1. Exposure Definition and Measurement in Observational Comparative Effectiveness Research Prepared for: Agency for Healthcare Research and Quality (AHRQ) www.ahrq.gov

2. This presentation will:  Propose a definition of exposure that is consistent with the clinical/ conceptual basis for the research question  Provide a rationale for choice of exposure time window  Describe the proposed data source(s) and explain how they are adequate and appropriate for defining exposure  Provide evidence of validity of the operational definition of exposure with estimates of sensitivity, specificity, and positive predictive value, when possible  Support the unit of analysis chosen for exposure measurement (e.g., person-months of exposure) and discuss the trade-offs for alternative units of measurement  Address issues of differential and nondifferential bias related to exposure measurement and propose strategies for reducing error and bias, where possible Outline of Material

3.  In epidemiology, the term exposure can be broadly applied to any factor that may be associated with an outcome of interest, including:  Primary explanatory variable of interest  Other variables associated with the outcome (confounders or effect modifiers)  “One-time” interventions may only require determining if and when (surgery or vaccine administration).  Pharmacological or other longer term exposures (educational interventions) involve measuring intensity (dose, frequency, duration).  Data elements define how exposure is measured (often proxy indicators such as dispensing records). Introduction

4.  Linking exposure measurement to study objectives:  The conceptual basis of a study is the foundation for developing an operational definition of exposure.  Distinguish short-term use from long-term use.  Examining the exposure/outcome relationship:  Lay out the theoretical and biological link between exposure and the outcome of interest (drawn from conceptual framework).  If the primary exposure of interest is a medication, can describe how pharmacology/pharmacokinetics/ pharmacodynamics informed the exposure definition. Conceptual Considerations for Exposure Measurement

5. Examples of Exposure/Outcome Relationships

6. Timeline of Exposure, Induction Period, Latent Period, and Outcome Adapted from White E, Armstrong BK, Saracci R. Principles of exposure measurement in epidemiology. 2nd ed. New York, NY: Oxford University Press Inc.; 2008. p. 18. By permission of Oxford University Press (www.oup.com). Copyright © 2008. All rights reserved.

7.  Existing electronic data  A consistent and accurate way to identify the exposure in the dataset?  One code or multiple codes? Variable code types?  If too broad, can lead to exposure misclassification  Validation methods to justify decisions  Prospective data collection  Abstraction of paper medical records  Characteristics of exposure and patient population can influence validity  Self-reported information, recall (timeframe, frequency) Data Sources for Exposure Measurement

8.  Time window of exposure  Defined as the period of time during which the exposure is having its effects relevant to the outcome of interest  Must consider the induction and latent periods when defining the exposure time window  Practical limitations of study data should be acknowledged and sensitivity analyses performed to evaluate robustness of the results to the time window  Unit of analysis  Largely dictated by the nature of the intervention  Can be defined at the patient level if it does not vary with time  Person-time can be used when exposure status varies over the course of the study period Creating an Exposure Definition

9.  Measurement scale  The more precise, the less measurement error  Dichotomous variables  Continuous covariates (typically used when there is a dose-response relationship)  Categorical variables (may introduce less bias)  Precision of exposure measure  Source of data can limit precision (dispensing records vs. actual usage)  Be aware of the benefits and limitations of the data source, and avoid exposure misclassification Creating an Exposure Definition

10.  Dosage and dose-response  The cumulative dose (total amount of exposure over a specified time period) is often optimal for adequately defining exposure (and existence of a threshold effect).  Frequency, amount/dose of each occurrence, and duration of exposure  It is applicable to medications and health services interventions.  The definition of exposure must be specific to the exposure of interest and must avoid misclassification due to the availability of other dosage forms or routes of administration.  Behavioral factors (medication adherence) might modify the effect of the observed association. Creating an Exposure Definition

11.  Changes in exposure status  Pertains particularly if patients switch between active exposures when two or more are being investigated  Determining “spillover” effects or biological effects  Exposure to multiple therapies  Lack of control over all medications used in a study  Exposure to other medications not randomly distributed  Must consider the influence of other exposures on the outcome, not just primary exposure  Multiplicative/additive effects Additional Exposure Considerations

12.  Differential misclassification: when an error in exposure measurement is dependent on the event of interest (leads to biased estimates towards or away from the null)  Nondifferential misclassification: when errors in measurement of exposure are proportionally the same in both the group that does and does not experience the outcome of interest  Sources of error: failure to account for changes in exposure, a short exposure time window, measurement during induction/latent periods, and using services not captured in the data source  Measurement time bias: bias in measurable exposure times  Immortal time bias: when person-time is inappropriately assigned to an exposure category Issues of Bias

13.  To operationalize exposure in comparative effectiveness research using observational data:  The clinical pathways and conceptual framework that motivate the comparative effectiveness research question should be the guide.  The characteristics of the exposure and the outcome of interest must be known.  Researchers must be aware of the level of detail on the exposure in a dataset and the options for characterizing it.  Researchers must deliberate the approaches used to limit the potential for bias and measurement error. Conclusion

14. Summary Checklist (1 of 2) GuidanceKey Considerations Propose a definition of exposure that is consistent with the clinical/conceptual basis for the research question Consider the physiological effects of the exposure/intervention when creating an operational definition of exposure Determine the most suitable scale for the measurement of exposure Provide a rationale for choice of exposure time window For medications, consider factors such as dose, duration of treatment, pharmacodynamic/ pharmacokinetic properties (e.g., half-life), and known or hypothesized biological mechanisms associated with the medication of interest Describe the proposed data source(s) and explain how they are adequate and appropriate for defining exposure

15. Summary Checklist (2 of 2) GuidanceKey Considerations Provide evidence of validity of the operational definition of exposure with estimates of sensitivity, specificity, and positive predictive value, when possible If there are no validation studies to define the exposure of interest, use measures and definitions that have been most commonly reported in the literature to facilitate comparison of results Alternative definitions could be developed and used in addition to a “commonly used” definition for exposure, particularly if there are reasons to suspect there may be more accurate definitions available Support the unit of analysis chosen for exposure measurement (e.g., person-months of exposure) and discuss the trade-offs for alternative units of measurement Address issues of differential and nondifferential bias related to exposure measurement and propose strategies for reducing error and bias, where possible