paris Cit\u00e9 et Universit\u00e9 Sorbonne Paris Nord, IAME, Inserm, Paris, F-75018, France"],"raw_orcid":"https://orcid.org/0009-0001-0048-5998","affiliations":[{"raw_affiliation_string":"Pharmacometrics Ipsen Innovation, Les Ulis, France","institution_ids":["https://openalex.org/I183367347"]},{"raw_affiliation_string":"Universit\u00e9 Paris Cit\u00e9 et Universit\u00e9 Sorbonne Paris Nord, IAME, Inserm, Paris, F-75018, France","institution_ids":["https://openalex.org/I204730241","https://openalex.org/I4210091437","https://openalex.org/I4210091279","https://openalex.org/I39804081","https://openalex.org/I154526488"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Karl Brendel","orcid":"https://orcid.org/0009-0004-7551-0758"},"institutions":[{"id":"https://openalex.org/I183367347","display_name":"Ipsen (France)","ror":"https://ror.org/00d801g55","country_code":"FR","type":"company","lineage":["https://openalex.org/I183367347"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Karl Brendel","raw_affiliation_strings":["Pharmacometrics Ipsen Innovation, Les Ulis, France"],"raw_orcid":"https://orcid.org/0009-0004-7551-0758","affiliations":[{"raw_affiliation_string":"Pharmacometrics Ipsen Innovation, Les Ulis, France","institution_ids":["https://openalex.org/I183367347"]}]},{"author_position":"last","author":{"id":null,"display_name":"France Mentr\u00e9","orcid":"https://orcid.org/0000-0002-7045-1275"},"institutions":[{"id":"https://openalex.org/I154526488","display_name":"Inserm","ror":"https://ror.org/02vjkv261","country_code":"FR","type":"government","lineage":["https://openalex.org/I154526488"]},{"id":"https://openalex.org/I204730241","display_name":"Universit\u00e9 Paris Cit\u00e9","ror":"https://ror.org/05f82e368","country_code":"FR","type":"education","lineage":["https://openalex.org/I204730241"]},{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]},{"id":"https://openalex.org/I4210091279","display_name":"Universit\u00e9 Sorbonne Paris Nord","ror":"https://ror.org/0199hds37","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210091279"]},{"id":"https://openalex.org/I4210091437","display_name":"Sorbonne Paris Cit\u00e9","ror":"https://ror.org/001z21q04","country_code":"FR","type":"other","lineage":["https://openalex.org/I4210091437"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"France Mentr\u00e9","raw_affiliation_strings":["Universit\u00e9 Paris Cit\u00e9 et Universit\u00e9 Sorbonne Paris Nord, IAME, Inserm, Paris, F-75018, France"],"raw_orcid":"https://orcid.org/0000-0002-7045-1275","affiliations":[{"raw_affiliation_string":"Universit\u00e9 Paris Cit\u00e9 et Universit\u00e9 Sorbonne Paris Nord, IAME, Inserm, Paris, F-75018, France","institution_ids":["https://openalex.org/I204730241","https://openalex.org/I4210091437","https://openalex.org/I4210091279","https://openalex.org/I39804081","https://openalex.org/I154526488"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I154526488","https://openalex.org/I183367347","https://openalex.org/I204730241","https://openalex.org/I39804081","https://openalex.org/I4210091279","https://openalex.org/I4210091437"],"apc_list":{"value":3340,"currency":"USD","value_usd":3340},"apc_paid":{"value":3340,"currency":"USD","value_usd":3340},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.64216089,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"221","issue":null,"first_page":"108379","last_page":"108379"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.5817000269889832,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.5817000269889832,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.31839999556541443,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.02239999920129776,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.9429000020027161},{"id":"https://openalex.org/keywords/fisher-information","display_name":"Fisher information","score":0.4950999915599823},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.44839999079704285},{"id":"https://openalex.org/keywords/multivariate-normal-distribution","display_name":"Multivariate