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Peter C Austin - One of the best experts on this subject based on the ideXlab platform.
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Propensity Score Matching with competing risks in survival analysis
Statistics in Medicine, 2019Co-Authors: Peter C Austin, Jason P FineAbstract:Propensity-Score Matching is a popular analytic method to remove the effects of confounding due to measured baseline covariates when using observational data to estimate the effects of treatment. Time-to-event outcomes are common in medical research. Competing risks are outcomes whose occurrence precludes the occurrence of the primary time-to-event outcome of interest. All non-fatal outcomes and all cause-specific mortality outcomes are potentially subject to competing risks. There is a paucity of guidance on the conduct of Propensity-Score Matching in the presence of competing risks. We describe how both relative and absolute measures of treatment effect can be obtained when using Propensity-Score Matching with competing risks data. Estimates of the relative effect of treatment can be obtained by using cause-specific hazard models in the matched sample. Estimates of absolute treatment effects can be obtained by comparing cumulative incidence functions (CIFs) between matched treated and matched control subjects. We conducted a series of Monte Carlo simulations to compare the empirical type I error rate of different statistical methods for testing the equality of CIFs estimated in the matched sample. We also examined the performance of different methods to estimate the marginal subdistribution hazard ratio. We recommend that a marginal subdistribution hazard model that accounts for the within-pair clustering of outcomes be used to test the equality of CIFs and to estimate subdistribution hazard ratios. We illustrate the described methods by using data on patients discharged from hospital with acute myocardial infarction to estimate the effect of discharge prescribing of statins on cardiovascular death.
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Propensity Score Matching and complex surveys
Statistical Methods in Medical Research, 2018Co-Authors: Peter C Austin, Nathaniel Jembere, Maria ChiuAbstract:Researchers are increasingly using complex population-based sample surveys to estimate the effects of treatments, exposures and interventions. In such analyses, statistical methods are essential to minimize the effect of confounding due to measured covariates, as treated subjects frequently differ from control subjects. Methods based on the Propensity Score are increasingly popular. Minimal research has been conducted on how to implement Propensity Score Matching when using data from complex sample surveys. We used Monte Carlo simulations to examine two critical issues when implementing Propensity Score Matching with such data. First, we examined how the Propensity Score model should be formulated. We considered three different formulations depending on whether or not a weighted regression model was used to estimate the Propensity Score and whether or not the survey weights were included in the Propensity Score model as an additional covariate. Second, we examined whether matched control subjects should retain their natural survey weight or whether they should inherit the survey weight of the treated subject to which they were matched. Our results were inconclusive with respect to which method of estimating the Propensity Score model was preferable. In general, greater balance in measured baseline covariates and decreased bias was observed when natural retained weights were used compared to when inherited weights were used. We also demonstrated that bootstrap-based methods performed well for estimating the variance of treatment effects when outcomes are binary. We illustrated the application of our methods by using the Canadian Community Health Survey to estimate the effect of educational attainment on lifetime prevalence of mood or anxiety disorders.
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the use of bootstrapping when using Propensity Score Matching without replacement a simulation study
Statistics in Medicine, 2014Co-Authors: Peter C Austin, Dylan S SmallAbstract:Propensity-Score Matching is frequently used to estimate the effect of treatments, exposures, and interventions when using observational data. An important issue when using Propensity-Score Matching is how to estimate the standard error of the estimated treatment effect. Accurate variance estimation permits construction of confidence intervals that have the advertised coverage rates and tests of statistical significance that have the correct type I error rates. There is disagreement in the literature as to how standard errors should be estimated. The bootstrap is a commonly used resampling method that permits estimation of the sampling variability of estimated parameters. Bootstrap methods are rarely used in conjunction with Propensity-Score Matching. We propose two different bootstrap methods for use when using Propensity-Score Matching without replacementand examined their performance with a series of Monte Carlo simulations. The first method involved drawing bootstrap samples from the matched pairs in the Propensity-Score-matched sample. The second method involved drawing bootstrap samples from the original sample and estimating the Propensity Score separately in each bootstrap sample and creating a matched sample within each of these bootstrap samples. The former approach was found to result in estimates of the standard error that were closer to the empirical standard deviation of the sampling distribution of estimated effects.
