{"title":"Up-front matching: an ongoing recruitment method for prospective observational studies that mimics randomization for selected baseline covariates.","authors":"William H Olson, Ibrahim Turkoz","doi":"10.1080/10543406.2024.2373436","DOIUrl":null,"url":null,"abstract":"<p><p>In a prospective observational study (POS) designed to assess the average causal effect of a treatment (e.g. Drug A) compared to a comparator (e.g. Drug B) in the treatment population, enrolling all patients who are assigned to the treatments of interest for follow-up has a potentially large negative impact on the statistical efficiency and bias of the analysis of the outcomes and on the cost of the study. \"Up-front matching\" is an innovative enrollment method for selecting patients for long-term follow-up among those who have already been assigned to treatment or comparator which uses frequency matching and hence avoids the restrictions of individual matching that other methods have used. To achieve potential statistical and logistical efficiencies in the POS, in up-front matching, a target population is defined based on a retrospective database which then enables selecting populations of patients for follow-up that have desirable statistical properties. In particular, the resulting populations of patients who are enrolled look like the population of treatment patients were randomized to treatment or comparator for the baseline covariates that are used to select patients for follow-up. The method is illustrated in detail for a study designed to assess the effect of injectable antipsychotics versus oral antipsychotics.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-14"},"PeriodicalIF":1.2000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biopharmaceutical Statistics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/10543406.2024.2373436","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
In a prospective observational study (POS) designed to assess the average causal effect of a treatment (e.g. Drug A) compared to a comparator (e.g. Drug B) in the treatment population, enrolling all patients who are assigned to the treatments of interest for follow-up has a potentially large negative impact on the statistical efficiency and bias of the analysis of the outcomes and on the cost of the study. "Up-front matching" is an innovative enrollment method for selecting patients for long-term follow-up among those who have already been assigned to treatment or comparator which uses frequency matching and hence avoids the restrictions of individual matching that other methods have used. To achieve potential statistical and logistical efficiencies in the POS, in up-front matching, a target population is defined based on a retrospective database which then enables selecting populations of patients for follow-up that have desirable statistical properties. In particular, the resulting populations of patients who are enrolled look like the population of treatment patients were randomized to treatment or comparator for the baseline covariates that are used to select patients for follow-up. The method is illustrated in detail for a study designed to assess the effect of injectable antipsychotics versus oral antipsychotics.
期刊介绍:
The Journal of Biopharmaceutical Statistics, a rapid publication journal, discusses quality applications of statistics in biopharmaceutical research and development. Now publishing six times per year, it includes expositions of statistical methodology with immediate applicability to biopharmaceutical research in the form of full-length and short manuscripts, review articles, selected/invited conference papers, short articles, and letters to the editor. Addressing timely and provocative topics important to the biostatistical profession, the journal covers:
Drug, device, and biological research and development;
Drug screening and drug design;
Assessment of pharmacological activity;
Pharmaceutical formulation and scale-up;
Preclinical safety assessment;
Bioavailability, bioequivalence, and pharmacokinetics;
Phase, I, II, and III clinical development including complex innovative designs;
Premarket approval assessment of clinical safety;
Postmarketing surveillance;
Big data and artificial intelligence and applications.