Mohammad Ehsanul Karim, Md Belal Hossain, Huah Shin Ng, Feng Zhu, Hanna A Frank, Helen Tremlett
{"title":"评估高维代理数据在多发性硬化症混杂校正研究中的作用：一个案例研究。","authors":"Mohammad Ehsanul Karim, Md Belal Hossain, Huah Shin Ng, Feng Zhu, Hanna A Frank, Helen Tremlett","doi":"10.1002/pds.70112","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Given the historical use of limited confounders in multiple sclerosis (MS) studies utilizing administrative health data, this brief report evaluates the impact of incorporating high-dimensional proxy information on confounder adjustment in MS research. We have implemented high-dimensional propensity score (hdPS) and high-dimensional disease risk score (hdDRS) methods to assess changes in effect estimates for the association between disease-modifying drugs (DMDs) and all-cause mortality in an MS cohort from British Columbia (BC), Canada.</p><p><strong>Methods: </strong>We conducted a population-based retrospective study using linked administrative databases from BC, including health insurance registries, demographics, physician visits, hospitalizations, prescriptions, and vital statistics. The cohort comprised 19 360 individuals with MS, followed from January 1, 1996, to December 31, 2017. DMD exposure was defined as at least 180 days of use for beta-interferon or glatiramer acetate, or at least 90 days for other DMDs. The outcome was time to all-cause mortality. We compared Cox proportional hazards models adjusting for investigator-specified covariates with those incorporating additional empirical covariates using hdPS and hdDRS methods.</p><p><strong>Results: </strong>In the unadjusted analysis, DMD exposure was associated with a 69% lower risk of mortality (HR 0.31; 95% CI: 0.27-0.36). Adjusting for investigator-specified covariates, the adjusted hazard ratio (aHR) was 0.76 (95% CI: 0.65-0.89). HdPS analyses showed a 20%-23% lower mortality risk (aHRs: 0.77 to 0.80), while hdDRS analyses indicated a 19%-21% reduction (aHRs: 0.79 to 0.81).</p><p><strong>Conclusions: </strong>Incorporating high-dimensional proxy information resulted in minor variations in effect estimates compared to traditional covariate adjustment. These findings suggest that the impact of residual confounding in the question under consideration may be modest. Further research should explore additional data dimensions and replicate these findings across different datasets.</p>","PeriodicalId":19782,"journal":{"name":"Pharmacoepidemiology and Drug Safety","volume":"34 2","pages":"e70112"},"PeriodicalIF":2.4000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791124/pdf/","citationCount":"0","resultStr":"{\"title\":\"Evaluating the Role of High-Dimensional Proxy Data in Confounding Adjustment in Multiple Sclerosis Research: A Case Study.\",\"authors\":\"Mohammad Ehsanul Karim, Md Belal Hossain, Huah Shin Ng, Feng Zhu, Hanna A Frank, Helen Tremlett\",\"doi\":\"10.1002/pds.70112\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Given the historical use of limited confounders in multiple sclerosis (MS) studies utilizing administrative health data, this brief report evaluates the impact of incorporating high-dimensional proxy information on confounder adjustment in MS research. We have implemented high-dimensional propensity score (hdPS) and high-dimensional disease risk score (hdDRS) methods to assess changes in effect estimates for the association between disease-modifying drugs (DMDs) and all-cause mortality in an MS cohort from British Columbia (BC), Canada.</p><p><strong>Methods: </strong>We conducted a population-based retrospective study using linked administrative databases from BC, including health insurance registries, demographics, physician visits, hospitalizations, prescriptions, and vital statistics. The cohort comprised 19 360 individuals with MS, followed from January 1, 1996, to December 31, 2017. DMD exposure was defined as at least 180 days of use for beta-interferon or glatiramer acetate, or at least 90 days for other DMDs. The outcome was time to all-cause mortality. We compared Cox proportional hazards models adjusting for investigator-specified covariates with those incorporating additional empirical covariates using hdPS and hdDRS methods.</p><p><strong>Results: </strong>In the unadjusted analysis, DMD exposure was associated with a 69% lower risk of mortality (HR 0.31; 95% CI: 0.27-0.36). Adjusting for investigator-specified covariates, the adjusted hazard ratio (aHR) was 0.76 (95% CI: 0.65-0.89). HdPS analyses showed a 20%-23% lower mortality risk (aHRs: 0.77 to 0.80), while hdDRS analyses indicated a 19%-21% reduction (aHRs: 0.79 to 0.81).</p><p><strong>Conclusions: </strong>Incorporating high-dimensional proxy information resulted in minor variations in effect estimates compared to traditional covariate adjustment. These findings suggest that the impact of residual confounding in the question under consideration may be modest. 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Evaluating the Role of High-Dimensional Proxy Data in Confounding Adjustment in Multiple Sclerosis Research: A Case Study.
