How Large a Study Is Needed to Detect TKA Revision Rate Reductions Attributable to Robotic or Navigated Technologies? A Simulation-based Power Analysis
Robotic and navigated TKA procedures have been introduced to improve component placement precision in the hope of improving implant survivorship and other clinical outcomes. Although numerous comparative studies have shown enhanced precision and accuracy in placing components, most comparative studi...
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| Veröffentlicht in: | Clinical orthopaedics and related research 2021-11, Vol.479 (11), p.2350-2361 |
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| description | Robotic and navigated TKA procedures have been introduced to improve component placement precision in the hope of improving implant survivorship and other clinical outcomes. Although numerous comparative studies have shown enhanced precision and accuracy in placing components, most comparative studies have not shown that such interventions result in improved implant survival. Given what we know about effect sizes from large arthroplasty registries, large cohort studies, and large randomized controlled trials (RCTs), we wondered how large randomized trials would need to be to detect such small differences, and if the number is very high, what that would tell us about the value of these treatments for preventing revision surgery.
In this simulation study, we asked: Given that survivorship differences between technology-assisted TKA (TA-TKA, which we defined as either navigated or robot-assisted TKA) and conventional TKA are either small or absent based on large arthroplasty registries, large cohort studies, and large RCTs, how large would randomized trials need to be to detect small differences between TA-TKA and conventional TKA if they exist, and how long would the follow-up period need to be to have a reasonable chance to detect those differences?
We used estimated effect sizes drawn from previous clinical and registry studies, combined with estimates of the accuracy and precision of various navigation and robotic systems, to model and simulate the likely outcomes of potential comparative clinical study designs. To characterize the ranges of patients enrolled and general follow-up times associated with traditional RCT studies, we conducted a structured search of previously published studies evaluating the effect of robotics and navigation on revision rates compared with that of conventional TKA. The structured search of the University of British Columbia's library database (which automatically searches medical publication databases such as PubMed, Embase, Medline, and Web of Science) and subsequent searching through included studies' reference lists yielded 103 search results. Only clinical studies assessing implant survival differences between patient cohorts of TA-TKA and conventional TKA were included. Studies analyzing registry data, using cadaver specimens, assessing revision TKA, conference proceedings, and preprint services were excluded. Twenty studies met all our inclusion criteria, but only one study reported a statistically significant differen |
| doi_str_mv | 10.1097/CORR.0000000000001909 |
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In this simulation study, we asked: Given that survivorship differences between technology-assisted TKA (TA-TKA, which we defined as either navigated or robot-assisted TKA) and conventional TKA are either small or absent based on large arthroplasty registries, large cohort studies, and large RCTs, how large would randomized trials need to be to detect small differences between TA-TKA and conventional TKA if they exist, and how long would the follow-up period need to be to have a reasonable chance to detect those differences?
We used estimated effect sizes drawn from previous clinical and registry studies, combined with estimates of the accuracy and precision of various navigation and robotic systems, to model and simulate the likely outcomes of potential comparative clinical study designs. To characterize the ranges of patients enrolled and general follow-up times associated with traditional RCT studies, we conducted a structured search of previously published studies evaluating the effect of robotics and navigation on revision rates compared with that of conventional TKA. The structured search of the University of British Columbia's library database (which automatically searches medical publication databases such as PubMed, Embase, Medline, and Web of Science) and subsequent searching through included studies' reference lists yielded 103 search results. Only clinical studies assessing implant survival differences between patient cohorts of TA-TKA and conventional TKA were included. Studies analyzing registry data, using cadaver specimens, assessing revision TKA, conference proceedings, and preprint services were excluded. Twenty studies met all our inclusion criteria, but only one study reported a statistically significant difference between the conventional and robotic or navigated groups. Next, we generated a large set of patients with simulated TKA (1.5 million), randomly assigning each simulated patient a set of patient-specific factors (age at the index surgery, gender, and BMI) drawn from data from registries and published information. We divided this set of simulated procedures into four groups, each associated with a coronal alignment precision reported for different types of surgical procedures, and randomly assigned each patient an overall coronal alignment consistent with their group's precision. TA procedures were modeled based on the alignment precision that an intervention could deliver, regardless of whether the technology used was navigation- or robot-assisted. To evaluate the power associated with using different cohort sizes, we ran a Monte Carlo simulation generating 3000 simulated populations that were drawn (with replacement) from the large set of simulated patients with TKA. We simulated the time to revision for aseptic loosening for each patient, computed the corresponding Kaplan-Meier survival curves, and applied a log-rank test to each study for statistical differences in revision rates at concurrent follow-up timepoints (1-25 years). From each simulation associated with a given cohort size, we determined the percentage of simulated studies that found a statistically significant difference at each follow-up interval. For each alternative precision, we then also calculated the expected reduction in revision rates (effect size) attributable to TA-TKA intervention and the number needed to treat (NNT) using TA-TKA to prevent one revision at 2, 5, 10, and 15 years after index surgery for the entire set of Kaplan-Meier survival analyses.
