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Methodological Approaches to Heterogeneous Data Sources: An FDA View

Methodological Approaches to Heterogeneous Data Sources: An FDA View. Gregory Campbell Director, Division of Biostatistics Office of Surveillance and Biometrics Center for Devices and Radiological Health MDEpiNet Annual Meeting October 15, 2014. Heterogeneous Data Sources.

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Methodological Approaches to Heterogeneous Data Sources: An FDA View

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  1. Methodological Approaches to Heterogeneous Data Sources: An FDA View Gregory CampbellDirector, Division of BiostatisticsOffice of Surveillance and BiometricsCenter for Devices and Radiological HealthMDEpiNet Annual MeetingOctober 15, 2014

  2. Heterogeneous Data Sources • PreMarket Randomized Trials • PreMarket Observational Studies • Compared to historical control • Compared to a performance goal • PostMarket Observational Condition-of-Approval Studies • PostMarket Registries (observational)

  3. Heterogeneous Populations • If the populations studied in multiple studies are different populations this will likely result in a bias. • It may be possible to adjust for the difference using baseline covariates, provided participant-level data are available. • There may still be a bias between randomized and non-randomized studies. This could be associated with the bias of knowing the treatment versus not.

  4. Two Directions • Using postmarket (or other PreMarket data) for PreMarket submissions • Examples include 2 Left Ventricular Heart Assist Devices (LVADs), mitral valves, transcatheter valves, stents. • Using Premarket (and PostMarket) Data to make postmarket assessments.

  5. I. Methodologies for PreMarket Submissions • Propensity score methods to make the other studies comparable with the prospective study of interest. • Bayesian methods to combine data from various sources, usually using a non-informative prior and a hierarchical Bayesian approach (this assumes study exchangeability)

  6. A. Propensity Score (PS) Methodology • PS-- Replace the collection of confounding covariates with one scalar function of these covariates: the propensity score (PS). • PS is the conditional prob. of receiving Exp. Trt. rather than Control, given a collection of observed covariates. Ref: Rubin (1973) Biometrics

  7. A. PS in Building Stage • It is crucial to mask or blind the PS modeling from the outcome data. • If the observational data does not collect all the important covariates (not just ones correlated with the outcome), the PS analysis is worthless. • A model that improves the balance by deleting covariates in the logistic regression model cannot be correct.

  8. A. PS in Building Stage May Not Work • Whereas it might make sense to trim many observations from the control (especially if it is large), under no circumstances should you trim (discard) observations from the new treatment arm (since this non-prospective act would make the label impossible to write). • A PS model does not guarantee that PS will be similar OR that the covariates will be balanced; both need to be checked.

  9. B. Bayesian Statistics • Finalized February 5, 2010. • http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm071072.htm

  10. Bayesian Hierarchical Modeling • A method to allow the “borrowing of strength” from prior data • Assumes that the different studies are “exchangeable” • Adaptively decides how much strength to borrow based on how similar the current study is to the previous ones. • Can be problematic if only one or two prior studies.

  11. Balancing Premarket and Postmarket Data Collection • Draft guidance issued April 23, 2014. • Balancing Premarket and Postmarket Data Collection for Devices Subject to Premarket Approval: Draft Guidance for Industry and Food and Drug Administration Staff • Legal basis: Section 513(a)(3)(C) of FD&C Act: • In making a determination of a reasonable assurance of the effectiveness of a device for which [a premarket approval application] has been submitted, the Secretary shall consider whether the extent of data that otherwise would be required for approval of the application with respect to effectiveness can be reduced through reliance on postmarket controls.

  12. II. Methodologies for PostMarket Inference • Bayesian methods • Meta-analysis (frequentist or Bayesian) • Adjust for different covariates in studies either by regression methods or propensity scores • Unique Device Identifiers (UDI) will play a crucial role in the future

  13. Thank you!

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