Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners

Desai, Rishi; Franklin, Jessica M.

doi:10.1136/bmj.l5657

Cited by 398 publications

(376 citation statements)

References 30 publications

(51 reference statements)

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“…Propensity score matching (PSM) was used to control possible confounding bias [ 17 ]. The PSM was constructed using age, ASA score, number of nodules, previous abdominal surgery and diagnosis.…”

Section: Methodsmentioning

confidence: 99%

Minimally invasive versus open right hepatectomy: comparative study with propensity score matching analysis

et al. 2020

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Background Minimally invasive liver resections (MILRs) have been increasingly performed in recent years. However, the majority of MILRs are actually minor or limited resections of peripheral lesions. Due to the technical complexity major hepatectomies remain challenging for minimally invasive surgery. The aim of this study was to compare the short and long-term outcomes of patients undergoing minimally invasive right hepatectomies (MIRHs) with contemporary patients undergoing open right hepatectomies (ORHs) Methods Consecutive patients submitted to anatomic right hepatectomies between January 2013 and December 2018 in two tertiary referral centers were studied. Study groups were compared on an intention-to-treat basis after propensity score matching (PSM). Overall survival (OS) analyses were performed for the entire cohort and specific etiologies subgroups Results During study period 178 right hepatectomies were performed. After matching, 37 patients were included in MIRH group and 60 in ORH group. The groups were homogenous for all baseline characteristics. MIRHs had significant lower blood loss (400 ml vs. 500 ml, P = 0.01), lower rate of minor complications (13.5% vs. 35%, P = 0.03) and larger resection margins (10 mm vs. 5 mm, P = 0.03) when compared to ORHs. Additionally, a non-significant decrease in hospital stay (ORH 9 days vs. MIRH 7 days, P = 0.09) was observed. No differences regarding the use of Pringle’s maneuver, operative time, overall morbidity or perioperative mortality were observed. OS was similar between the groups (P = 0.13). Similarly, no difference in OS was found in subgroups of patients with primary liver tumors (P = 0.09) and liver metastasis (P = 0.80). Conclusions MIRHs are feasible and safe in experienced hands. Minimally invasive approach was associated with less blood loss, a significant reduction in minor perioperative complications, and did not negatively affect long-term outcomes.

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Section: Methodsmentioning

confidence: 99%

Minimally invasive versus open right hepatectomy: comparative study with propensity score matching analysis

et al. 2020

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“…The use of propensity score matching against other methods used in observational data such as difference-in-difference and regression models for (health) economic analysis has been explored by Crown [94]. Guidance on choosing an appropriate weighting mechanism for propensity score matching is described by Desai and Franklin [95]. These 'matching' methods are useful when there is interest in better defining a group for comparison to reduce bias.…”

Section: Statistical Considerations Based On Study Design Underlyingmentioning

confidence: 99%

Conducting Value for Money Analyses for Non-randomised Interventional Studies Including Service Evaluations: An Educational Review with Recommendations

2020

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This article provides an educational review covering the consideration of conducting 'value for money' analyses as part of non-randomised study designs including service evaluations. These evaluations represent a vehicle for producing evidence such as value for money of a care intervention or service delivery model. Decision makers including charities and local and national governing bodies often rely on evidence from non-randomised data and service evaluations to inform their resource allocation decision-making. However, as randomised data obtained from randomised controlled trials are considered the 'gold standard' for assessing causation, the use of this alternative vehicle for producing an evidence base requires careful consideration. We refer to value for money analyses, but reflect on methods associated with economic evaluations as a form of analysis used to inform resource allocation decision-making alongside a finite budget. Not all forms of value for money analysis are considered a full economic evaluation with implications for the information provided to decision makers. The type of value for money analysis to be conducted requires considerations such as the outcome(s) of interest, study design, statistical methods to control for confounding and bias, and how to quantify and describe uncertainty and opportunity costs to decision makers in any resulting value for money estimates. Service evaluations as vehicles for producing evidence present different challenges to analysts than what is commonly associated with research, randomised controlled trials and health technology appraisals, requiring specific study design and analytic considerations. This educational review describes and discusses these considerations, as overlooking them could affect the information provided to decision makers who may make an 'ill-informed' decision based on 'poor' or 'inaccurate' information with long-term implications. We make direct comparisons between randomised controlled trials relative to non-randomised data as vehicles for assessing causation; given 'gold standard' randomised controlled trials have limitations. Although we use UK-based decision makers as examples, we reflect on the needs of decision makers internationally for evidence-based decision-making specific to resource allocation. We make recommendations based on the experiences of the authors in the UK, reflecting on the wide variety of methods available, used as documented in the empirical literature. These methods may not have been fully considered relevant to non-randomised study designs and/or service evaluations, but could improve and aid the analysis conducted to inform the relevant value for money decision problem.

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“…2. Explain why a specific propensity score approach (e.g., matching or inverse probability weighting) was used, because the effect estimates have varying interpretations (16)(17)(18). 3.…”

Section: Table 2 Key Elements That Should Be Reported In Research Mamentioning

confidence: 99%

Guidance on Statistical Reporting to Help Improve Your Chances of a Favorable Statistical Review

Harhay

Donaldson

2020

Am J Respir Crit Care Med

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Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners

Cited by 398 publications

References 30 publications

Minimally invasive versus open right hepatectomy: comparative study with propensity score matching analysis

Minimally invasive versus open right hepatectomy: comparative study with propensity score matching analysis

Conducting Value for Money Analyses for Non-randomised Interventional Studies Including Service Evaluations: An Educational Review with Recommendations

Guidance on Statistical Reporting to Help Improve Your Chances of a Favorable Statistical Review

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