Transcript Where is Epidemiology going?

Where is Epidemiology going?
Jan P Vandenbroucke
Bern, STROBE meeting August 2010
Part I version 22 Aug
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
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.
Surge (2) Classic papers about ‘the
impossible”
– Miettinen, intended and unintended effects (1980):
Therapeutic effects = RCTs
Adverse 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)
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
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
New Techniques or older ones that
suddenly become popular
• Propensity score
• Confounding score
• Instrumental variable analysis
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)
(C1A) Stürmer, preconference course
Int Soc Pharmaco Epi, Aug 201
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
Prop
Score
(3)
Schneeweiss,
2009
Prop Score (4) Appendix, Hackam
Lancet 2006
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
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?
(C6A) Schneeweiss, preconference course
Clopidogrel - MI (2)
Coxib - GI bleed
Statin - death
TCA suicide (1)
0.60
0.30
0.00
-0.30
only hd-PS
+ hd-PS
adjust.
+ specified
covars
age-sexAge-sexrace-year
year-race
adjustment
adjusted
-0.60
Unadjusted
Unadjusted
log
risk).
(relativerisk)
log(relative
Int Soc Pharmaco Epi, Aug 201
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”
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
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!
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)
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]
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
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