Targeted Maximum Likelihood Estimation in Safety Analysis Sam Lendle 1 Bruce Fireman 2 Mark van der Laan 1 1UC Berkeley 2Kaiser Permanente ISPE Advanced Topics Session, Barcelona, August 2012 1 / 35 Outline 1 Introduction 2 Super learning 3 TMLE and collaborative TMLE 4 Kaiser Permanente data example 5 Simulations based on KP data 2 / 35 Outline 1 Introduction 2 Super learning 3 TMLE and collaborative TMLE 4 Kaiser Permanente data example 5 Simulations based on KP data 3 / 35 Traditional approach in epidemiology and clinical medicine • Fit several parametric logistic regression models, and select a favorite one. • Report point estimate of coefficient in front of treatment, confidence intervals, and p-value, as if this parametric model was a priori-specified. • Problems • Parametric model is misspecified, but parameter estimates are interpreted as if the model is correct • Estimates of variance do not account for model selection, so confidence intervals and p-values are wrong, even if the final model is somehow correct! 4 / 35 The statistical estimation problem • Observed data: Realizations of random variables with a probability distribution. • Statistical model: Set of possible distributions for the data-generating distribution, defined by actual knowledge about the data. e.g. in an RCT, we know the probability of each subject receiving treatment. • Statistical target parameter: Function of the data-generating distribution that we wish to learn from the data. • Estimator: An a priori-specified algorithm that takes the observed data and returns an estimate of the target parameter. Benchmarked by a dissimilarity-measure (e.g., MSE) w.r.t target parameter. 5 / 35 Causal inference • Non-testable assumptions in addition to the assumptions defining the statistical model. (e.g. the “no unmeasured confounders” assumption). • Allows for causal interpretation of statistical parameter estimates • Even if we don’t believe the non-testable causal assumptions, the statistical estimation problem is still the same, and estimates still have valid statistical interpretations. 6 / 35 Targeted learning • Define true statistical models, and interesting target parameters • Avoid reliance on human art and nonrealistic parametric models • Target the fit of the data-generating distribution to the parameter of interest • Statistical inference • Has been applied to: static or dynamic treatments, direct and indirect effects, parameters of MSMs, variable importance analysis, longitudinal/repeated measures data with time-dependent confounding, censoring/missingness, case-control studies, RCTs 7 / 35 Two stage estimation methodology • Super learning (SL) (van der Laan et al. 2007) • Uses a library of candidate estimators (e.g. multiple parametric models, machine learning algorithms like neural networks, RandomForest, etc.) • Builds data-adaptive weighted combination of estimators using cross validation • Targeted maximum likelihood estimation (TMLE) (van der Laan and Rubin 2006) • Updates initial estimate, often a Super Learner, to remove bias for the parameter of interest • Calculates final parameter from updated fit of the data-generating distribution 8 / 35 Outline 1 Introduction 2 Super learning 3 TMLE and collaborative TMLE 4 Kaiser Permanente data example 5 Simulations based on KP data 9 / 35 Super learning • No need to chose a priori a particular parametric model or machine learning algorithm for a particular problem • Allows one to combine many data-adaptive estimators into one improved estimator. • Grounded by oracle results for loss-function based cross-validation (Van Der Laan and Dudoit 2003). Loss function needs to be bounded. • Performs asymptotically as well as best (oracle) weighted combination, or achieves parametric rate of convergence. 10 / 35 Super learning Figure: Relative Cross-Validated Mean Squared Error (compared to main terms least squares regression) 11 / 35 Super learning 12 / 35 Outline 1 Introduction 2 Super learning 3 TMLE and collaborative TMLE 4 Kaiser Permanente data example 5 Simulations based on KP data 13 / 35 TMLE algorithm 14 / 35 Targeted MLE 1 Identify the least favorable parametric model for fluctuating initial Pˆ – Small “fluctuation” → maximum change in target. 2 Identify optimum amount of fluctuation by MLE. 3 Apply optimal fluctuation to Pˆ → 1st-step targeted maximum likelihood estimator. 4 Repeat until the incremental “fluctuation" is zero – Some important cases: 1 step to convergence. 5 Final probability distribution solves efficient score equation for target parameter → T-MLE is a double robust & locally efficient plug-in estimator 15 / 35 Collaborative TMLE (CTMLE) algorithm • Like TMLE, but chooses an estimate gˆ of the treatment mechanism/propensity score based on how well it helps estimate Ψ(Q0) instead of how well it estimates the true g0. • Build estimate for g0 in a stepwise fashion • Strongest confounders are adjusted for first • Instrumental variables and weak confounders tend to be excluded • Order of terms added to gˆ is chosen via a penalized log likelihood, and number of terms is chosen via cross-validation 16 / 35 Kang and Schafer (2007) simulations • Outcome Y continuous subject to missingness, and 4 covariates, W1, W2, W3, W4 • True population mean (target parameter) is 210, mean among the non-missing is 200. • Positivity violations g0(∆ = 1 | W ) as small as 0.01 • Modification 1: stronger positivity violations, g0(∆ = 1 | W ) as small as 1.1 × 10−5 • Modification 2: same as 1, but one covariate is no longer affects Y , so it is an instrumental variable. 