Apixaban Benefit in Subclinical Atrial Fibrillation Accounting for Competing Risks: A Reanalysis of the ARTESIA Trial

Apixaban Benefit in Subclinical Atrial Fibrillation Accounting for Competing Risks: A Reanalysis of the ARTESIA Trial | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Apixaban Benefit in Subclinical Atrial Fibrillation Accounting for Competing Risks: A Reanalysis of the ARTESIA Trial Shao-Wei Lo , Darae Ko , Dae Hyun Kim , Long H. Ngo , Daniel E Singer , View ORCID Profile Sachin J Shah doi: https://doi.org/10.1101/2025.09.19.25335927 Shao-Wei Lo 1 Department of Medicine, Cleveland Clinic Foundation , Cleveland, Ohio, USA MD, MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Darae Ko 2 Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife , Boston, Massachusetts, USA 3 Department of Medicine, Harvard Medical School , Boston, Massachusetts, USA MD, MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dae Hyun Kim 2 Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife , Boston, Massachusetts, USA 3 Department of Medicine, Harvard Medical School , Boston, Massachusetts, USA MD, MPH, ScD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Long H. Ngo 3 Department of Medicine, Harvard Medical School , Boston, Massachusetts, USA 4 Department of Medicine, Beth Israel Deaconess Medical Center , Boston, Massachusetts, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel E Singer 3 Department of Medicine, Harvard Medical School , Boston, Massachusetts, USA 5 Department of Medicine, Massachusetts General Hospital , Boston, Massachusetts, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sachin J Shah 3 Department of Medicine, Harvard Medical School , Boston, Massachusetts, USA 5 Department of Medicine, Massachusetts General Hospital , Boston, Massachusetts, USA MD, MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sachin J Shah For correspondence: sshah{at}mgh.harvard.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF ABSTRACT Background It is uncertain if anticoagulants provide a net benefit in subclinical AF (SCAF). The ARTESIA trial showed apixaban reduced the relative hazard of stroke/systemic embolism (SSE) in SCAF, but did not report absolute risk reduction (ARR). The reported Kaplan-Meier analysis did not account for the competing risk of death or 24-hour AF events. We reanalyzed ARTESIA accounting for competing risks to determine the ARR of apixaban vs. aspirin. Methods Individual time-to-event and time-to-censoring data were extracted from the published Kaplan-Meier curve. ARTESIA classified deaths and 24-hour AF events as censoring events. We probabilistically reclassified them to competing events to estimate the ARR. We used the Aalen-Johansen estimator to estimate the cumulative risk of stroke/systemic embolism at 3.5 years, accounting for competing events. We compared these results to the Kaplan-Meier estimator. Result After reclassification of deaths and 24-hour AF to competing events, there were 1111 censoring and 852 competing events in the apixaban arm, and 1100 censoring and 816 competing events in the aspirin arm. Compared to the Aalen-Johansen estimator, the Kaplan-Meier estimator overestimated the cumulative SSE risk at 3.5 years with apixaban (2.64% vs. 2.20%, difference in estimates 0.43 percentage points; 95% CI 0.27 to 0.62) and with aspirin (4.58% vs. 3.82%, difference in estimates 0.76 percentage points; 95% CI 0.56 to 1.01). Compared to the Aalen-Johansen estimator, the Kaplan-Meier estimator overestimated the ARR of SSE risk at 3.5 years with apixaban compared to aspirin (1.94% vs. 1.61%, difference in estimates 0.33 percentage points; 95% CI, 0.06 to 0.64). Conclusion In SCAF, apixaban reduced the 3.5-year risk of SSE by 1.61%, which would be overestimated by 20% if death and >24-hour AF events are treated as censoring rather than competing events. Introduction It is uncertain if anticoagulants provide a net benefit in subclinical atrial fibrillation (SCAF). Apixaban for the Reduction of Thrombo-Embolism in Patients with Device-Detected Subclinical Atrial Fibrillation (ARTESIA) randomized participants with SCAF between 6 minutes and 24 hours and a CHA 2 DS 2 -VASc score ≥2 to either apixaban or aspirin to prevent stroke and systemic embolism (SSE). 1 When individuals developed >24 hours of AF, they were censored and started on oral anticoagulants. The trial showed apixaban reduced the relative hazard of SSE. However, ARTESIA did not report absolute risk reduction (ARR), the measure most relevant for clinical decision-making. 