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Comorbidity Scores Bias Introduction of issue and background papers Sebastian Schneeweiss, MD, ScD Professor of Medicine and Epidemiology Division of Pharmacoepidemiology and Pharmacoeconomics, Dept of Medicine, Brigham & Women’s Hospital/ Harvard Medical School 1 Potential conflicts of interest PI, Brigham & Women’s Hospital DEcIDE Center for Comparative Effectiveness Research (AHRQ) PI, DEcIDE Methods Center (AHRQ) Co-Chair, Methods Core of the Mini Sentinel System (FDA) Member, national PCORI Methods Committee No paid consulting or speaker fees from pharmaceutical manufacturers Consulting in past year: . WHISCON LLC, Booz&Co, Aetion Investigator-initiated research grants to the Brigham from Pfizer, Novartis, Boehringer-Ingelheim Multiple grants from NIH 2 Objective of Comparative Effectiveness Research Efficacy Effectiveness* (Can it work?) (Does it work in routine care?) Placebo Most RCTs comparison for drug (or usual care) approval Active Goal of comparison (head-to-head) CER Effectiveness = Efficacy X Adherence X Subgroup effects (+/-) RCT Reality of routine care 3 * Cochrane A. Nuffield Provincial Trust, 1972 CER Baseline Non- randomization randomized Primary Secondary Primary Secondary data data data data 4 Challenges of observational research Measurement /surveillance-related biases . Informative missingness/ misclassification Selection-related biases . Confounding . Informative treatment changes/discontinuations Time-related biases . Immortal time bias . Temporality . Effect window (Multiple comparisons) 5 Informative missingness: Unintended effects of statins Data source: QResearch EMR H-C C and CC, BMJ 2010 system, England & Wales 6 Confounding (Obs) and informative censoring (Obs and RCT) By indication By contraindicaton, healthy users Adherence Severity, Side effect, comorbidity, treatment failure prognosis Patient follow-up Treat A Rx Rx Rx Rx Patients Treat B Rx Rx 7 Immortal time bias in an event- based cohort Cohort entry is defined by a new diagnosis “>” but time until 1st drug exposure is misclassified Cohort entry is defined by - 1st drug use for exposed - an event (Dx) for the unexposed Suissa S, AJE 2008 8 Opportunities: Confounding Utilize naturally occurring variation in the healthcare system (Dylan Small) . Between providers . Between systems . Between regions . Between time periods Measure naturally occurring variation and capture via propensity score analyses Negative controls (Prasad & Jena) 9 Opportunities: Censoring As treated vs. intention to treat analyses (Miguel Hernan) Inverse probability of discontinuing weighting . And similar methods that rely on characterizing the factors leading to treatment change 10 Opportunities: Transparency about choices No single study(design) will satisfy the information needs of a decision maker Need to understand the desired study characteristics and transparent choose accordingly: . Internal validity . External validity . Precision . Timeliness . Logistical constraints . etc 11 Intrinsic Study Characteristics Internal validity (bias) External validity (generalizability, transportability) Precision Heterogeneity in risk or benefit (personalized evidence) Ethical consideration (equipoise) External Study Characteristics Timeliness (rapidly changing technology, policy needs) Logistical constraints (study size, complexity, cost) Data availability, quality, completeness 4 Design Below are general considerations that may be neither comprehensive nor applicable for every scenario. These considerations are meant to be amended and changed as more experience is gained with this tool. Is baseline randomization indicated? YES-Prefer baseline randomization for: • high validity in the presence of strong baseline confounding • if no ethical issues prevent randomization • if sufficient resources available • if enough time available to await results NO-Prefer observational study for: • high representativeness for “routine care” by not perturbing the care system • Need good reason to believe that confounding can be controlled through adjustment Helpful references include: Rothwell PM Lancet 2005 Miler FG & Joffe S NEJM 2001 Concato J PDS 2012 13 Intrinsic Study Characteristics Internal validity (bias) External validity (generalizability, transportability) Precision Heterogeneity in risk or benefit (personalized evidence) Ethical consideration (equipoise) External Study Characteristics Timeliness (rapidly changing technology, policy needs) Logistical constraints (study size, complexity, cost) Data availability, quality, completeness 14 Opportunities: Study portfolio Often we need multiple studies with different data sources (prim/sec) and different designs Is there an optimal way to arrange multiple studies so that they . complement each other (speed, validity, generalizability) . and collectively provide most valid and comprehensive information for decision makers? 15 Opportunities: Investigator error Training Guidance ‘Standards’ 16 Yes Basic Design Consideration Meaningful exposure variation within patients? Consider case-crossover design no Cohort study (case-control, case-cohort sampling) Exposure/outcome considerations Exposure definition Outcome Definition Comparison group considerations Clinical meaningfulness Incident user design considerations Specificity and sensitivity of measurement Exposure risk window considerations Case validation necessary? Yes Subgroup Analysis ? Subgroup definition Prior pharmacology knowledge Prior clinical Knowledge 17 Balancing Patient Characteristics Defining covariates based on clinical knowledge Defining additional covariates empirically (high-dimensional proxy adjustment) Demonstrate covariate distributions by treatment group with RDs and 95% CIs Collect additional information in subpop. Supplemental covariate information required Yes • 2-stage sampling that is not available in primary data source? • External data source -(PS Calibration) - Multiple imputation Yes Propensity score (PS) analysis Missing covariate values in EMRs? Multiple imputation Estimating propensity score Graphically explore PS distribution by treatment group Explore effect measure modification by PS: tabulate RR, RD for each PS stratum Yes Trim 5% of patients on each end of Effect measure modification by PS? PS distribution or match by PS •Stratify by PS deciles •Match on PS (1:1, 1:n, 1:n:m) Demonstrate covariate balance by treatment group with RDs and 95% CIs Calculate risk difference (RD) and Statistical analysis* risk ratio (RR); 95% confidence intervals (CIs) for main result. Report person-time (p-t), number of events Subgroup analysis Include time since initiation as subgroup Calculate RR, RD for each subgroup Dose-response analysis Repeat analyses after changes in: Sensitivity Analyses • Definition of “incident users” • Definition of exposure risk window • Outcome definition if appropriate Explore changes in effect estimates after making structural assumptions about unmeasured confounders Report *For illustration purposes only an analysis after PS matching is shown. 19 Opportunities: Transparency Sharing of data vs. sharing of analytics environment . Data stay were they are but analytics infrastructure allows other investigators (in collaboration) to re-analyze data 20 .
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