Survival-time inverse-probability- weighted regression adjustment

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Presentation transcript:

Survival-time inverse-probability- weighted regression adjustment 2017/09/26 Wen-Ching Lan

IPWRA estimators model both the outcome and the treatment to account for the nonrandom treatment assignment. IPWRA uses IPW weights to estimate corrected regression coefficients that are subsequently used to perform regression adjustment.

Survival-time treatment-effects estimators handle two types of missing data. First, only one of the potential outcomes is observed, as is standard in causal inference. Second, the potential outcome for the received treatment may be censored.

Weighted adjusted censoring assumptions The censoring time must be random. The censoring time must be from a known distribution. The distribution of the censoring time cannot vary by treatment level.

Weighted regression adjusted estimators Each observation-level weight is the inverse of the probability of not being censored. The WRA estimators use averages of the predicted mean survival times to estimate treatment-effect parameters.

Inverse-probability-weighted estimators IPW estimators are weighted averages of the observed outcome. The weights correct for missing data due to unobserved potential outcomes and censoring. Each weight is the inverse of the probability that a given value is observed. Observed values that were not likely to be observed have higher weights.

Inverse-probability-weighted regression adjustment estimators IPWRA estimators are averages of treatment-specific predicted conditional means that were made using missingness-adjusted regression parameters. The censored observations can be handled either by weighting under the WAC assumptions to obtain the WAC-IPWRA estimator or by adding a term to the log-likelihood function to obtain the LAC-IPWRA estimator.

There are two methods of accounting for the data lost to censoring: likelihood-adjusted-censoring IPWRA (LAC-IPWRA) Weighted-adjusted-censoring IPWRA (WAC-IPWRA)

Likelihood-adjusted-censoring IPWRA When a censoring model is not specified, stteffects ipwra uses the formulas for the LAC-IPWRA given in Likelihood-adjusted-censoring IPWRA, below. The LAC-IPWRA estimator handles the case in which no observations are censored and requires the weaker independent censoring assumptions, which allows for fixed censoring times.

Weighted-adjusted-censoring IPWRA When a censoring model is specified, stteffects ipwra uses the formulas for the WAC-IPWRA estimator given in Weighted-adjusted-censoring IPWRA. The WAC-IPWRA estimator requires that some observations be censored and that the WAC assumptions hold.

LAC-IPWRA v.s WAC-IPWRA Estimate the parameters of a treatment-assignment model and compute inverse-probability-of treatment weights. Obtain the treatment-specific predicted mean outcomes for each subject by using the weighted maximum likelihood estimators. Compute the means of the treatment-specific predicted mean outcomes. Estimate the parameters of a treatment-assignment model and compute inverse-probability-of treatment weights. Estimate the parameters of a time-to-censoring model and compute inverse-probability-of censoring weights. Obtain the treatment-specific predicted mean outcomes for each subject by using the weighted maximum likelihood estimators. Compute the means of the treatment-specific predicted mean outcomes.

Example 1: Estimating the ATE by LAC-IPWRA → stteffects ipwra (age exercise diet education [weibull]) (smoke age exercise diet education [logit])

Example 2: Different outcome and treatment models

Example 3: Estimating the ATE by WAC-IPWRA

Example 4: Estimating the ATET by LAC-IPWRA

Postestimation tools for stteffects

Nonsmoker Smoker

Reference Cattaneo, M. D., D. M. Drukker, and A. D. Holland. 2013. Estimation of multivalued treatment effects under conditional independence. Stata Journal 13: 407–450.

Thank for your attention.