Benefit of second-line systemic chemotherapy for advanced biliary tract cancer: A propensity score analysis
Florian MoikJakob M. RiedlThomas WinderAngelika TerbuchChristopher RossmannJoanna SzkanderaThomas BauernhoferAnne-Katrin KasparekRenate Schaberl-MoserAndreas ReicherFelix PrinzMartin PichlerHerbert StögerMichael StotzArmin GergerFlorian Posch
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Abstract:
Whether 2nd-line-chemotherapy (2LCTX) + best-supportive-care (BSC) benefits patients with advanced biliary tract cancer (aBTC) more than BSC alone is unclear. We therefore conducted a propensity-score-based comparative effectiveness analysis of overall survival (OS) outcomes in 80 patients with metastatic, recurrent, or inoperable aBTC, of whom 38 (48%) were treated with BSC + 2LCTX and 42 (52%) with BSC alone. After a median follow-up of 14.8 months and 49 deaths, the crude 6-, 12-, and 18-month Kaplan-Meier OS estimates were 77%, 53% and 23% in the BSC + 2LCTX group, and 29%, 21%, and 14% in patients in the BSC group (p = 0.0003; Hazard ratio (HR) = 0.36, 95%CI:0.20-0.64, p = 0.001). An inverse-probability-of-treatment-weighted (IPTW) analysis was conducted to rigorously account for the higher prevalence of favorable prognostic variables in the 2LCTX + BSC group. After IPTW-weighting, the favorable association between 2LCTX and OS prevailed (adjusted HR = 0.40, 95%CI: 0.17-0.95, p = 0.037). IPTW-weighted 6-, 12-, and 18-month OS estimates were 77%, 58% and 33% in the BSC + 2LCTX group, and 39%, 28% and 22% in the BSC group (p = 0.037). Moreover, the benefit of 2LCTX was consistent across several clinically-relevant subgroups. Within the limitations of an observational study, these findings support the concept that 2LCTX + BSC is associated with an OS benefit over BSC alone in aBTC.Keywords:
Biliary Tract Cancer
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KEY POINT Propensity score matching can reduce confounding in observational research by creating two groups that are well balanced with respect to baseline characteristics.Related Article, see p 1618 In this issue of Anesthesia & Analgesia, Miyao and Kotake1 report results of an observational study on the association between hydroxyethyl starch (HES) and renal morbidity in surgical patients. The authors used propensity scores to match 8823 patients who received HES to 8823 controls who had not received HES. In observational research, the treatment groups are not randomly assigned. Rather, treatment assignment is typically affected by individual patient characteristics and provider preference or choice. Therefore, patients with a certain exposure (eg, HES administration) usually systemically differ from patients without the exposure, and such differences may confound the relationship with the outcome (eg, renal morbidity).2 Any direct comparison between treatment groups is therefore likely biased, and statistical methods to reduce confounding are thus required when analyzing observational outcome data. Traditionally, multivariable regression has been used for this purpose.3 More recently, methods based on propensity scores have become popular alternatives.4 This Statistical Minute focuses on propensity score matching (PSM) as applied by Miyao and Kotake.1 Alternatively, and at least equally useful, propensity scores can also be used to weight, rather than to match, observations for subsequent analyses (inverse probability of treatment weighting [IPTW]). Other propensity score–based approaches, such as using propensity scores for stratification or as covariates in regression analyses, are inferior in reducing confounding compared to PSM and IPTW.4 The propensity score is the probability of exposure to a particular treatment given baseline covariates.4 As treatment is usually a binary variable (eg, patient received HES versus did not receive HES), the propensity score is commonly estimated using logistic regression, in which the treatment assignment is the outcome variable, and in which baseline covariates are the independent variables. In PSM, patients who received the treatment are matched to one or several control patients with "similar" propensity scores. Technically, matching is often not performed on the propensity score itself, but on the logit of the propensity score (natural logarithm of the odds of treatment), and a maximum allowable distance (caliper) of 0.2 standard deviations is commonly recommended.4 This results—on average—in groups with comparable covariate patterns; in other words, there is no systematic difference and so these covariates can no longer confound the between-group comparison. This is akin to the situation in a randomized trial. However, randomization controls for both observed and unobserved confounders, while propensity scores can only balance observed confounders. Thus, residual bias is still possible. After matching, researchers should calculate standardized differences (differences in means or proportions divided by the pooled standard deviation) between the matched groups to assess whether the matching was successful in balancing baseline covariates. Generally speaking, absolute standardized differences of <0.1 indicate adequate balance. When baseline covariates are well balanced, the outcome variable(s) can be compared between the 2 groups using standard statistical techniques, including simple hypothesis tests, regression techniques, or survival analysis.3,5 However, there is a considerable debate in statistical literature about whether the matched design must be accounted for in the analysis (eg, whether to use a paired or unpaired t test to compare a continuous outcome).4Figure.: Excerpt from Table 3 and Table 4 in Miyao and Kotake.1 Table 3 shows the improvement in balance among covariates between the groups (only 2 of 36 covariates shown in this excerpt), with a marked reduction of the standardized differences (shown as percent; 1.6% ≙ 0.016) after matching. Table 4 shows the estimated risk of AKI after HES administration before and after PS matching. Note that the unadjusted analysis (crude odds ratio) substantially overestimated the relationship between HES and AKI compared to the more valid PSM analysis (adjusted odds ratio). AKI indicates acute kidney injury; CI, confidence interval; HES, hydroxyethyl starch; IQR, interquartile range; PS, propensity score.