2 The published Kaplan-Meier analysis did not account for the competing risk of death or >24-hour AF events, which were common in ARTESIA, 18% and 24%, respectively. These competing events occurred before the primary outcome and precluded assessing the study’s primary outcome. Failing to account for competing risks overestimates the cumulative risk in each arm and may overestimate the ARR. 3 We reanalyzed ARTESIA to estimate the ARR of apixaban vs. aspirin, accounting for competing events. Methods ARTESIA’s analytic goal was to measure the causal hazard ratio, treating death and >24-hour AF events as censoring events. To accomplish our goal of measuring the ARR, a different estimand, we used an estimator that treated deaths and >24-hour AF events as competing events—deaths because they precluded the primary outcome, and >24-hour AF events, because they made participants ineligible for the trial. To conduct the analysis, we extracted time-to-event and censoring data from ARTESIA’s Kaplan–Meier curves using the IPDfromKM package. 4 In ARTESIA, participants were censored for death, >24-hour AF, dropout, or study end. We reclassified deaths and >24-hour AF from censoring to competing events. Because the trial did not report which censoring events occurred at each time point, we could not directly reclassify deaths and >24-hour AF events. Instead, we probabilistically reclassified these censoring events as competing events. For each arm (a) and year (y), the probability that a censoring event represented a competing event was: where N=number at risk, p=annual event probabilities, w=Gompertz weight for age-related mortality, and C=total censoring events. We used these probabilities ( P ay ) to reclassify censoring events as competing events. We repeated this probabilistic reclassification 1,000 times, yielding 1,000 analytic datasets. Results were averaged across these datasets to reflect the uncertainty introduced by probabilistic reclassification. Using the reclassified data, we plotted the cumulative risk using the Kaplan-Meier estimator, which treated deaths and >24-hour AF events as censoring events, and the Aalen-Johansen estimator, 5 which treated them as competing events. Then, we used both estimators to calculate the cumulative risk of SSE in each arm and the ARR at 3.5 years, consistent with other secondary analyses of ARTESIA. 6 , 7 We calculated the differences in estimates and obtained 95% confidence intervals using 1,000 paired nonparametric bootstrapped replicates of the individual-level data, resampling within each arm and recomputing both the Kaplan–Meier and Aalen–Johansen estimates in each replicate; this ensures the computed confidence intervals accounted for the correlation between estimators. Results The extracted ARTESIA data included 1963 censoring events in the apixaban arm and 1916 in the aspirin arm. After reclassifying deaths and 24-hour AF events as competing events, there were 1111 censoring events and 852 competing events in the apixaban arm, and 1100 censoring events and 816 competing events in the aspirin arm. Compared to the Aalen-Johansen estimator, the Kaplan-Meier estimator overestimated the cumulative SSE risk at 3.5 years with apixaban (2.64% vs. 2.20%, difference in estimates 0.43 percentage points; 95% CI 0.27 to 0.62) and with aspirin (4.58% vs. 3.82%, difference in estimates 0.76 percentage points; 95% CI 0.56 to 1.01). Compared to the Aalen-Johansen estimator, the Kaplan-Meier estimator overestimated the ARR of SSE risk at 3.5 years with apixaban compared to aspirin (1.94% vs. 1.61%, difference in estimates 0.33 percentage points; 95% CI, 0.06 to 0.64). Conclusion Competing risk analysis provides a more realistic estimate of patient risks at the point of treatment decision-making. 