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Propensity score calibration in the absence of surrogacy.

ABSTRACT: Propensity score calibration (PSC) can be used to adjust for unmeasured confounders using a cross-sectional validation study that lacks information on the disease outcome (Y), under a strong surrogacy assumption. Using directed acyclic graphs and path analysis, the authors developed a formula to predict the presence and magnitude of the bias of PSC in the simplest setting of a binary exposure (T) and 1 confounder (X) that are observed in the main study and 1 confounder (C) that is observed in the validation study only. PSC bias is predicted on the basis of parameters that can be estimated from the data and a single unidentifiable parameter, the relative risk (RR) associated with C (RR(CY)). The authors simulated 1,000 cohort studies each with a Poisson-distributed outcome Y, varying parameter values over a wide range. When using the true parameter for RR(CY), the formula predicts PSC bias almost perfectly in this simple setting (correlation with observed bias over 24 scenarios assessed: r = 0.998). The authors conclude that the bias from PSC observed in certain scenarios can be estimated from the imbalance in C between treated and untreated persons, after adjustment for X, in the validation study and assuming a range of plausible values for the unidentifiable RR(CY).

SUBMITTER: Lunt M

PROVIDER: S-EPMC3491974 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Propensity score calibration in the absence of surrogacy.

Lunt Mark M Glynn Robert J RJ Rothman Kenneth J KJ Rothman Kenneth J KJ Avorn Jerry J Stürmer Til T

American journal of epidemiology 20120424 12

Propensity score calibration (PSC) can be used to adjust for unmeasured confounders using a cross-sectional validation study that lacks information on the disease outcome (Y), under a strong surrogacy assumption. Using directed acyclic graphs and path analysis, the authors developed a formula to predict the presence and magnitude of the bias of PSC in the simplest setting of a binary exposure (T) and 1 confounder (X) that are observed in the main study and 1 confounder (C) that is observed in th ...[more]

PMID: 22688682

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Project description:BackgroundThe diagnostic process is a key element of medicine but it is complex and prone to errors. Infectious diseases are one of the three categories of diseases in which diagnostic errors can be most harmful to patients. In this study we aimed to estimate the effect of initial misdiagnosis of the source of infection in patients with bacteraemia on 14 day mortality using propensity score methods to adjust for confounding.MethodsData from a previously described longitudinal cohort of patients diagnosed with monobacterial bloodstream infection (BSI) at the Leiden University Medical Centre (LUMC) between 2013 and 2015 were used. Propensity score matching and inversed probability of treatment weighting (IPTW) were applied to correct for confounding. The average treatment effect on the treated (ATT), which in this study was the average effect of initial misdiagnosis on the misdiagnosed (AEMM), was estimated. Methodological issues that were encountered when applying propensity score methods were addressed by performing additional sensitivity analyses. Sensitivity analyses consisted of varying caliper in propensity score matching and using different truncated weights in inversed probability of treatment weighting.ResultsData of 887 patients were included in the study. Propensity scores ranged between 0.015 and 0.999 and 80 patients (9.9%) had a propensity score > 0.95. In the matched analyses, 35 of the 171 misdiagnosed patients died within 14 days (20.5%), versus 10 of the 171 correctly diagnosed patients (5.8%), yielding a difference of 14.6% (7.6%; 21.6%). In the total group of patients, the observed percentage of patients with an incorrect initial diagnosis that died within 14 days was 19.8% while propensity score reweighting estimated that their probability of dying would have been 6.5%, if they had been correctly diagnosed (difference 13.3% (95% CI 6.9%;19.6%)). After adjustment for all variables that showed disbalance in the propensity score a difference of 13.7% (7.4%; 19.9%) was estimated. Sensitivity analyses yielded similar results. However, performing weighted analyses without truncation yielded unstable results.ConclusionThus, we observed a substantial increase of 14 day mortality in initially misdiagnosed patients. Furthermore, several patients received propensity scores extremely close to one and were almost sure to be initially misdiagnosed.

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Propensity score calibration in the absence of surrogacy.

Publications

Propensity score calibration in the absence of surrogacy.

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