Validity of Drug and Diagnosis Data in Pharmacoepidemiology

Ritchey, Mary E.; West, Suzanne L.; Maldonado, George

doi:10.1002/9781119413431.ch37

Cited by 5 publications

(6 citation statements)

References 156 publications

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“…For clarity, we refer to the random sample as “validation study sample” and to the entire population as the “validation study population.” A hypothetical summary of a validation study result is shown in Table 1 (adapted from Figure. 37.1 of Ritchey et al [ 6 ]). The rows represent the outcomes (“positive” or “negative”) as identified by the proposed phenotype algorithm.…”

Section: Methodsmentioning

confidence: 99%

“…Because the algorithm is imperfect, as characterized by the validation study results, we need to understand how it performs when applied to the DB study. Two such measures of performance are the positive-predictive value (PPV) and the negative-predictive value (NPV) [ 6 ]. In the developments below, estimates of sensitivity, specificity, and disease prevalence are assumed to be available from past validation studies or other sources.…”

Section: Methodsmentioning

confidence: 99%

“…We now examine the impact of misclassifications on relative measures of risk, namely, the risk ratio (RR). As stated by Ritchey et al, the ultimate criterion for the importance of misclassification is the degree of bias exerted on relative measures of risk [ 6 ].…”

Section: Methodsmentioning

confidence: 99%

“…If the RR TRUE is greater than 1, then the bias term is always negative in this “ideal” situation (see Appendix B, Additional file 1 ). In real-life situations, there may be other sources of bias so that the overall bias may not be negative [ 6 , 26 ].…”

Section: Methodsmentioning

confidence: 99%

“…Some general references related to validation studies are available [ 4 – 9 ]. For common outcomes, systematic reviews of validation studies are available [ 6 , 10 – 17 ].…”

Section: Introductionmentioning

confidence: 99%

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Use of diagnostic likelihood ratio of outcome to evaluate misclassification bias in the planning of database studies

Hiro

Nakazuru

2022

BMC Med Inform Decis Mak

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Background The diagnostic likelihood ratio (DLR) and its utility are well-known in the field of medical diagnostic testing. However, its use has been limited in the context of an outcome validation study. We considered that wider recognition of the utility of DLR would enhance the practices surrounding database studies. This is particularly timely and important since the use of healthcare-related databases for pharmacoepidemiology research has greatly expanded in recent years. In this paper, we aimed to advance the use of DLR, focusing on the planning of a new database study. Methods Theoretical frameworks were developed for an outcome validation study and a comparative cohort database study; these two were combined to form the overall relationship. Graphical presentations based on these relationships were used to examine the implications of validation study results on the planning of a database study. Additionally, novel uses of graphical presentations were explored using some examples. Results Positive DLR was identified as a pivotal parameter that connects the expected positive-predictive value (PPV) with the disease prevalence in the planned database study, where the positive DLR is equal to sensitivity/(1-specificity). Moreover, positive DLR emerged as a pivotal parameter that links the expected risk ratio with the disease risk of the control group in the planned database study. In one example, graphical presentations based on these relationships provided a transparent and informative summary of multiple validation study results. In another example, the potential use of a graphical presentation was demonstrated in selecting a range of positive DLR values that best represented the relevant validation studies. Conclusions Inclusion of the DLR in the results section of a validation study would benefit potential users of the study results. Furthermore, investigators planning a database study can utilize the DLR to their benefit. Wider recognition of the full utility of the DLR in the context of a validation study would contribute meaningfully to the promotion of good practice in planning, conducting, analyzing, and interpreting database studies.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Some general references related to validation studies are available [ 4 – 9 ]. For common outcomes, systematic reviews of validation studies are available [ 6 , 10 – 17 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Use of diagnostic likelihood ratio of outcome to evaluate misclassification bias in the planning of database studies

Hiro

Nakazuru

2022

BMC Med Inform Decis Mak

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Validity of Drug and Diagnosis Data in Pharmacoepidemiology

Ritchey

West

Maldonado

2021

Textbook of Pharmacoepidemiology

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Core concepts in pharmacoepidemiology: Key biases arising in pharmacoepidemiologic studies

Acton

Willis

Hennessy

2022

Pharmacoepidemiology and Drug

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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.

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Validity of Drug and Diagnosis Data in Pharmacoepidemiology

Cited by 5 publications

References 156 publications

Use of diagnostic likelihood ratio of outcome to evaluate misclassification bias in the planning of database studies

Use of diagnostic likelihood ratio of outcome to evaluate misclassification bias in the planning of database studies

Validity of Drug and Diagnosis Data in Pharmacoepidemiology

Core concepts in pharmacoepidemiology: Key biases arising in pharmacoepidemiologic studies

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