normal distribution","score":0.4415000081062317},{"id":"https://openalex.org/keywords/copula","display_name":"Copula (linguistics)","score":0.3982999920845032},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.3921999931335449},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.388700008392334},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.3671000003814697}],"concepts":[{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.9429000020027161},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5217999815940857},{"id":"https://openalex.org/C29406490","wikidata":"https://www.wikidata.org/wiki/Q1420659","display_name":"Fisher information","level":2,"score":0.4950999915599823},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.44839999079704285},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.44839999079704285},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.4415000081062317},{"id":"https://openalex.org/C17618745","wikidata":"https://www.wikidata.org/wiki/Q207509","display_name":"Copula (linguistics)","level":2,"score":0.3982999920845032},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.3921999931335449},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.388700008392334},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.3671000003814697},{"id":"https://openalex.org/C16012445","wikidata":"https://www.wikidata.org/wiki/Q1501135","display_name":"Mixed model","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3425000011920929},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.33820000290870667},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.31859999895095825},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.31349998712539673},{"id":"https://openalex.org/C41587187","wikidata":"https://www.wikidata.org/wiki/Q1501882","display_name":"Generalized linear model","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C96608239","wikidata":"https://www.wikidata.org/wiki/Q1199823","display_name":"Statistical power","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.30239999294281006},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2605000138282776},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2587999999523163}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.csda.2026.108379","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.csda.2026.108379","pdf_url":null,"source":{"id":"https://openalex.org/S132362803","display_name":"Computational Statistics & Data Analysis","issn_l":"0167-9473","issn":["0167-9473","1872-7352"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics & Data Analysis","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.csda.2026.108379","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.csda.2026.108379","pdf_url":null,"source":{"id":"https://openalex.org/S132362803","display_name":"Computational Statistics & Data Analysis","issn_l":"0167-9473","issn":["0167-9473","1872-7352"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics & Data Analysis","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.718402624130249}],"awards":[],"funders":[{"id":"https://openalex.org/F4320314481","display_name":"Ipsen Biopharmaceuticals","ror":null},{"id":"https://openalex.org/F4320321663","display_name":"Association Nationale de la Recherche et de la Technologie","ror":"https://ror.org/00ht2ab73"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W982224934","https://openalex.org/W1542069658","https://openalex.org/W1968004121","https://openalex.org/W1981562928","https://openalex.org/W1995565517","https://openalex.org/W2007471319","https://openalex.org/W2020328455","https://openalex.org/W2036628480","https://openalex.org/W2038939603","https://openalex.org/W2042355708","https://openalex.org/W2048144055","https://openalex.org/W2062685800","https://openalex.org/W2065831867","https://openalex.org/W2083793917","https://openalex.org/W2085895448","https://openalex.org/W2098132613","https://openalex.org/W2116692231","https://openalex.org/W2131695829","https://openalex.org/W2145527652","https://openalex