Inverse probability weighting
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Because of limitations in randomized controlled trials, medical researchers are often forced to rely upon studies of observational data. Confounding is a major difficulty encountered in such studies that can create considerable bias in estimates of treatment effects. Propensity score analysis was developed by Rosenbaum & Rubin in 1983 to overcome these difficulties. In essence, a propensity score allows balance to be achieved on confounding covariates in treatment and control groups, thus creating a 'quasi-randomized' trial from observational data. In this study, I illustrate the use of propensity matching to demonstrate that African American race is a significant risk factor for receiving a lower quality donor kidney using a national database on transplantation. I then use propensity matching to demonstrate the benefits of laparoscopic resection for hepatic colorectal metastases. In doing so, the great value of propensity matching in reducing bias in observational studies is demonstrated.
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Application of propensity score as a method for analyzing observational study is very useful.
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Evaluations of vocational education and training (VET) programs play a key role in informing training policy in Australia and elsewhere. Increasingly, such evaluations use observational data from surveys or administrative collections to assess the effectiveness of VET programs and interventions. The difficulty associated with using observational data is that they are inherently prone to selection bias, which results from individuals self-selecting into a given VET program based on differences in background characteristics or other external factors. The effects of the VET program on outcomes of interest are thus confounded with the effects of pre-existing systematic differences between program participants and non-participants. Propensity score matching (PSM) can mitigate selection bias in evaluation studies with observational data by statistically balancing program participants and non-participants post hoc on observed background characteristics. This article seeks to offer a general introduction to PSM and to provide interested VET researchers with an initial stepping stone for using the method in their own work.
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Gold standard (test)
Marginal structural model
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How Can Causal Relationships Be Measured in Observational Studies? Propensity Score Matching: A Tutorial Article
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Propensity score methods are a popular approach to mitigating confounding bias when estimating causal effects in observational studies. When study units are clustered (eg, patients nested within health systems), additional challenges arise such as accounting for unmeasured confounding at multiple levels and dependence between units within the same cluster. While clustered observational data are widely used to draw causal inferences in many fields, including medicine and healthcare, extensions of propensity score methods to clustered settings are still a relatively new area of research. This article presents a framework for estimating causal effects using propensity scores when study units are nested within clusters and are nonrandomly assigned to treatment conditions. We emphasize the need for investigators to examine the nature of the clustering, among other properties, of the observational data at hand in order to guide their choice of causal estimands and the corresponding propensity score approach.
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In this article, we presented the rationale and calculation procedures of the propcnsity score matching (PSM), and its application in the designing stage of an cpidcrniological study. Based on existing observational data, PSM can be used to select one or more comparable controls for each subject in 'treatment' group according to the propensity scores estimated by 'treatment' variable and main covariates. The results of an example analysis showed that the bias for main confounders between the treated and control samples declined more than 55% after PMS. Conclusion: PSM can reduce most of the confounding bias of the observational study, and can obtain approximate study effect to the randomized controlled trials when used in the designing of thc cpidcmiological study.
Key words:
Propensity score matching; Confounding bias; Epidemiology
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