Using probability of drug use as independent variable in a register‐based pharmacoepidemiological cause‐effect study—An application of the reverse waiting time distribution

Hallas, Jesper; Pottegård, Anton; Støvring, Henrik

doi:10.1002/pds.4326

Cited by 4 publications

(9 citation statements)

References 11 publications

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“…For the two last approaches, we kept all generated individuals in the analysis to maintain comparability of standard errors with the other analyses, even though it is common practice in pharmacoepidemiologic studies to only include individuals which have no observed prescription redemptions (the reference group) or are classified as exposed (i.e. have a redemption inside the prespecified main exposure window) [ 3 , 7 ].…”

Section: Methodsmentioning

confidence: 99%

“…In simulation studies and an empirical validation study for Warfarin, the method provided estimates with low bias, unless the model was severely misspecified [ 1 , 2 ]. In a recent study, we suggested to use the reverse WTD to estimate the probability of being exposed on the index date, and use this probability as exposure variable [ 3 ]. The objective was to better allow for the fact that the actual exposure status is not recorded, and that the analyst may therefore introduce misclassification bias when forcing a dichotomy upon exposure.…”

Section: Introductionmentioning

confidence: 99%

“…The approach was intended to directly represent the analyst’s uncertainty about actual exposure by using probability of being exposed as exposure covariate. We found that the approach improved statistical efficiency when studying the risk of upper gastrointestinal bleeding (UGIB) associated with use of a nonsteroidal anti-inflammatory drug (NSAID) [ 3 ]. The suggested method consisted of two steps: First, a parametric reverse WTD model was fitted to the last observed NSAID prescription redemption among controls before their index date to estimate their probability of being users on the index date.…”

Section: Introductionmentioning

confidence: 99%

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A new likelihood model for analyses of pharmacoepidemiologic case–control studies which avoids decision rules for determining latent exposure status

Støvring

Pottegård

Hallas

2021

BMC Med Res Methodol

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Background Case–control studies based on pharmaco-epidemiological databases typically use decision rules to determine exposure status from information on dates of prescription redemptions, although this induces misclassification. The reverse Waiting Time Distribution has been suggested as a likelihood based model to estimate the latent exposure status, and we therefore suggest to extend this into a joint likelihood based model, which incorporates both the latent exposure status and the exposure-outcome association. This will achieve consistency and efficiency of the estimates, i.e. they can be expected to be asymptotically unbiased and have optimal precision. Methods We established a joint likelihood for the observed case–control status and last prescription redemption before the index date. The likelihood combines the ordinary logistic regression likelihood and the reverse Waiting Time Distribution, and allows inclusion of covariates in both parts to adjust for observed confounders. We conducted a simulation study of the new model and standard models based on decision rules for exposure and the probability of being exposed, respectively, to assess the relative bias and variability of estimates. Lastly, we applied the models to a case–control study on use of nonsteroidal anti-inflammatory drugs and risk of upper-gastrointestinal bleeding. Results In simulation studies the new model had low relative bias (< 1.4%) and largely retained nominal coverage probabilities (90.2% to 95.1% of nominal 95% confidence intervals), also when moderate misspecification was introduced. All standard methods generally had substantial bias (-21.1% to 17.0%) and low coverage probabilities (0.0% to 68.9%). When analyzing the empirical case–control study, the new method estimated the effect of nonsteroidal anti-inflammatory drugs on risk of with upper-gastrointestinal bleeding hospitalization to 2.52 (1.59 – 3.45), whereas the other methods had estimates ranging from 3.52 (2.19 – 5.65) to 5.17 (2.40 – 11.11). Conclusions Unlike standard methods, the proposed model gave nearly unbiased estimates with adequate coverage probabilities in simulation studies. The developed model demonstrates the potential for the reverse Waiting Time Distribution to be integrated with existing likelihood based analyses in pharmacoepidemiological studies.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new likelihood model for analyses of pharmacoepidemiologic case–control studies which avoids decision rules for determining latent exposure status

Støvring

Pottegård

Hallas

2021

BMC Med Res Methodol

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“…have a redemption inside the prespecified main exposure window). (1,6) For each method and setting we estimated the median relative bias of the estimated OR, the median standard error of the log-OR, the coverage probability of nominal 95% confidence intervals and finally the relative variance inflation factor with respect to the optimal analysis when exposure status is directly observed.…”