The results from our simulation found survivorship differences favoring TA-TKA ranging from 1.4% to 2.0% at 15 years of follow-up. Comparative studies would need to enroll between 2500 and 4000 patients in each arm of the study, depending on the precision of the navigated or robotic procedure, to have an 80% chance of showing this reduction in revision rates at 15 years of follow-up. For the highest precision simulated intervention, the NNT using TA-TKA to prevent one revision was 1000 at 2 years, 334 at 5 years, 100 at 10 years, and 50 at 15 years post-index surgery.
Based on these simulations, it appears that TA-TKA interventions could potentially result in a relative reduction in revision rates as large as 27% (from 7.5% down to about 5.5% at 15 years for the intervention with the most precise coronal alignment); however, since this 2% absolute reduction in revision rates is relatively small in comparison with the baseline success rate of TKA and would not be realized until 15 years after the index surgery, traditional RCT studies would require excessively large numbers of patients to be enrolled and excessively long follow-up times to demonstrate whether such a reduction actually exists.
Given that the NNTs to avoid revisions at various time points are predicted to be high, it would require correspondingly low system costs to justify broad adoption of TA-TKA based on avoided revision costs alone, though we speculate that technology assistance could perhaps prove to be cost effective in the care of patients who are at an elevated risk of revision.</description><identifier>ISSN: 0009-921X</identifier><identifier>EISSN: 1528-1132</identifier><identifier>DOI: 10.1097/CORR.0000000000001909</identifier><identifier>PMID: 34351313</identifier><language>eng</language><publisher>United States: Wolters Kluwer</publisher><subject>Aged ; Aged, 80 and over ; Arthroplasty ; Arthroplasty, Replacement, Knee - statistics & numerical data ; Bone implants ; Clinical Studies as Topic - methods ; Clinical trials ; Computer Simulation ; Female ; Humans ; Intervention ; Joint surgery ; Male ; Middle Aged ; Monte Carlo simulation ; Patient Selection ; Patients ; Registries ; Reoperation - statistics & numerical data ; Research Design ; Robotic surgery ; Robotic Surgical Procedures - statistics & numerical data ; Robotics ; Statistical analysis ; Survival</subject><ispartof>Clinical orthopaedics and related research, 2021-11, Vol.479 (11), p.2350-2361</ispartof><rights>Wolters Kluwer</rights><rights>Copyright © 2021 by the Association of Bone and Joint Surgeons.</rights><rights>2021 by the Association of Bone and Joint Surgeons</rights><rights>2021 by the Association of Bone and Joint Surgeons 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5123-fbbe9a19c157ed5a1e1a4f4c71b9df6507a176edae84d221ae48026e66bbe7393</citedby><cites>FETCH-LOGICAL-c5123-fbbe9a19c157ed5a1e1a4f4c71b9df6507a176edae84d221ae48026e66bbe7393</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8509967/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8509967/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34351313$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hickey, Matthew D.</creatorcontrib><creatorcontrib>Anglin, Carolyn</creatorcontrib><creatorcontrib>Masri, Bassam</creatorcontrib><creatorcontrib>Hodgson, Antony J.</creatorcontrib><title>How Large a Study Is Needed to Detect TKA Revision Rate Reductions Attributable to Robotic or Navigated Technologies? A Simulation-based Power Analysis</title><title>Clinical orthopaedics and related research</title><addtitle>Clin Orthop Relat Res</addtitle><description>Robotic and navigated TKA procedures have been introduced to improve component placement precision in the hope of improving implant survivorship and other clinical outcomes. Although numerous comparative studies have shown enhanced precision and accuracy in placing components, most comparative studies have not shown that such interventions result in improved implant survival. Given what we know about effect sizes from large arthroplasty registries, large cohort studies, and large randomized controlled trials (RCTs), we wondered how large randomized trials would need to be to detect such small differences, and if the number is very high, what that would tell us about the value of these treatments for preventing revision surgery.
