Putting it all together CIMPOD 2017, February 27-28

Putting it all together CIMPOD 2017, February 27-28
1/26/2015 Putting it all together CIMPOD 2017, February 27-28 Miguel Hernán departments of epidemiology and biostatistics

Putting it all together? In less than 30 minutes? Really?
Estimating treatment effects (Drukker) Observational data (Rosen-Zvi) Propensity scores (Seeger) IV estimation (Swanson) IP weighting (Patel, Cain) G-formula (Young) Doubly-robust estimators (Rosenblum) Mediation analysis (Daniel) Machine learning (Rose) Hernán - Putting it all together

What is comparative effectiveness research?
“The generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care” Institute of Medicine 2009 Comparative effectiveness or comparative safety Patient-centered outcomes research Hernán - Putting it all together

Hernán - Putting it all together
The goal of comparative effectiveness research is to help decision-makers decide Patients, clinicians, policy makers, health care managers “To assist [decision makers] to make informed decisions that will improve health care at both the individual and population levels” Institute of Medicine 2009 Hernán - Putting it all together

Comparative effectiveness research and its purpose
“The generation and synthesis of evidence that compares the benefits and harms…” “to make informed decisions that will improve health…” So CER is about estimating the effects of treatments where “treatment” means any health-related intervention Hernán - Putting it all together

CIMPOD 2017 Putting it all together

The situation: We need to make decisions NOW
Treat with A or with B? Treat now or later? When to switch to C? A relevant randomized trial would, in principle, answer each comparative effectiveness and safety question Interference/scaling up issues aside Hernán - Putting it all together

But we rarely have randomized trials
expensive, untimely, unethical, impractical And deferring decisions is not an option no decision is a decision: “Keep status quo” Question: What do we do? Hernán - Putting it all together

Answer: We analyze observational data (pre-existing or collected for research) Epidemiologic studies Electronic medical records Administrative claims databases National registers Disease registries Other Hernán - Putting it all together

We analyze observational data
but only because we cannot conduct a randomized trial Observational studies are not our preferred choice For each observational study with a causal goal, we can imagine a hypothetical randomized trial that we would prefer to conduct If only it were possible Hernán - Putting it all together

The Target Trial An analysis of observational data can be viewed as an attempt to emulate a (hypothetical) pragmatic randomized trial As suggested more or less explicitly by many authors, including Cochran, Rubin, Feinstein, Dawid, Robins… Hernán, Robins. Am J Epidemiol 2016 Hernán - Putting it all together

Procedure to answer comparative effectiveness research questions
Step #1 Describe the protocol of the target trial Step #2 Option A Conduct the target trial Option B Use observational data to explicitly emulate the target trial Hernán - Putting it all together

Observational study needs to emulate Key elements of target trial protocol Eligibility criteria Strategies Randomized assignment Start/End follow-up Outcomes Causal contrast(s) of interest Analysis plan Eligibility criteria Strategies Randomized assignment Start/End follow-up Outcomes Causal contrast(s) of interest Analysis plan Hernán - Putting it all together

CIMPOD 2017: Examples of Target Trials emulated using observational data Seeger Statin initiation and acute myocardial infarction Patel Antiretroviral therapy and survival of HIV-positive children In utero exposure to atazanavir and neurodevelopment Swanson Sigmoidoscopy screening and colorectal cancer Antidepressants during pregnancy and depression Young Fish consumption and coronary heart disease Strategies for initiation of antiretroviral therapy and mortality Cain Strategies to monitor HIV-positive patients and mortality Hernán - Putting it all together

Procedure to answer CER questions
Step #1 Describe the protocol of the target trial Step #2 Option A Conduct the target trial Option B Use observational data to explicitly emulate the target trial Apply appropriate causal inference analytics to estimate the effects of interest Hernán - Putting it all together

Observational analyses are, by definition, based on nonrandomized treatments Individuals receiving each treatment may not be comparable on average Confounding Emulation of randomization requires measuring and adjusting for confounding factors If insufficient information on baseline confounders then successful emulation of the target trial’s random assignment is not possible Confounding bias Hernán - Putting it all together

Emulation of randomization requires adjustment for confounding factors
Adjustment methods include matching, stratification/regression with or without propensity scores standardization/g-formula IP weighting … Confounding adjustment methods are used to emulate randomization Sometimes referred to as “causal inference methods” Hernán - Putting it all together