Section: Simulation Studymentioning

confidence: 99%

“…In a recent study, we suggested to use the reverse Waiting Time Distribution (WTD) to estimate the probability of being exposed on the index date, and use this probability as exposure variable. (1) The objective was to better allow for the fact that the actual exposure status is not recorded, and that the analyst may therefore introduce misclassification bias when forcing a dichotomy upon exposure. The approach was intended to directly represent the analyst's uncertainty about actual exposure by using probability of being exposed as exposure covariate.…”

Section: Introductionmentioning

confidence: 99%

A new likelihood model for analyses of pharmacoepidemiologic case-control studies which avoids decision rules for determining latent exposure status

Støvring

Pottegård

Hallas

2020

Preprint

Self Cite

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Background Case-control studies based on pharmaco-epidemiological databases typically use decision rules to determine exposure status from information on dates of prescription redemptions, although this induces misclassification. The reverse Waiting Time Distribution has been suggested as a likelihood based model to estimate the latent exposure status, and we therefore suggest to extend this into a joint likelihood based model, which incorporates both the latent exposure status and the exposure-outcome association. This will achieve consistency and efficiency of the estimates, i.e. they are asymptotically unbiased and have optimal precision. Methods We established a joint likelihood for the observed case-control status and last prescription redemption before the index date. The likelihood combines the ordinary logistic regression likelihood and the reverse Waiting Time Distribution, and allows inclusion of covariates in both parts to adjust for observed confounders. We conducted a simulation study of the new model and standard models based on decision rules for exposure and the probability of being exposed, respectively, to assess the relative bias and variability of estimates. Lastly, we applied the models to a case-control study on use of nonsteroidal anti-inflammatory drugs and risk of upper-gastrointestinal bleeding. Results In simulation studies the new model had low relative bias (< 1.4%) and largely retained nominal coverage probabilities (90.2–95.1% of nominal 95% confidence intervals), also when moderate misspecification was introduced. All standard methods generally had substantial bias (-21.1–17.0%) and low coverage probabilities (0.0–68.9%). The new method estimated the effect of nonsteroidal anti-inflammatory drugs on risk of with upper-gastrointestinal bleeding hospitalization to 2.52 (1.59–3.45), whereas the other methods had estimates ranging from 3.52 (2.19–5.65) to 5.17 (2.40–11.11). Conclusions Unlike standard methods, the proposed model consistently gave unbiased estimates with adequate coverage probabilities. The model demonstrates the potential for the reverse Waiting Time Distribution to be integrated with existing likelihood based analyses in pharmacoepidemiological studies.

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A data‐informed approach using individualised dispensing patterns to estimate medicine exposure periods and dose from pharmaceutical claims data

Bharat

Degenhardt

Pearson

et al. 2022

Pharmacoepidemiology and Drug

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Pharmaceutical claims data are often used as the primary information source to define medicine exposure periods in pharmacoepidemiological studies. However, often critical information on directions for use and the intended duration of medicine supply are not available. In the absence of this information, alternative approaches are needed to support the assignment of exposure periods. This study summarises the key methods commonly used to estimate medicine exposure periods and dose from pharmaceutical claims data; and describes a method using individualised dispensing patterns to define time-dependent estimates of medicine exposure and dose. This method extends on important features of existing methods and also accounts for recent changes in an individual's medicine use. Specifically, this method constructs medicine exposure periods and estimates the dose used by considering characteristics from an individual's prior dispensings, accounting for the time between prior dispensings and the amount supplied at prior dispensings. Guidance on the practical applications of this method is also provided. Although developed primarily for application to databases, which do not contain duration of supply or dose information, use of this method may also facilitate investigations when such information is available and there is a need to consider individualised and/or changing dosing regimens. By shifting the reliance on prescribed duration and dose to determine exposure and dose estimates, individualised dispensing information is used to estimate patterns of exposure and dose for an individual. Reflecting real-world individualised use of medicines with complex and variable dosing regimens, this method offers a pragmatic approach that can be applied to all medicine classes.

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Using probability of drug use as independent variable in a register‐based pharmacoepidemiological cause‐effect study—An application of the reverse waiting time distribution

Cited by 4 publications

References 11 publications

A new likelihood model for analyses of pharmacoepidemiologic case–control studies which avoids decision rules for determining latent exposure status

A new likelihood model for analyses of pharmacoepidemiologic case–control studies which avoids decision rules for determining latent exposure status

A new likelihood model for analyses of pharmacoepidemiologic case-control studies which avoids decision rules for determining latent exposure status

A data‐informed approach using individualised dispensing patterns to estimate medicine exposure periods and dose from pharmaceutical claims data

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