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optimal caliper widths for Propensity Score Matching when estimating differences in means and differences in proportions in observational studies
Pharmaceutical Statistics, 2011Co-Authors: Peter C AustinAbstract:In a study comparing the effects of two treatments, the Propensity Score is the probability of assignment to one treatment conditional on a subject's measured baseline covariates. Propensity-Score Matching is increasingly being used to estimate the effects of exposures using observational data. In the most common implementation of Propensity-Score Matching, pairs of treated and untreated subjects are formed whose Propensity Scores differ by at most a pre-specified amount (the caliper width). There has been a little research into the optimal caliper width. We conducted an extensive series of Monte Carlo simulations to determine the optimal caliper width for estimating differences in means (for continuous outcomes) and risk differences (for binary outcomes). When estimating differences in means or risk differences, we recommend that researchers match on the logit of the Propensity Score using calipers of width equal to 0.2 of the standard deviation of the logit of the Propensity Score. When at least some of the covariates were continuous, then either this value, or one close to it, minimized the mean square error of the resultant estimated treatment effect. It also eliminated at least 98% of the bias in the crude estimator, and it resulted in confidence intervals with approximately the correct coverage rates. Furthermore, the empirical type I error rate was approximately correct. When all of the covariates were binary, then the choice of caliper width had a much smaller impact on the performance of estimation of risk differences and differences in means. Copyright © 2010 John Wiley & Sons, Ltd.
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some methods of Propensity Score Matching had superior performance to others results of an empirical investigation and monte carlo simulations
Biometrical Journal, 2009Co-Authors: Peter C AustinAbstract:Propensity-Score Matching is increasingly being used to reduce the impact of treatment-selection bias when estimating causal treatment effects using observational data. Several Propensity-Score Matching methods are currently employed in the medical literature: Matching on the logit of the Propensity Score using calipers of width either 0.2 or 0.6 of the standard deviation of the logit of the Propensity Score; Matching on the Propensity Score using calipers of 0.005, 0.01, 0.02, 0.03, and 0.1; and 5 1 digit Matching on the Propensity Score. We conducted empirical investigations and Monte Carlo simulations to investigate the relative performance of these competing methods. Using a large sample of patients hospitalized with a heart attack and with exposure being receipt of a statin prescription at hospital discharge, we found that the 8 different methods produced Propensity-Score matched samples in which qualitatively equivalent balance in measured baseline variables was achieved between treated and untreated subjects. Seven of the 8 Propensity-Score matched samples resulted in qualitatively similar estimates of the reduction in mortality due to statin exposure. 5 1 digit Matching resulted in a qualitatively different estimate of relative risk reduction compared to the other 7 methods. Using Monte Carlo simulations, we found that Matching using calipers of width of 0.2 of the standard deviation of the logit of the Propensity Score and the use of calipers of width 0.02 and 0.03 tended to have superior performance for estimating treatment effects (© 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Hideo Yasunaga - One of the best experts on this subject based on the ideXlab platform.