Emulation of randomization requires measuring and adjusting for confounding factors If insufficient information on baseline confounders then successful emulation of the target trial’s random assignment is not possible Confounding bias Not quite true Hernán - Putting it all together

Two approaches to emulate randomization (Two approaches to adjust for confounding) Correctly measure and appropriately adjust for all confounders Stratification/regression, matching with or without propensity scores G-methods: standardization/g-formula, g-estimation, IP weighting Exploit sources of randomness in the data to adjust for confounding without measuring the confounders instrumental variable estimation, regression discontinuity, etc. Hernán - Putting it all together

Instrumental variable methods
Do not require that all confounders are measured and adjusted for In fact, one does not need to even know which the confounders are But require other (often very strong) conditions for the validity of the estimates Hernán - Putting it all together

Observational study needs to emulate Key elements of target trial protocol Eligibility criteria Strategies assigned at start of follow-up Randomized assignment Start/End follow-up Outcomes Causal contrast(s) of interest Analysis plan Eligibility criteria Strategies followed from start of follow-up Randomized assignment Start/End follow-up Outcomes Causal contrast(s) of interest Analysis plan Hernán - Putting it all together

Classification of treatment strategies according to their time course
Point interventions Intervention occurs at a single time Examples: one-dose vaccination, short-lived traumatic event, surgery… Intention-to-treat effects in RCTs are about point interventions Sustained strategies Interventions occur at several times Examples: medical treatments, lifestyle, environmental exposures… Many (most?) questions are about sustained exposures Hernán - Putting it all together

Classification of sustained treatment strategies
Static a fixed strategy for everyone Example: treat with 150mg of daily aspirin during 5 years Dynamic a strategy that assigns different values to different individuals as a function of their evolving characteristics Example: start aspirin treatment if coronary heart disease, stop if stroke Hernán - Putting it all together

Choice of confounding adjustment method depends on type of strategies
Comparison of strategies involving point interventions only All methods work if all confounders are measured or the instrumental variable conditions hold Comparison of sustained strategies Generally only g-methods work Developed by Robins and collaborators since 1986 Hernán - Putting it all together

Comparative effects of point interventions
Time-fixed treatment implies time-fixed (i.e., baseline) confounding Any adjustment method will correctly adjust for measured baseline confounders e.g., outcome regression such as logistic or Cox regression Hernán - Putting it all together

Comparative effect of sustained strategies
Time-varying treatments imply time-varying confounders possible treatment-confounder feedback Conventional methods may introduce bias even when sufficient data are available on Time-varying treatments and time-varying confounders G-methods can appropriately handle treatment-confounder feedback Sometimes referred to as “causal” methods Hernán - Putting it all together

Treatment-confounder feedback
At: Antiretroviral therapy Y: Outcome Lt: CD4 cell count U: Immunologic status A0 L1 Y U A1 There is treatment-confounder feedback if the time-varying confounders are affected by previous treatment Confounder on the causal pathway NOT necessary for bias Hernán - Putting it all together

G-methods Parametric g-formula Robins 1986 G-estimation of nested structural models Robins 1989, 1991 IP weighting of marginal structural models Robins 1998 Doubly-robust versions Bang, Robins, Vanderlaan, Rotnitzky… e.g., collaborative targeted maximum likelihood estimation Hernán - Putting it all together

CIMPOD 2017: Examples of sustained treatment strategies
Patel Antiretroviral therapy and survival of HIV-positive children In utero exposure to atazanavir and neurodevelopment Young Fish consumption and coronary heart disease Strategies for initiation of antiretroviral therapy and mortality Cain Strategies to monitor HIV-positive patients and mortality Hernán - Putting it all together

Mediation analysis A particular application of causal inference
Goal is estimating what component of the effect of treatment is, or is not, mediated by another factor Direct effect, indirect effect The target trial has two treatments The treatment of interest The mediator Need to emulate randomization for both the treatment and the mediator Stronger assumptions than non-mediational analyses Hernán - Putting it all together

Machine learning is a method for prediction
Not for causal inference Lots of active research on incorporating machine learning into causal inference Hernán - Putting it all together

Remember Observational analyses are what we do when we cannot conduct a randomized trial In the absence of practical and ethical constraints, sane people will always prefer a randomized trial No alternative to observational studies So we better keep improving them because people will keep using (Big and Small) observational data to guide their decisions Hernán - Putting it all together

Putting it all together CIMPOD 2017, February 27-28

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Putting it all together CIMPOD 2017, February 27-28

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