Where is Epidemiology going?
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Where is Epidemiology going?
Jan P VandenbrouckeBern, STROBE meeting August 2010
Part I version 22 Aug
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Four topics
• The ‘surge’ of Comparative Effectiveness Research
• New statistical techniques (or old ones that are suddenly popular)
• New methodologic insights (confounding, selection bias, interaction, mediation..)
• The call for registration of observational research
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Surge of Comparative Effectiveness research (1)
• The impact of Obama on epidemiologic theory: – 1 billion $ for CER– New or reinvigorated agencies that want to know which
health care actions are worthwhile and which are not (Emmanuel, NEJM 2010)
• A lot of persons storm into non-randomized effectiveness comparisons; a few pause, think and realize: “attempting the impossible”. Still, they are enthusiastic about the challenge and seek ways out.
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Surge (2) Classic papers about ‘the impossible”
– Miettinen, intended and unintended effects (1980):Therapeutic effects = RCTsAdverse effects = possible with data usual practice – Rubin (1978, Ann Statistics) in health care the
assignment variables too many & subtle, unclear in their definition & relationship with other variables poorly understood: Bayesian analysis to enter assignment in models becomes too sensitive to prespecifications – randomization solves the problem.
• Rubin DB Bayesian Inference for Causal Effects: The Role of Randomization. The Annals of Statistics, Vol. 6, No. 1 (Jan., 1978)
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Surge of Comparative Effectiveness research (3)
• All admit that RCT ideal, but will never deliver the goods– Never sufficient head-to-head comparisons– Never sufficient long-time– Never sufficient real life
• Preconference courses at 2010 Int Soc Pharmacoepidemioly and at 2010 American College of Epidemiology meetings, by group of mostly Harvard-based epidemiologists
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Surge of Comparative Effectiveness research (4)
• Solutions are sought (Stürmer 2009):– Severe restriction for indication & contraindication– New user cohorts e.g. at least 1 to 3 yrs medication free– Comparisons with active drugs for similar indication– Propensity Score and/or strong Instrumental Variables
• Agency for Health Care Research and Quality (AHRQ) & Int Soc for Pharmacoeconomics and Outcome Research (ISPOR) published series of papers as guides to CER (J Clin Epidemiol 2010, Value in Health 2009)
• Current consensus: it may be possible, but at the expense of generalizability
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New Techniques or older ones that suddenly become popular
• Propensity score• Confounding score• Instrumental variable analysis
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Propensity Score (1)
• Rosenbaum & Rubin 1983• Strong recent increase in popularity• Idea: model ‘propensity’ to be exposed; for
two persons with similar propensity, the choice (assignment) is ‘ignorable’ – under the assumption of perfect knowledge (like with usual thinking about confounding)
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(C1A) Stürmer, preconference courseInt Soc Pharmaco Epi, Aug 201
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Propensity Score (2)
• Construction of propensity score:– Regression of determinants of exposure– Every person gets score– Overlapping area between scores of exposed
and unexposed is determined– Either used for matching on score, or in
multivariate analysis
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PropScore
(3)
Schneeweiss, 2009
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Prop Score (4) Appendix, Hackam Lancet 2006
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PS (5) Long term debate • Is it better than adjustment for confounding?• Logically, only variables that determine outcome
can make a difference (proven in simulations and real life examples; Brookhart AJE 2009)
• Variables that are only related to exposure increase standard error and may even introduce confounding – ideally use include variables that are somehow related to outcome, do not use variables that only predict exposure (Brookhart AJE 2006)
• Good tool if outcome rare relative to number of variables to stratify for
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PS (6) New arguments “pro”
• In large database settings with hundreds of variables; outcome always relatively rare relative to number of variables
• Hundreds of variables may capture the complexity of prescribing even if underlying reasons for prescription cannot be identified…. Answer to Rubin?
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(C6A) Schneeweiss, preconference course
Int Soc Pharmaco Epi, Aug 201
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PS (7) What should be reported?(My digest)
• The choice of variables: care taken to use only variables related to outcome?
• The way of making the score (model) • The discrimination achieved by the score: mind!! if too
much discrimination: shows that there is too much confounding by indication – PS analysis can’t be done (look for another comparator, etc)
• The trimming of the data (restriction of score)• The use of either matching or multivariate analysis • Additional analyses: enter also major confounders like
age, sex and institution, next to prop score or in matched model: “Dual robustness”
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Confounder score• Cornfield JAMA 1971• Miettinen 1976 (Disease and exposure risk
scores)
• Equivalent of prop score, but this time a score made with confounders [Fine point: if PS only of variables also related to outcome – identical?]
• Mentioned for completeness – sometimes both used in a sensitivity analysis
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Instrumental variable analysis (1)
• Idea: a variable that – Determines exposure – Unrelated to patient characteristics– Unrelated to (perceived) risk of outcome
• E.g. postal code in cardiovascular resuscitation
• Long history in econometrics!
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Instrumental variable analysis (2)
• To understand: in essence any randomization is an instrumental variable:– ‘Flip of coin’ satisfies all three conditions– Mind!: ‘flip of coin’ gives no guarantee that patient
receives treatment!• Analysis, e.g. postal code:
– As such: one area vs. other = intention to treat– Or IV analysis: ‘rule of three’: example: in one postal
code area 30% new intervention, in other 70% new intervention; what would happen if all received new treatment (= regression analysis of percentage outcome with difference percentage treatment)
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IV (3) New use in pharmacoepi• The previous prescription: patient ‘John Smith’ in study;
received Vioxx for joint pain; outcome of interest is association with MI (unintended) or GI bleeding (intended);
• What was previous NSAIDs prescription in same practice? John Smith enrolled in study with all his data, but with exposure (prescription) of previous patient. Same happens with all patients: some ‘switch’ NSAIDs, some do not
• Rationale: previous prescription is not guided by perceived risk of John Smith, but gives info about prescription preference of physician (Brookhart, Epidemiology 2006)
• Counterargument: becomes a comparison by health care practice. If different types of patients, or different other treatments, then confounded (Hernan, Robins 2006)
• Applicable in large data-bases• [Fine point: analogous to argument why no confounding by
indication if risk of adverse effect is unknown]
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IV (4) What should be reported From paper Brookhart et al 2010
• Justify need for and role of IV in study• Describe theoretical basis for choice of IV• Report strength of instrument and results from first
stage model (=intention to treat)• Distribution of patient risk factors across levels of IV
and exposure (answer to Hernan and Robins)• Explore concomitant treatments (answer to Hernan
& Robins)• Evaluate sensitivity of IV to modeling assumptions• Discuss issues related to interpretation of estimator
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What is best: IV or PS?(My digest)
• Different experiences: papers in literature: Stukel JAMA 2007 finds IV superior, vs. Bosco, Lash J Clin Epidemiology 2010 “A most stubborn bias”
• IV strongly related to exposure intuitively seems best; weak IV may leave confounding and imprecision. However, strong IV rare and if assumptions violated (e.g., when strong confounding by indication) may also leave confounding (Martens, Epidemiology 2006)
• Combine? All three (classic confounding, PS, & IV) presented in one paper as a mutual sensitivity analysis: Schneeweiss NEJM 2008