17 / 35 Kang and Schafer (2007) simulations Kang and Schafer Simulation 10 5 0 −5 −10 OLS WLS TMLE A−IPCW C−TMLE 18 / 35 Kang and Schafer (2007) simulations Modification 1 to Kang and Schafer Simulation 40 20 ● ● 0 ● ● −20 ● ● ● −40 ● ● OLS WLS TMLE A−IPCW C−TMLE 19 / 35 Kang and Schafer (2007) simulations Modification 2 to Kang and Schafer Simulation 40 20 ● 0 ● −20 ● ● ● ● ● −40 ● ● OLS WLS TMLE A−IPCW C−TMLE 20 / 35 Outline 1 Introduction 2 Super learning 3 TMLE and collaborative TMLE 4 Kaiser Permanente data example 5 Simulations based on KP data 21 / 35 Description of dataset • A subset of data from Kaiser Permanente, part of which is used in FDA’s Mini-Sentinel drug safety surveillance. • Population: diabetic patients without prior cardiovascular disease who are new users of pioglitazone or a sulfonylurea (two anti-diabetic drugs) and who are followed up for at least 6 months without also starting the other drug.1 • Treatment arm (in this example): pioglitazone (Treatment variable A = 1) • Comparator: sulfonylurea (A = 0) • Outcome (Y ): acute myocardial infarction (AMI) in first 6 months of new anti-diabetic drug use. • Baseline covariates (W ): fifty covariates including demographics, comorbidities, and other drug use. 1We found that adjusting for missing outcomes had no effect on the results in this case so we suppress those results and ignore missingness in this example. 22 / 35 Causal model, counterfactual outcomes, and parameter of interest • Non-parametric structural equation model: Each variable is an unknown deterministic function of the past and an error. • W = fW (UW ) • A = fA(W , UA) • Y = fY (A, W , UY ) • Counterfactual outcomes: substitute a fixed treatment for A in fY : Ya = fY (W , a, UY ) for a ∈ {0, 1}. • Causal parameter of interest: The average treatment effect (ATE). E(Y1 − Y0) • Statistical parameter of interest: Ψ(P0) = E[E(Y | A = 1, W ) − E(Y | A = 0, W )] equals E(Y1 − Y0) under randomization assumption (“no unmeasured confounders”) and positivity assumption 23 / 35 Analysis results • Summary of outcome by treatment Treatment Comparator Total Total 2146 25022 27168 AMI 5 (0.233%) 86 (0.3437%) 91 (0.335%) • Estimates Estimate p-value Unadjusted −0.0011 0.3943 G-comp −0.0007 0.6134 PS matching −0.0013 0.4512 IPTW −0.0005 0.7476 AIPTW −0.0003 0.8585 TMLE −0.0004 0.8042 • Though sample size is large, there are so few AMIs in this subset of data from Kaiser Permanente that it is hard to tell if adjustment for potential confounders is important. 24 / 35 Outline 1 Introduction 2 Super learning 3 TMLE and collaborative TMLE 4 Kaiser Permanente data example 5 Simulations based on KP data 25 / 35 Strategy Simulate datasets based on real study data where the true effect is known to highlight properties of estimators. • Start with KP data set, including additional new users of three other anti-diabetic drugs. • Sample W with replacement from empirical distribution of baseline covariates • Simulate treatment A assignments based on a known function of baseline covariates • Simulate outcome Y based on a function of W adjusted so that Y is not too rare. • Because the Y is simulated based on a function of only baseline covariates and not the treatment, the true average treatment effect is known to be zero. 26 / 35 Simulation 1 • Treatment mechanism a function of 12 covariates strongly predictive of the outcome. • Outcome and propensity score models known and can be correctly specified. • Outcome and propensity score models are misspecified by leaving out half of the important confounders. • Results demonstrate the double-robustness of TMLE and AIPTW: when either the model for the outcome regression or the PS is specified correctly, the parameter estimate is consistent, which is not the case for the G-computation estimator or IPTW. 27 / 35 Simulation 1 Estimator Bias MSE n=1000 n=5000 n=1000 n=5000 Unadjusted 0.0584 0.0575 0.0038 0.0034 G-comp 0.0015 0.0000 0.0003 0.0001 PSM 0.0012 0.0003 0.0006 0.0001 IPTW 0.0017 0.0002 0.0005 0.0001 AIPTW 0.0013 0.0002 0.0004 0.0001 TMLE 0.0014 0.0002 0.0004 0.0001 G-comp, misspecified 0.0183 0.0168 0.0007 0.0004 PSM, misspecified 0.0179 0.0167 0.0008 0.0004 IPTW, misspecified 0.0180 0.0166 0.0007 0.0004 AIPTW, Outcome misspecified 0.0016 0.0002 0.0004 0.0001 AIPTW, PS misspecified 0.0014 0.0001 0.0004 0.0001 TMLE, Outcome misspecified 0.0015 0.0002 0.0004 0.0001 TMLE, PS misspecified 0.0015 0.0001 0.0004 0.0001 28 / 35 Simulation 2 • Treatment mechanism now depends on a covariate that is very predictive of treatment, resulting in positivity violations, but is not a confounder.