Number needed to treat for time‐to‐event data with competing risks

Self Cite

111

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.

Section: Statistical Methods For Propensity‐score Matching In the Prementioning

confidence: 99%

“…The reciprocal of the difference in CIF functions provides an estimate of the NNT in the presence of competing risks. 26 Fine and Gray introduced the subdistribution hazard function for the kth event type: sd…”

Section: Estimating the Effect Of Treatment On The Cif When Using Promentioning

confidence: 99%

Propensity‐score matching with competing risks in survival analysis

Austin

Fine

2018

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111

\int_{0}^{t} f_{12} false(u false| z false) d u

for z =0,1.…”

Section: Application In Cardiac Surgerymentioning

confidence: 99%

“…The 95% CIs were estimated by bootstrapping with 1000 replications. We did not report the 95% CIs for the NNT functions when the ARR contained zero …”

Section: Application In Cardiac Surgerymentioning

confidence: 99%

“…In addition to marginal HRs associated with SVD, we also estimated the marginal cumulative incidence function (CIF) for SVD depending on the presence or absence of severe PPM, and the corresponding absolute risk reduction (ARR) and number needed to treat (NNT) functions, 2 statistical indicators easy to interpret and communicate to guide clinical practice. 35 The marginal CIFs of SVD at time t in both groups were calculated as ∫ t 0 f 12 (u|z)du for z = 0, 1. The ARR function was defined as the absolute difference in the CIFs for SVD between the presence and absence of severe PPM, and the NNT function was defined as the reciprocal of the ARR function.…”

Section: Modelling Strategies and Checking Of Assumptionsmentioning

confidence: 99%

See 1 more Smart Citation

Inverse probability weighting to control confounding in an illness‐death model for interval‐censored data

Gillaizeau

Sénage

Borgne

et al. 2017

Multistate models with interval-censored data, such as the illness-death model, are still not used to any considerable extent in medical research regardless of the significant literature demonstrating their advantages compared to usual survival models. Possible explanations are their uncommon availability in classical statistical software or, when they are available, by the limitations related to multivariable modelling to take confounding into consideration. In this paper, we propose a strategy based on propensity scores that allows population causal effects to be estimated: the inverse probability weighting in the illness semi-Markov model with interval-censored data. Using simulated data, we validated the performances of the proposed approach. We also illustrated the usefulness of the method by an application aiming to evaluate the relationship between the inadequate size of an aortic bioprosthesis and its degeneration or/and patient death. We have updated the R package multistate to facilitate the future use of this method.

Accounting for competing risks in randomized controlled trials: a review and recommendations for improvement

Austin

Fine

2017

Self Cite

101

In studies with survival or time‐to‐event outcomes, a competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. Specialized statistical methods must be used to analyze survival data in the presence of competing risks. We conducted a review of randomized controlled trials with survival outcomes that were published in high‐impact general medical journals. Of 40 studies that we identified, 31 (77.5%) were potentially susceptible to competing risks. However, in the majority of these studies, the potential presence of competing risks was not accounted for in the statistical analyses that were described. Of the 31 studies potentially susceptible to competing risks, 24 (77.4%) reported the results of a Kaplan–Meier survival analysis, while only five (16.1%) reported using cumulative incidence functions to estimate the incidence of the outcome over time in the presence of competing risks. The former approach will tend to result in an overestimate of the incidence of the outcome over time, while the latter approach will result in unbiased estimation of the incidence of the primary outcome over time. We provide recommendations on the analysis and reporting of randomized controlled trials with survival outcomes in the presence of competing risks. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.