In this simulation study, we asked: Given that survivorship differences between technology-assisted TKA (TA-TKA, which we defined as either navigated or robot-assisted TKA) and conventional TKA are either small or absent based on large arthroplasty registries, large cohort studies, and large RCTs, how large would randomized trials need to be to detect small differences between TA-TKA and conventional TKA if they exist, and how long would the follow-up period need to be to have a reasonable chance to detect those differences?
We used estimated effect sizes drawn from previous clinical and registry studies, combined with estimates of the accuracy and precision of various navigation and robotic systems, to model and simulate the likely outcomes of potential comparative clinical study designs. To characterize the ranges of patients enrolled and general follow-up times associated with traditional RCT studies, we conducted a structured search of previously published studies evaluating the effect of robotics and navigation on revision rates compared with that of conventional TKA. The structured search of the University of British Columbia's library database (which automatically searches medical publication databases such as PubMed, Embase, Medline, and Web of Science) and subsequent searching through included studies' reference lists yielded 103 search results. Only clinical studies assessing implant survival differences between patient cohorts of TA-TKA and conventional TKA were included. Studies analyzing registry data, using cadaver specimens, assessing revision TKA, conference proceedings, and preprint services were excluded. Twenty studies met all our inclusion criteria, but only one study reported a statistically significant difference between the conventional and robotic or navigated groups. Next, we generated a large set of patients with simulated TKA (1.5 million), randomly assigning each simulated patient a set of patient-specific factors (age at the index surgery, gender, and BMI) drawn from data from registries and published information. We divided this set of simulated procedures into four groups, each associated with a coronal alignment precision reported for different types of surgical procedures, and randomly assigned each patient an overall coronal alignment consistent with their group's precision. TA procedures were modeled based on the alignment precision that an intervention could deliver, regardless of whether the technology used was navigation- or robot-assisted. To evaluate the power associated with using different cohort sizes, we ran a Monte Carlo simulation generating 3000 simulated populations that were drawn (with replacement) from the large set of simulated patients with TKA. We simulated the time to revision for aseptic loosening for each patient, computed the corresponding Kaplan-Meier survival curves, and applied a log-rank test to each study for statistical differences in revision rates at concurrent follow-up timepoints (1-25 years). From each simulation associated with a given cohort size, we determined the percentage of simulated studies that found a statistically significant difference at each follow-up interval. For each alternative precision, we then also calculated the expected reduction in revision rates (effect size) attributable to TA-TKA intervention and the number needed to treat (NNT) using TA-TKA to prevent one revision at 2, 5, 10, and 15 years after index surgery for the entire set of Kaplan-Meier survival analyses.
The results from our simulation found survivorship differences favoring TA-TKA ranging from 1.4% to 2.0% at 15 years of follow-up. Comparative studies would need to enroll between 2500 and 4000 patients in each arm of the study, depending on the precision of the navigated or robotic procedure, to have an 80% chance of showing this reduction in revision rates at 15 years of follow-up. For the highest precision simulated intervention, the NNT using TA-TKA to prevent one revision was 1000 at 2 years, 334 at 5 years, 100 at 10 years, and 50 at 15 years post-index surgery.