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phenytoin versus fosphenytoin for second line treatment of status epilepticus Propensity Score Matching analysis using a nationwide inpatient database
Seizure-european Journal of Epilepsy, 2020Co-Authors: Kensuke Nakamura, Hiroyuki Ohbe, Hiroki Matsui, Kiyohide Fushimi, Hiromu Naraba, Hidehiko Nakano, Yuji Takahashi, Hideo YasunagaAbstract:Abstract Purpose For status epilepticus, the choice of antiepileptic drugs for second-line treatment after benzodiazepine remains controversial: phenytoin or fosphenytoin are recommended, however, it has been unknown which is better. Using a nationwide database, we compared the efficacy and safety of them. Method An observational study conducted with the Japanese Diagnosis Procedure Combination inpatient database identified adult patients who had been admitted for status epilepticus and who had received intravenous diazepam on the day of admission from January 1, 2011 through December 31, 2015. Propensity Score Matching was applied to compare outcomes of the phenytoin and fosphenytoin groups. Results The analysis examined data of 5265 patients: 2969 patients received phenytoin; 2296 received fosphenytoin, on the day of admission. One-to-one Propensity Score Matching created 1871 matched pairs. No significant difference was found for vasopressor use on the day of admission (4.2 % vs. 4.4 %; odds ratio 1.07; 95 % confidence intervals 0.77–1.48; p = 0.69), or for mechanical ventilation on the day of admission, in-hospital mortality, length of hospital stay, or total hospitalization cost. Higher age, comorbidity of cardiac diseases and lower body mass index were associated significantly with increased vasopressor use, whereas the dose of phenytoin equivalents and the choice of fosphenytoin were not. Conclusions This nationwide observational study found no evidence that fosphenytoin provides higher efficacy or safety than phenytoin for treatment of status epilepticus in adults after diazepam. Age, cardiac disease and low body mass index were identified as independent risk factors for vasopressor use in both phenytoin and fosphenytoin.
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levetiracetam vs fosphenytoin for second line treatment of status epilepticus Propensity Score Matching analysis using a nationwide inpatient database
Frontiers in Neurology, 2020Co-Authors: Kensuke Nakamura, Hiroyuki Ohbe, Hiroki Matsui, Aiki Marushima, Kiyohide Fushimi, Yuji Takahashi, Yoshiaki Inoue, Hideo YasunagaAbstract:OBJECTIVE: Status epilepticus is a major emergency condition. The choice of antiepileptic drugs for second-line treatment after benzodiazepine remains controversial, including levetiracetam versus fosphenytoin. We compare the safety of intravenous levetiracetam and fosphenytoin as a second-line treatment in patients with status epilepticus using a nationwide database. METHODS: An observational study conducted with the Japanese Diagnosis Procedure Combination inpatient database identified adult patients who had been admitted for status epilepticus and who had received intravenous diazepam on the day of admission from March 1, 2011 to March 31, 2018. Patients who received intravenous levetiracetam on the day of admission were defined as the levetiracetam group and those who received intravenous fosphenytoin on the day of admission were defined as the fosphenytoin group. Propensity Score Matching was performed to compare outcomes obtained for the levetiracetam and fosphenytoin groups. RESULTS: The analysis examined data of 5667 patients. Overall, 1,403 (25%) patients received levetiracetam; 4,264 (75%) received fosphenytoin. One-to-one Propensity Score Matching created 1,363 matched pairs. No significant difference was found in in-hospital mortality (5.2% vs. 5.1%; odds ratio, 1.03; 95% confidence interval, 0.73–1.46). The proportion of vasopressor use on the day of admission was significantly lower for the levetiracetam group than for the fosphenytoin group (3.2% vs. 4.9%; odds ratio, 0.63; 95% confidence interval, 0.43–0.92). No significant difference was found in other secondary outcomes including total hospitalization cost. CONCLUSION: Levetiracetam was related to significantly reduced vasopressor use on the day of admission than that found for fosphenytoin, in adult status epilepticus.
Sadek Wahba - One of the best experts on this subject based on the ideXlab platform.
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Propensity Score Matching methods for nonexperimental causal studies
The Review of Economics and Statistics, 2002Co-Authors: Rajeev Dehejia, Sadek WahbaAbstract:This paper considers causal inference and sample selection bias in nonexperimental settings in which (i) few units in the nonexperimental comparison group are comparable to the treatment units, and (ii) selecting a subset of comparison units similar to the treatment units is difficult because units must be compared across a high-dimensional set of pre-treatment characteristics. We discuss the use of Propensity Score-Matching methods, and implement them using data from the National Supported Work experiment. Following LaLonde (1986), we pair the experimental treated units with nonexperimental comparison units from the CPS and PSID, and compare the estimates of the treatment effect obtained using our methods to the benchmark results from the experiment. For both comparison groups, we show that the methods succeed in focusing attention on the small subset of the comparison units comparable to the treated units and, hence, in alleviating the bias due to systematic differences between the treated and compariso...
