Pharmacoepidemiology & Drug Safety

doi: 10.1002/pds.5453pmid: N/A

Weinstein, Erica J.; Ritchey, Mary Elizabeth; Lo Re, Vincent

doi: 10.1002/pds.5537pmid: 36057777

Real‐world healthcare data, including administrative and electronic medical record databases, provide a rich source of data for the conduct of pharmacoepidemiologic studies but carry the potential for misclassification of health outcomes of interest (HOIs). Validation studies are important ways to quantify the degree of error associated with case‐identifying algorithms for HOIs and are crucial for interpreting study findings within real‐world data. This review provides a rationale, framework, and step‐by‐step approach to validating case‐identifying algorithms for HOIs within healthcare databases. Key steps in validating a case‐identifying algorithm within a healthcare database include: (1) selecting the appropriate health outcome; (2) determining the reference standard against which to validate the algorithm; (3) developing the algorithm using diagnosis codes, diagnostic tests or their results, procedures, drug therapies, patient‐reported symptoms or diagnoses, or some combinations of these parameters; (4) selection of patients and sample sizes for validation; (5) collecting data to confirm the HOI; (6) confirming the HOI; and (7) assessing the algorithm's performance. Additional strategies for algorithm refinement and methods to correct for bias due to misclassification of outcomes are discussed. The review concludes by discussing factors affecting the transportability of case‐identifying algorithms and the need for ongoing validation as data elements within healthcare databases, such as diagnosis codes, change over time or new variables, such as patient‐generated health data, are included in these data sources.

Acton, Emily K.; Willis, Allison W.; Hennessy, Sean

doi: 10.1002/pds.5547pmid: 36216785

Pharmacoepidemiology has an increasingly important role in informing and improving clinical practice, drug regulation, and health policy. Therefore, unrecognized biases in pharmacoepidemiologic studies can have major implications when study findings are translated to the real world. We propose a simple taxonomy for researchers to use as a starting point when thinking through some of the most pervasive biases in pharmacoepidemiology. We organize this discussion according to biases best assessed with respect to the study population (including confounding by indication, channeling bias, healthy user bias, and protopathic bias), the study design (including prevalent user bias and immortal time bias), and the data source (including misclassification bias and missing data/loss to follow up). This tutorial defines, provides a curated list of recommended references, and illustrates through relevant case examples these key biases to consider when planning, conducting, or evaluating pharmacoepidemiologic studies.

Parracha, Edna Ribeiro; Advinha, Ana Margarida; Lopes, Manuel José; Oliveira‐Martins, Sofia

doi: 10.1002/pds.5542pmid: 36125022

Coste, Astrid; Wong, Angel; Bokern, Marleen; Bate, Andrew; Douglas, Ian J.

doi: 10.1002/pds.5548pmid: 36218170

Wang, Shirley V.; Pottegård, Anton; Crown, William; Arlett, Peter; Ashcroft, Darren M.; Benchimol, Eric I.; Berger, Marc L.; Crane, Gracy; Goettsch, Wim; Hua, Wei; Kabadi, Shaum; Kern, David M.; Kurz, Xavier; Langan, Sinead; Nonaka, Takahiro; Orsini, Lucinda; Perez‐Gutthann, Susana; Pinheiro, Simone; Pratt, Nicole;

Shu, Di; Webster‐Clark, Michael; Platt, Robert W.; Toh, Sengwee

doi: 10.1002/pds.5527pmid: 35976190

Grimmsmann, Thomas; Himmel, Wolfgang

doi: 10.1002/pds.5536pmid: 36039726

Calip, Gregory S.; Cohen, Aaron; Rohrer, Rebecca; Wang, Xiaoliang; Wang, Xiaoyan; Webster, Amy; Wu, Amy; Griffith, Sandra D.; Showalter, Timothy N.; Miksad, Rebecca A.

doi: 10.1002/pds.5541pmid: 36111444

Vasilakis‐Scaramozza, Catherine; Hagberg, Katrina Wilcox; Persson, Rebecca; Yelland, Eleanor; Williams, Tim; Myles, Puja; Jick, Susan S.

doi: 10.1002/pds.5551pmid: 36251264