.org/W2149860981","https://openalex.org/W2150295088","https://openalex.org/W2166384105","https://openalex.org/W2171238253","https://openalex.org/W2171323312","https://openalex.org/W2381281769","https://openalex.org/W2548473334","https://openalex.org/W2556435907","https://openalex.org/W2940899252","https://openalex.org/W2947332008","https://openalex.org/W2962818040","https://openalex.org/W4384664147","https://openalex.org/W4399648996","https://openalex.org/W4409542559","https://openalex.org/W4412399554"],"related_works":[],"abstract_inverted_index":{"\u2022":[0,12,24,34],"Fisher":[1,194],"Information":[2,17,195],"Matrix":[3,18,196],"in":[4,44,137,159,291],"nonlinear":[5],"mixed":[6],"effects":[7,73,120,130,179],"models":[8],"(NLMEM)":[9,65],"with":[10,128,227],"covariates":[11,67,136,152,215],"New":[13],"computation":[14,203],"method":[15],"of":[16,26,80,151,174,187,213,230,267,284],"using":[19,29,252],"copula":[20,209],"and":[21,31,100,121,131,170,197,201,208,222],"Gaussian":[22,206],"quadrature":[23,207],"Optimisation":[25],"covariate":[27,43,119,129,178,273,292],"allocation":[28,150],"partitioning":[30],"projected":[32],"gradient":[33],"Reduced":[35],"sample":[36],"size":[37],"needed":[38,286],"to":[39,53,68,113,117,156,180,247,280,287],"detect":[40,181],"clinically":[41,219],"relevant":[42],"a":[45,125,132,160,199,243,260],"PK":[46],"example":[47],"A":[48],"novel":[49],"framework":[50],"is":[51,110],"introduced":[52],"design":[54,109],"experiments":[55],"for":[56,85,135,233,259,294],"pharmacometrics":[57],"studies":[58],"analysed":[59],"by":[60],"Non-Linear":[61],"Mixed":[62],"Effects":[63],"Models":[64],"including":[66,263],"describe":[69],"inter-individual":[70],"variability.":[71],"Covariate":[72,276],"may":[74],"help":[75],"identify":[76],"subpopulations":[77],"at":[78],"risk":[79],"sub-therapeutic":[81],"or":[82,89,185,269,296],"toxic":[83],"responses,":[84],"instance":[86],"when":[87],"hepatic":[88,270],"renal":[90,268],"impairment":[91],"reduces":[92],"drug":[93],"elimination,":[94],"increasing":[95],"safety":[96],"risks.":[97],"Before":[98],"collecting":[99],"modelling":[101],"new":[102,161],"clinical":[103,143,183],"trial":[104],"data,":[105],"choosing":[106],"an":[107],"appropriate":[108],"crucial,":[111],"particularly":[112],"ensure":[114],"sufficient":[115],"information":[116],"estimate":[118],"their":[122,223],"uncertainty.":[123],"Assuming":[124],"known":[126],"NLMEM":[127],"joint":[133],"distribution":[134],"the":[138,145,149,154,182,193,228,234,282],"target":[139],"population":[140],"from":[141],"previous":[142],"studies,":[144],"proposed":[146],"approach":[147],"optimises":[148],"among":[153],"subjects":[155,285],"be":[157],"included":[158],"trial.":[162],"It":[163],"aims":[164],"achieving":[165],"better":[166],"overall":[167],"parameter":[168],"estimations":[169],"therefore":[171],"increase":[172],"power":[173,290],"statistical":[175],"tests":[176,293],"on":[177,192],"relevance":[184,295],"non-relevance":[186],"relationships.":[188],"The":[189,211,237],"methodology":[190],"relies":[191],"introduces":[198],"fast":[200],"deterministic":[202],"method,":[204],"leveraging":[205],"modelling.":[210],"domains":[212],"continuous":[214],"were":[216,274],"divided":[217],"into":[218],"meaningful":[220],"intervals":[221],"proportions":[224],"optimised,":[225],"along":[226],"proportion":[229],"each":[231],"category":[232],"discrete":[235],"covariates.":[236],"optimisation":[238,277],"problem":[239,245],"was":[240,278],"formulated":[241],"as":[242,272],"convex":[244],"subject":[246],"linear":[248],"constraints,":[249],"allowing":[250],"resolution":[251],"Projected":[253],"Gradient":[254],"Descent":[255],"algorithm.":[256],"Different":[257],"scenarios":[258],"pharmacokinetics":[261],"model":[262],"either":[264],"biological":[265],"measurements":[266],"function":[271],"explored.":[275],"shown":[279],"reduce":[281],"number":[283],"achieve":[288],"desired":[289],"non-relevance.":[297]},"counts_by_year":[],"updated_date":"2026-04-02T13:48:15.688549","created_date":"2026-03-25T00:00:00"}