Propensity Score Methods for Confounding Control in Observational Studies of Therapeutics for COVID-19 Infection

被引:0
作者
Hurwitz, Kathleen E. [1 ]
Rathnayaka, Nuvan [1 ]
Hendrickson, Kayla [1 ]
Brookhart, M. Alan [2 ]
机构
[1] Target RWE, Epidemiol, Durham, NC USA
[2] Duke Univ, Dept Populat Hlth Sci, 215 Morris St,DUMC 104023, Durham, NC 27710 USA
关键词
observational studies; propensity score matching; COVID-19; inverse probability of treatment weighting; confounding; INFLUENZA VACCINE EFFECTIVENESS; INVERSE PROBABILITY; CAUSAL INFERENCE; SENSITIVITY-ANALYSIS; BIAS; OUTCOMES; ADJUSTMENT; KNOWLEDGE; DIAGRAMS; TRIAL;
D O I
10.1093/cid/ciae516
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
The authors provide a brief overview of different propensity score methods that can be used in observational research studies that lack randomization. Under specific assumptions, these methods result in unbiased estimates of causal effects, but the different ways propensity scores are used may require different assumptions and result in estimated treatment effects that can have meaningfully different interpretations. The authors review these issues and consider their implications for studies of therapeutics for coronavirus disease 2019.
引用
收藏
页码:S131 / S136
页数:6
相关论文
共 45 条
  • [1] Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies
    Austin, Peter C.
    Stuart, Elizabeth A.
    [J]. STATISTICS IN MEDICINE, 2015, 34 (28) : 3661 - 3679
  • [2] Using the Standardized Difference to Compare the Prevalence of a Binary Variable Between Two Groups in Observational Research
    Austin, Peter C.
    [J]. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2009, 38 (06) : 1228 - 1234
  • [3] Doubly robust estimation in missing data and causal inference models
    Bang, H
    [J]. BIOMETRICS, 2005, 61 (04) : 962 - 972
  • [4] Counterpoint: The Treatment Decision Design
    Brookhart, M. Alan
    [J]. AMERICAN JOURNAL OF EPIDEMIOLOGY, 2015, 182 (10) : 840 - 845
  • [5] Propensity Score Methods for Confounding Control in Nonexperimental Research
    Brookhart, M. Alan
    Wyss, Richard
    Layton, J. Bradley
    Stuerner, Til
    [J]. CIRCULATION-CARDIOVASCULAR QUALITY AND OUTCOMES, 2013, 6 (05): : 604 - 611
  • [6] Confounding Control in Healthcare Database Research Challenges and Potential Approaches
    Brookhart, M. Alan
    Sturmer, Til
    Glynn, Robert J.
    Rassen, Jeremy
    Schneeweiss, Sebastian
    [J]. MEDICAL CARE, 2010, 48 (06) : S114 - S120
  • [7] Adjusted survival curves with inverse probability weights
    Cole, SR
    Hernán, MA
    [J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2004, 75 (01) : 45 - 49
  • [8] Constructing inverse probability weights for marginal structural models
    Cole, Stephen R.
    Hernan, Miguel A.
    [J]. AMERICAN JOURNAL OF EPIDEMIOLOGY, 2008, 168 (06) : 656 - 664
  • [9] The Consistency Statement in Causal Inference A Definition or an Assumption?
    Cole, Stephen R.
    Frangakis, Constantine E.
    [J]. EPIDEMIOLOGY, 2009, 20 (01) : 3 - 5
  • [10] Characterizing Imbalance in the Tails of the Propensity Score Distribution
    DiPrete, Bethany L.
    Girman, Cynthia J.
    Mavros, Panagiotis
    Breskin, Alexander
    Brookhart, M. Alan
    [J]. AMERICAN JOURNAL OF EPIDEMIOLOGY, 2024, 193 (02) : 389 - 403