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Propensity Score Matching methods for non experimental causal studies
Social Science Research Network, 1998Co-Authors: Dehejia Rajeev, Sadek WahbaAbstract:This paper considers causal inference and sample selection bias in non-experimental settings in which: (i) few units in the non-experimental comparison group are comparable to the treatment units; and (ii) selecting a subset of comparison units similar to the treatment units is difficult because units must be compared across a high-dimensional set of pretreatment characteristics. We discuss the use of Propensity Score Matching methods, and implement them using data from the NSW experiment. Following Lalonde (1986), we pair the experimental treated units with non-experimental comparison units from the CPS and PSID, and compare the estimates of the treatment effect obtained using our methods to the benchmark results from the experiment. For both comparison groups, we show that the methods succeed in focusing attention on the small subset of the comparison units comparable to the treated units and, hence, in alleviating the bias due to systematic differences between the treated and comparison units.
Arnab Majumdar - One of the best experts on this subject based on the ideXlab platform.
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the impacts of speed cameras on road accidents an application of Propensity Score Matching methods
Accident Analysis & Prevention, 2013Co-Authors: Daniel J Graham, Arnab MajumdarAbstract:This paper aims to evaluate the impacts of speed limit enforcement cameras on reducing road accidents in the UK by accounting for both confounding factors and the selection of proper reference groups. The Propensity Score Matching (PSM) method is employed to do this. A naive before and after approach and the empirical Bayes (EB) method are compared with the PSM method. A total of 771 sites and 4787 sites for the treatment and the potential reference groups respectively are observed for a period of 9 years in England. Both the PSM and the EB methods show similar results that there are significant reductions in the number of accidents of all severities at speed camera sites. It is suggested that the Propensity Score can be used as the criteria for selecting the reference group in before-after control studies. Speed cameras were found to be most effective in reducing accidents up to 200 meters from camera sites and no evidence of accident migration was found.
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the impacts of speed cameras on road accidents an application of Propensity Score Matching methods
Transportation Research Board 92nd Annual MeetingTransportation Research Board, 2013Co-Authors: Daniel J Graham, Arnab MajumdarAbstract:This paper aims to evaluate the impacts of speed limit enforcement cameras on reducing road accidents in the UK. The Propensity Score Matching (PSM) method is employed to control for selection bias and selecting proper reference groups. A naive before and after approach and the empirical Bayes (EB) method are compared with the PSM method. The authors observe 771 sites and 4787 sites for the treatment and the potential reference groups respectively for a period of 9 years. Both the PSM and the EB methods show similar results that there are significant reductions in accidents number at all severities at speed camera sites. It is suggested that the Propensity Score can be used as the criteria for selecting the reference group in before-after control studies.
Jielai Xia - One of the best experts on this subject based on the ideXlab platform.
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optimal caliper width for Propensity Score Matching of three treatment groups a monte carlo study
PLOS ONE, 2013Co-Authors: Yongji Wang, Hongwei Cai, Zhiwei Jiang, Ling Wang, Jiugang Song, Jielai XiaAbstract:Propensity Score Matching is a method to reduce bias in non-randomized and observational studies. Propensity Score Matching is mainly applied to two treatment groups rather than multiple treatment groups, because some key issues affecting its application to multiple treatment groups remain unsolved, such as the Matching distance, the assessment of balance in baseline variables, and the choice of optimal caliper width. The primary objective of this study was to compare Propensity Score Matching methods using different calipers and to choose the optimal caliper width for use with three treatment groups. The authors used caliper widths from 0.1 to 0.8 of the pooled standard deviation of the logit of the Propensity Score, in increments of 0.1. The balance in baseline variables was assessed by standardized difference. The Matching ratio, relative bias, and mean squared error (MSE) of the estimate between groups in different Propensity Score-matched samples were also reported. The results of Monte Carlo simulations indicate that Matching using a caliper width of 0.2 of the pooled standard deviation of the logit of the Propensity Score affords superior performance in the estimation of treatment effects. This study provides practical solutions for the application of Propensity Score Matching of three treatment groups.