8 Using a competing risk analysis, apixaban, compared to aspirin, lowered the absolute SSE risk for people with SCAF by 1.61 percent at 3.5 years. This absolute benefit is overestimated by 20% if death and >24-hour AF events are treated as censoring rather than competing events. Our study has limitations. Using extracted Kaplan-Meier data necessitated probabilistic identification of competing events, precluded subgroup analysis, and may have introduced approximation errors in event timing. Also, we could not assess how competing risks affect bleeding risk because time-to-bleeding data were unavailable. Accurate estimation of the ARR is important because, as with clinical AF, the expected absolute reduction in SSE with anticoagulants must be sufficiently large to outweigh their harms. This reanalysis highlights the importance of competing risks methods when measuring the absolute benefit of cardiovascular interventions. Data Availability Original data can be obtained http://doi.org/10.1056/NEJMoa2310234 . Analytic data used in this analysis and complete replication of statistical code are available at: https://github.com/MGH-DGIM-SHAH/ARTESIA Data sharing Complete replication of statistical code, including data, is available at: https://github.com/MGH-DGIM-SHAH/ARTESIA Ethical review The Mass General Brigham Institutional Review Board deemed this analysis to be nonhuman subjects research and waived review (#3469). Conflict of Interest Disclosure Dr. Shah reported funding from the National Institute on Aging/National Institutes of Health related to the conduct of this study (noted below). Dr. Singer receives research support from Bristol Myers Squibb-Pfizer and has received consulting fees from Bristol Myers Squibb. Dr. Kim is a consultant to Pfizer. The remaining authors have nothing to disclose. Funding This analysis was funded by the NIA (K76AG074919). Dr. Singer was supported, in part, by the Eliot B. and Edith C. Shoolman Fund of the Massachusetts General Hospital. Role of the Funder/Sponsor The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Download figure Open in new tab Figure. Cumulative incidence of stroke or systemic embolism in the ARTESIA trial, treating death and 24-hour AF events as either censoring events or competing events ARR – absolute risk reduction, pp – percentage point The figure displays the cumulative incidence of stroke or systemic embolism by study arm, with death and 24-hour AF events treated as censoring events (using a Kaplan-Meier estimator, mirroring the analysis presented in Healey et al., NEJM 2024) and as competing events (using the Aalen-Johansen estimator). The Aalen-Johansen cumulative incidence line is the average across 1000 datasets in which censoring events were probabilistically reclassified as competing events. ACKNOWLEDGMENTS Footnotes Updated manuscript text to include estimand framework and contextualize the new vs. original publication. References 1. ↵ Healey JS , Lopes RD , Granger CB , et al. Apixaban for Stroke Prevention in Subclinical Atrial Fibrillation . N Engl J Med . 2023 ; 0 ( 0 ):null. doi: 10.1056/NEJMoa2310234 OpenUrl CrossRef PubMed 2. ↵ Elting LS , Martin CG , Cantor SB , Rubenstein EB . Influence of data display formats on physician investigators’ decisions to stop clinical trials: prospective trial with repeated measures . BMJ . 1999 ; 318 ( 7197 ): 1527 – 1531 . doi: 10.1136/bmj.318.7197.1527 OpenUrl Abstract / FREE Full Text 3. ↵ Austin Peter C. , Lee Douglas S. , Fine Jason P. Introduction to the Analysis of Survival Data in the Presence of Competing Risks . Circulation . 2016 ; 133 ( 6 ): 601 – 609 . doi: 10.1161/CIRCULATIONAHA.115.017719 OpenUrl Abstract / FREE Full Text 4. ↵ Liu N , Zhou Y , Lee JJ . IPDfromKM: reconstruct individual patient data from published Kaplan-Meier survival curves . BMC Med Res Methodol . 2021 ; 21 ( 1 ): 111 . doi: 10.1186/s12874-021-01308-8 OpenUrl CrossRef PubMed 5. ↵ Aalen OO , Johansen S. An Empirical Transition Matrix for Non-Homogeneous Markov Chains Based on Censored Observations . Scand J Stat . 1978 ; 5 ( 3 ): 141 – 150 . OpenUrl Web of Science 6. ↵ Gouskova NA , Kundu S , Imrey PB , Fine JP . Number needed to treat for time-to-event data with competing risks . Stat Med . 2014 ; 33 ( 2 ): 181 – 192 . doi: 10.1002/sim.5922 OpenUrl CrossRef PubMed 7. ↵ Lopes RD , Granger CB , Wojdyla DM , et al. Apixaban vs Aspirin According to CHA2DS2-VASc Score in Subclinical Atrial Fibrillation . JACC . 2024 ; 84 ( 4 ): 354 – 364 . doi: 10.1016/j.jacc.2024.05.002 OpenUrl CrossRef PubMed 8. ↵ Lesko CR , Zalla LC , Heyward J , Joseph C , Edwards JK . Weighing Risks and Benefits in the Presence of Competing Risks . Curr Epidemiol Rep . 2023 ; 10 ( 4 ): 221 – 239 . doi: 10.1007/s40471-023-00331-1 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted November 18, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. 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