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A common problem in the analysis of multiple data sources, including individual participant data meta-analysis (IPD-MA), is the misclassification of binary variables. Misclassification may lead to biased estimators of model parameters, even when the misclassification is entirely random. We aimed to develop statistical methods that facilitate unbiased estimation of adjusted and unadjusted exposure-outcome associations and between-study heterogeneity in IPD-MA, where the extent and nature of exposure misclassification may vary across studies.
We present Bayesian methods that allow misclassification of binary exposure variables to depend on study- and participant-level characteristics. In an example of the differential diagnosis of dengue using two variables, where the gold standard measurement for the exposure variable was unavailable for some studies which only measured a surrogate prone to misclassification, our methods yielded more accurate estimates than analyses naive with regard to misclassification or based on gold standard measurements alone. In a simulation study, the evaluated misclassification model yielded valid estimates of the exposure-outcome association, and was more accurate than analyses restricted to gold standard measurements.
Our proposed framework can appropriately account for the presence of binary exposure misclassification in IPD-MA. It requires that some studies supply IPD for the surrogate and gold standard exposure, and allows misclassification to follow a random effects distribution across studies conditional on observed covariates (and outcome). The proposed methods are most beneficial when few large studies that measured the gold standard are available, and when misclassification is frequent.
Objective: To determine whether assessment tools for non-randomised studies (NRS) address critical elements that influence the validity of NRS findings for comparative safety and effectiveness of medications.
Design: Systematic review and Delphi survey.
Data sources: We searched PubMed, Embase, Google, bibliographies of reviews and websites of influential organisations from inception to November 2019. In parallel, we conducted a Delphi survey among the International Society for Pharmacoepidemiology Comparative Effectiveness Research Special Interest Group to identify key methodological challenges for NRS of medications. We created a framework consisting of the reported methodological challenges to evaluate the selected NRS tools.
Study selection Checklists or scales assessing NRS.
Data extraction: Two reviewers extracted general information and content data related to the prespecified framework.
Results: Of 44 tools reviewed, 48% (n=21) assess multiple NRS designs, while other tools specifically addressed case-control (n=12, 27%) or cohort studies (n=11, 25%) only. Response rate to the Delphi survey was 73% (35 out of 48 content experts), and a consensus was reached in only two rounds. Most tools evaluated methods for selecting study participants (n=43, 98%), although only one addressed selection bias due to depletion of susceptibles (2%). Many tools addressed the measurement of exposure and outcome (n=40, 91%), and measurement and control for confounders (n=40, 91%). Most tools have at least one item/question on design-specific sources of bias (n=40, 91%), but only a few investigate reverse causation (n=8, 18%), detection bias (n=4, 9%), time-related bias (n=3, 7%), lack of new-user design (n=2, 5%) or active comparator design (n=0). Few tools address the appropriateness of statistical analyses (n=15, 34%), methods for assessing internal (n=15, 34%) or external validity (n=11, 25%) and statistical uncertainty in the findings (n=21, 48%). None of the reviewed tools investigated all the methodological domains and subdomains.
Conclusions: The acknowledgement of major design-specific sources of bias (eg, lack of new-user design, lack of active comparator design, time-related bias, depletion of susceptibles, reverse causation) and statistical assessment of internal and external validity is currently not sufficiently addressed in most of the existing tools. These critical elements should be integrated to systematically investigate the validity of NRS on comparative safety and effectiveness of medications.
Systematic review protocol and registration: https://osf.io/es65q.
If individual participant data are available from multiple studies or clusters, then a prediction model can be externally validated multiple times. This allows the model's discrimination and calibration performance to be examined across different settings. Random-effects meta-analysis can then be used to quantify overall (average) performance and heterogeneity in performance. This typically assumes a normal distribution of 'true' performance across studies. We conducted a simulation study to examine this normality assumption for various performance measures relating to a logistic regression prediction model. We simulated data across multiple studies with varying degrees of variability in baseline risk or predictor effects and then evaluated the shape of the between-study distribution in the C-statistic, calibration slope, calibration-in-the-large, and E/O statistic, and possible transformations thereof. We found that a normal between-study distribution was usually reasonable for the calibration slope and calibration-in-the-large; however, the distributions of the C-statistic and E/O were often skewed across studies, particularly in settings with large variability in the predictor effects. Normality was vastly improved when using the logit transformation for the C-statistic and the log transformation for E/O, and therefore we recommend these scales to be used for meta-analysis. An illustrated example is given using a random-effects meta-analysis of the performance of QRISK2 across 25 general practices.
