Causal Mediation Analysis for Standardized Mortality Ratios

Daignault, Katherine; Lawson, Keith A.; Finelli, Antonio; Saarela, Olli

doi:10.1097/ede.0000000000001015

Cited by 11 publications

(8 citation statements)

References 20 publications

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“…To assess whether the indicator also measures quality of care relevant to patient outcomes, it can be used as a mediator in causal mediation analysis, if a hypothetical causal pathway exists. Mediation analysis can be used to determine if and how much of the between‐hospital variation in a relevant outcome measure is mediated through the indicator, as suggested by us in Reference 22. A relevant outcome in the case of the partial proportion indicator would be kidney function, as the purpose of the less radical treatment is to preserve this.…”

Section: Discussionmentioning

confidence: 99%

“…These expectations may be ranked, contrasted pairwise between index hospital z and reference hospital z ∗ as E [ Y ( z )]− E [ Y ( z ∗ )], compared with the average level of care in the system, formulated as E [ Y ( z )]− E [ Y ( Z )] = E [ Y ( z )]− E [ Y ], 5 or to the care level of randomly chosen hospital as

E [Y (z)] - \frac{1}{m} \sum_{z^{*} = 1}^{m} E [Y (z^{*})]

21 . We do not consider hospital effects among the patient population of a given hospital here, since these are related to indirect standardization, which we have discussed in causal inference framework elsewhere, 5,22 while our focus for the remainder of this article is on direct standardization. However, we note that expected potential outcomes can be considered more generally with respect to target population specified by a chosen covariate distribution h ( x ) f ( x ), given by

\begin{align} E_{h} false[Y false(z false) false] = \frac{\int_{x} E false[Y false(z false) false| x false] h false(x false) f false(x false) normald x}{\int_{x} h false(x false) f false(x false) normald x}, \end{align}

where the choice of h ( x ) = 1 returns the previous population average potential outcome.…”

Section: Causal Inference Framework For Quality Of Care Comparisonsmentioning

confidence: 99%

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Constructing inverse probability weights for institutional comparisons in healthcare

Tang

Austin

Lawson

et al. 2020

Statistics in Medicine

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In comparing quality of care between hospitals, disease‐specific quality indicators measure structural, process, or outcome elements related to the care of a particular condition. Such comparisons can be framed in terms of causal contrasts, answering the question of whether a patient (or a population of patients on average) would receive different care if treated at the care level of a different hospital. Fair comparisons have to be adjusted for patient case‐mix, which is equivalent to controlling for confounding by the patient‐level factors, including demographic factors, comorbidities, and disease progression. The methodological choice for such comparisons is usually between direct and indirect standardization methods. In this article, we discuss the alternative of inverse probability weighting as a tool for standardization in hospital comparisons. This involves fitting multinomial logistic hospital assignment models and using these to construct the inverse probability weights. The challenge in the present context is the presence of large number of hospitals being compared, many of which have a small patient volume. We propose methods to include small categories in the weighted analysis, as well as metrics and visualizations for checking the positivity/overlap and covariate balance in constructing such weights. The methods are illustrated in a running example using linked administrative data on surgical treatment of kidney cancer patients in Ontario.

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Section: Discussionmentioning

confidence: 99%

E [Y (z)] - \frac{1}{m} \sum_{z^{*} = 1}^{m} E [Y (z^{*})]

\begin{align} E_{h} false[Y false(z false) false] = \frac{\int_{x} E false[Y false(z false) false| x false] h false(x false) f false(x false) normald x}{\int_{x} h false(x false) f false(x false) normald x}, \end{align}

where the choice of h ( x ) = 1 returns the previous population average potential outcome.…”

Section: Causal Inference Framework For Quality Of Care Comparisonsmentioning

confidence: 99%

Constructing inverse probability weights for institutional comparisons in healthcare

