Dataset Information

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index.

ABSTRACT: When randomized controlled trials are not feasible, retrospective studies using big data provide an efficient and cost-effective alternative, though they are at risk for treatment selection bias. Treatment selection bias occurs in a non-randomized study when treatment selection is based on pre-treatment characteristics that are also associated with the outcome. These pre-treatment characteristics, or confounders, can influence evaluation of a treatment's effect on the outcome. Propensity scores minimize this bias by balancing the known confounders between treatment groups. There are a few approaches to performing propensity score analyses, including stratifying by the propensity score, propensity matching, and inverse probability of treatment weighting (IPTW). Described here is the use of IPTW to balance baseline comorbidities in a cohort of patients within the US Military Health System Data Repository (MDR). The MDR is a relatively optimal data source, as it provides a contained cohort in which nearly complete information on inpatient and outpatient services is available for eligible beneficiaries. Outlined below is the use of the MDR supplemented with information from the national death index to provide robust mortality data. Also provided are suggestions for using administrative data. Finally, the protocol shares an SAS code for using IPTW to balance known confounders and plot the cumulative incidence function for the outcome of interest.

SUBMITTER: Mitchell JD

PROVIDER: S-EPMC7652599 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index.

Mitchell Joshua D JD Gage Brian F BF Fergestrom Nicole N Novak Eric E Villines Todd C TC

Journal of visualized experiments : JoVE 20200108 155

When randomized controlled trials are not feasible, retrospective studies using big data provide an efficient and cost-effective alternative, though they are at risk for treatment selection bias. Treatment selection bias occurs in a non-randomized study when treatment selection is based on pre-treatment characteristics that are also associated with the outcome. These pre-treatment characteristics, or confounders, can influence evaluation of a treatment's effect on the outcome. Propensity scores ...[more]

PMID: 31984959

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Project description:BACKGROUND:It is important to estimate the treatment effect of interest accurately and precisely within the analysis of randomised controlled trials. One way to increase precision in the estimate and thus improve the power for randomised trials with continuous outcomes is through adjustment for pre-specified prognostic baseline covariates. Typically covariate adjustment is conducted using regression analysis, however recently, Inverse Probability of Treatment Weighting (IPTW) using the propensity score has been proposed as an alternative method. For a continuous outcome it has been shown that the IPTW estimator has the same large sample statistical properties as that obtained via analysis of covariance. However the performance of IPTW has not been explored for smaller population trials (<?100 participants), where precise estimation of the treatment effect has potential for greater impact than in larger samples. METHODS:In this paper we explore the performance of the baseline adjusted treatment effect estimated using IPTW in smaller population trial settings. To do so we present a simulation study including a number of different trial scenarios with sample sizes ranging from 40 to 200 and adjustment for up to 6 covariates. We also re-analyse a paediatric eczema trial that includes 60 children. RESULTS:In the simulation study the performance of the IPTW variance estimator was sub-optimal with smaller sample sizes. The coverage of 95% CI's was marginally below 95% for sample sizes <?150 and???100. For sample sizes <?100 the coverage of 95% CI's was always significantly below 95% for all covariate settings. The minimum coverage obtained with IPTW was 89% with n?=?40. In comparison, regression adjustment always resulted in 95% coverage. The analysis of the eczema trial confirmed discrepancies between the IPTW and regression estimators in a real life small population setting. CONCLUSIONS:The IPTW variance estimator does not perform so well with small samples. Thus we caution against the use of IPTW in small sample settings when the sample size is less than 150 and particularly when sample size <?100.

Project description:BackgroundThe prevalence of Short Birth Interval (SBI) is higher in Low- and Middle-Income countries (LMICs), including Bangladesh. Previous studies in LMICs have estimated the effects of SBI on child mortality by comparing two unequal groups of mothers based on their socio-economic status. This approach may lead to overestimation or underestimation of the true effect of birth interval on child mortality, particularly when sample sizes are relatively small.ObjectiveWe determined the effects of SBI on several forms of child mortality in Bangladesh by comparing two equal groups created by applying the propensity score matching technique.MethodsThis study analyzed data from 5,941 mothers and 1,594 health facilities extracted from the 2017/18 Bangladesh Demographic and Health Survey and the 2017 Bangladesh Health Facility Survey. The exposure variable was SBI (defined as the interval between two subsequent births <33 months: yes, no), while the outcome variables were neonatal mortality (defined as mortality within 28 days of birth: yes, no), infant mortality (defined as mortality within 1 year of birth: yes, no), and under-five mortality (defined as mortality within 5 years of birth: yes, no). Multilevel Poisson regression based on inverse probability treatment weights was used to determine the association between exposure and outcome variables.ResultsThe prevalence rates of neonatal, infant, and under-five mortality were 48.8, 30.8, and 23.1 per 1000 live births, respectively. Newborns of SBI mothers were found to have a 63% higher likelihood of neonatal mortality (aPR, 1.63; 95% CI, 1.08-2.46) compared to newborns of non-SBI mothers. Furthermore, the prevalence of infant mortality and under-five mortality was 1.45 times higher (aPR, 1.45; 95% CI, 1.01-2.08) and 2.82 times higher (aPR, 2.82; 95% CI, 2.16-3.70), respectively, among babies born in a short interval of their immediately preceding sibling as compared to babies born in a normal interval of their immediately preceding sibling.ConclusionsFindings of this study indicate that SBI is an important predictor of child mortality. Consequently, around 1 million children born in a short interval every year in Bangladesh are at risk of dying before reaching their fifth birthday. This indicates a challenge for Bangladesh to achieve the SDG 3 target to reduce neonatal and under-five mortality to 12 and 25 deaths per 1000 live births, respectively. Hence, awareness-building programs about the adverse effects of SBI and strengthening existing healthcare facilities are important.

Dataset Information

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index.

Publications

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index.

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