Purpose: Given the historical use of limited confounders in multiple sclerosis (MS) studies utilizing administrative health data, this brief report evaluates the impact of incorporating high-dimensional proxy information on confounder adjustment in MS research. We have implemented high-dimensional propensity score (hdPS) and high-dimensional disease risk score (hdDRS) methods to assess changes in effect estimates for the association between disease-modifying drugs (DMDs) and all-cause mortality in an MS cohort from British Columbia (BC), Canada.
Methods: We conducted a population-based retrospective study using linked administrative databases from BC, including health insurance registries, demographics, physician visits, hospitalizations, prescriptions, and vital statistics. The cohort comprised 19 360 individuals with MS, followed from January 1, 1996, to December 31, 2017. DMD exposure was defined as at least 180 days of use for beta-interferon or glatiramer acetate, or at least 90 days for other DMDs. The outcome was time to all-cause mortality. We compared Cox proportional hazards models adjusting for investigator-specified covariates with those incorporating additional empirical covariates using hdPS and hdDRS methods.
Results: In the unadjusted analysis, DMD exposure was associated with a 69% lower risk of mortality (HR 0.31; 95% CI: 0.27-0.36). Adjusting for investigator-specified covariates, the adjusted hazard ratio (aHR) was 0.76 (95% CI: 0.65-0.89). HdPS analyses showed a 20%-23% lower mortality risk (aHRs: 0.77 to 0.80), while hdDRS analyses indicated a 19%-21% reduction (aHRs: 0.79 to 0.81).
Conclusions: Incorporating high-dimensional proxy information resulted in minor variations in effect estimates compared to traditional covariate adjustment. These findings suggest that the impact of residual confounding in the question under consideration may be modest. Further research should explore additional data dimensions and replicate these findings across different datasets.
期刊介绍：
The aim of Pharmacoepidemiology and Drug Safety is to provide an international forum for the communication and evaluation of data, methods and opinion in the discipline of pharmacoepidemiology. The Journal publishes peer-reviewed reports of original research, invited reviews and a variety of guest editorials and commentaries embracing scientific, medical, statistical, legal and economic aspects of pharmacoepidemiology and post-marketing surveillance of drug safety. Appropriate material in these categories may also be considered for publication as a Brief Report.
Particular areas of interest include:
design, analysis, results, and interpretation of studies looking at the benefit or safety of specific pharmaceuticals, biologics, or medical devices, including studies in pharmacovigilance, postmarketing surveillance, pharmacoeconomics, patient safety, molecular pharmacoepidemiology, or any other study within the broad field of pharmacoepidemiology;
comparative effectiveness research relating to pharmaceuticals, biologics, and medical devices. Comparative effectiveness research is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition, as these methods are truly used in the real world;
methodologic contributions of relevance to pharmacoepidemiology, whether original contributions, reviews of existing methods, or tutorials for how to apply the methods of pharmacoepidemiology;
assessments of harm versus benefit in drug therapy;
patterns of drug utilization;
relationships between pharmacoepidemiology and the formulation and interpretation of regulatory guidelines;
evaluations of risk management plans and programmes relating to pharmaceuticals, biologics and medical devices.
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