Tang

Austin

Lawson

et al. 2020

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

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“…This dataset was cross-linked to kidney cancer diagnosis in the Ontario Cancer Registry and corresponding Ontario Health Insurance Plan (OHIP) billings, as well as to abstracted pathology reports. There is evidence to show that the early-stage radical nephrectomy patients with minimally invasive surgery experienced a shorter length of stay than the patients with open surgery (Semerjian et al, 2015;Bragayrac et al, 2016;Daignault et al, 2019). Our aim was to determine how much of the betweenhospital variation in the length of stay is due to the proportion of the minimally invasive q q q q q q q q −5 0 5 10 15 Total (6.44) Indirect (6.95) Direct (2.93) Cov (−3.44) Components Variance Estimates Method q q FE RE n=5000, q=10 q q q q q q q q −5 0 5 10 15 Total (8.38) Indirect (6.3) Direct (4.25) Cov (−2.16) Components Variance Estimates Method q q FE RE n=5000, q=25 q q q q q q q q −5 0…”

Section: Illustration In Real Datamentioning

confidence: 99%

“…In particular, if between-hospital variation is found in an outcome type indicator, this raises the question of whether this can be explained by between-hospital variation in a process type indicator. For instance, the observed variation of length of the stay after the radical nephrectomy for early-stage (T1-T2) kidney cancer patients may be explained by between-hospital variation in minimally invasive surgery (MIS) rates (Daignault et al, 2019), with the pathway through MIS understood as the indirect (mediated) effect, and all other pathways as the direct effect.…”

Section: Introductionmentioning

confidence: 99%

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Causal mediation analysis decomposition of between-hospital variance

Chen,

Lawson,

Finelli

et al. 2020

Preprint

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Causal variance decompositions for a given disease-specific quality indicator can be used to quantify differences in performance between hospitals or health care providers. While variance decompositions can demonstrate variation in quality of care, causal mediation analysis can be used to study care pathways leading to the differences in performance between the institutions. This raises the question of whether the two approaches can be combined to decompose between-hospital variation in an outcome type indicator to that mediated through a given process (indirect effect) and remaining variation due to all other pathways (direct effect). For this purpose, we derive a causal mediation analysis decomposition of between-hospital variance, discuss its interpretation, and propose an estimation approach based on generalized linear mixed models for the outcome and the mediator. We study the performance of the estimators in a simulation study and demonstrate its use in administrative data on kidney cancer care in Ontario.

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Prognostic score‐based methods for estimating center effects based on survival probability: Application to post‐kidney transplant survival

Lee,

Reese,

Tran

et al. 2024

Statistics in Medicine

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In evaluating the performance of different facilities or centers on survival outcomes, the standardized mortality ratio (SMR), which compares the observed to expected mortality has been widely used, particularly in the evaluation of kidney transplant centers. Despite its utility, the SMR may exaggerate center effects in settings where survival probability is relatively high. An example is one‐year graft survival among U.S. kidney transplant recipients. We propose a novel approach to estimate center effects in terms of differences in survival probability (ie, each center versus a reference population). An essential component of the method is a prognostic score weighting technique, which permits accurately evaluating centers without necessarily specifying a correct survival model. Advantages of our approach over existing facility‐profiling methods include a metric based on survival probability (greater clinical relevance than ratios of counts/rates); direct standardization (valid to compare between centers, unlike indirect standardization based methods, such as the SMR); and less reliance on correct model specification (since the assumed model is used to generate risk classes as opposed to fitted‐value based ‘expected’ counts). We establish the asymptotic properties of the proposed weighted estimator and evaluate its finite‐sample performance under a diverse set of simulation settings. The method is then applied to evaluate U.S. kidney transplant centers with respect to graft survival probability.

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Causal Mediation Analysis for Standardized Mortality Ratios

Cited by 11 publications

References 20 publications

Constructing inverse probability weights for institutional comparisons in healthcare

Constructing inverse probability weights for institutional comparisons in healthcare

Causal mediation analysis decomposition of between-hospital variance

Prognostic score‐based methods for estimating center effects based on survival probability: Application to post‐kidney transplant survival

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