Dataset Information

A systematic review of how missing data are handled and reported in multi-database pharmacoepidemiologic studies.

ABSTRACT:

Purpose

Pharmacoepidemiologic multi-database studies (MDBS) provide opportunities to better evaluate the safety and effectiveness of medicines. However, the issue of missing data is often exacerbated in MDBS, potentially resulting in bias and precision loss. We sought to measure how missing data are being recorded and addressed in pharmacoepidemiologic MDBS.

Methods

We conducted a systematic literature search in PubMed for pharmacoepidemiologic MDBS published between 1st January 2018 and 31st December 2019. Included studies were those that used ≥2 distinct databases to assess the same safety/effectiveness outcome associated with a drug exposure. Outcome variables extracted from the studies included strategies to execute a MDBS, reporting of missing data (type, bias evaluation) and the methods used to account for missing data.

Results

Two thousand seven hundred and twenty-six articles were identified, and 62 studies were included: using data from either North America (56%), Europe (31%), multiple regions (11%) or East-Asia (2%). Thirty-five (56%) articles reported missing data: 11 of these studies reported that this could have introduced bias and 19 studies reported a method to address missing data. Thirteen (68%) carried out a complete case analysis, 2 (11%) applied multiple imputation, 2 (11%) used both methods, 1 (5%) used mean imputation and 1 (5%) substituted information from a similar variable.

Conclusions

Just over half of the recent pharmacoepidemiologic MDBS reported missing data and two-thirds of these studies reported how they accounted for it. We should increase our vigilance for database completeness in MDBS by reporting and addressing the missing data that could introduce bias.

SUBMITTER: Hunt NB

PROVIDER: S-EPMC8252545 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Missing data in covariates can result in biased estimates and loss of power to detect associations. It can also lead to other challenges in time-to-event analyses including the handling of time-varying effects of covariates, selection of covariates and their flexible modelling. This review aims to describe how researchers approach time-to-event analyses with missing data. METHODS:Medline and Embase were searched for observational time-to-event studies in oncology published from January 2012 to January 2018. The review focused on proportional hazards models or extended Cox models. We investigated the extent and reporting of missing data and how it was addressed in the analysis. Covariate modelling and selection, and assessment of the proportional hazards assumption were also investigated, alongside the treatment of missing data in these procedures. RESULTS:148 studies were included. The mean proportion of individuals with missingness in any covariate was 32%. 53% of studies used complete-case analysis, and 22% used multiple imputation. In total, 14% of studies stated an assumption concerning missing data and only 34% stated missingness as a limitation. The proportional hazards assumption was checked in 28% of studies, of which, 17% did not state the assessment method. 58% of 144 multivariable models stated their covariate selection procedure with use of a pre-selected set of covariates being the most popular followed by stepwise methods and univariable analyses. Of 69 studies that included continuous covariates, 81% did not assess the appropriateness of the functional form. CONCLUSION:While guidelines for handling missing data in epidemiological studies are in place, this review indicates that few report implementing recommendations in practice. Although missing data are present in many studies, we found that few state clearly how they handled it or the assumptions they have made. Easy-to-implement but potentially biased approaches such as complete-case analysis are most commonly used despite these relying on strong assumptions and where often more appropriate methods should be employed. Authors should be encouraged to follow existing guidelines to address missing data, and increased levels of expectation from journals and editors could be used to improve practice.