Based on these simulations, it appears that TA-TKA interventions could potentially result in a relative reduction in revision rates as large as 27% (from 7.5% down to about 5.5% at 15 years for the intervention with the most precise coronal alignment); however, since this 2% absolute reduction in revision rates is relatively small in comparison with the baseline success rate of TKA and would not be realized until 15 years after the index surgery, traditional RCT studies would require excessively large numbers of patients to be enrolled and excessively long follow-up times to demonstrate whether such a reduction actually exists.
Given that the NNTs to avoid revisions at various time points are predicted to be high, it would require correspondingly low system costs to justify broad adoption of TA-TKA based on avoided revision costs alone, though we speculate that technology assistance could perhaps prove to be cost effective in the care of patients who are at an elevated risk of revision.</description><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Arthroplasty</subject><subject>Arthroplasty, Replacement, Knee - statistics & numerical data</subject><subject>Bone implants</subject><subject>Clinical Studies as Topic - methods</subject><subject>Clinical trials</subject><subject>Computer Simulation</subject><subject>Female</subject><subject>Humans</subject><subject>Intervention</subject><subject>Joint surgery</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Monte Carlo simulation</subject><subject>Patient Selection</subject><subject>Patients</subject><subject>Registries</subject><subject>Reoperation - statistics & numerical data</subject><subject>Research Design</subject><subject>Robotic surgery</subject><subject>Robotic Surgical Procedures - statistics & numerical data</subject><subject>Robotics</subject><subject>Statistical analysis</subject><subject>Survival</subject><issn>0009-921X</issn><issn>1528-1132</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9ks1uEzEUhUcIREPhEUCW2LCZ4muPZ8YbUBR-WhG1KA0SO8szc5O4OOPW9iTKk_C6OE2pShd4Y53r7xz52jfLXgM9ASqr95OL2eyEPlggqXySjUCwOgfg7Gk2SlWZSwY_j7IXIVwlyQvBnmdHvOACOPBR9vvUbclU-yUSTS7j0O3IWSDniB12JDryCSO2kcy_jckMNyYY15OZjphUN7QxyUDGMXrTDFE3FveemWtcNC1xnpzrjVkmvCNzbFe9s25pMHwkY3Jp1oPV-4C80SEB390WPRn32u6CCS-zZwttA76624-zH18-zyen-fTi69lkPM1bAYzni6ZBqUG2ICrshAYEXSyKtoJGdotS0EpDVWKnsS46xkBjUVNWYlkmY8UlP84-HHKvh2aNXYt99Nqqa2_W2u-U00b9e9KblVq6jaoFlbKsUsC7uwDvbgYMUa1NaNFa3aMbgmJC1OnRGZQJffsIvXKDTw3vqZpLqIHy_1OipjUFQRMlDlTrXQgeF_dXBqr2A6L2A6IeD0jyvXnY773r70QkoDgAW2cj-vDLDulf1Aq1javbPE7rMmeUAUBS-W2J_wFdqMb2</recordid><startdate>20211101</startdate><enddate>20211101</enddate><creator>Hickey, Matthew D.</creator><creator>Anglin, Carolyn</creator><creator>Masri, Bassam</creator><creator>Hodgson, Antony J.</creator><general>Wolters Kluwer</general><general>Lippincott Williams & Wilkins Ovid Technologies</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QP</scope><scope>7T5</scope><scope>H94</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20211101</creationdate><title>How Large a Study Is Needed to Detect TKA Revision Rate Reductions Attributable to Robotic or Navigated Technologies? A Simulation-based Power Analysis</title><author>Hickey, Matthew D. ; Anglin, Carolyn ; Masri, Bassam ; Hodgson, Antony J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5123-fbbe9a19c157ed5a1e1a4f4c71b9df6507a176edae84d221ae48026e66bbe7393</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Arthroplasty</topic><topic>Arthroplasty, Replacement, Knee - statistics & numerical data</topic><topic>Bone implants</topic><topic>Clinical Studies as Topic - methods</topic><topic>Clinical trials</topic><topic>Computer Simulation</topic><topic>Female</topic><topic>Humans</topic><topic>Intervention</topic><topic>Joint surgery</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Monte Carlo simulation</topic><topic>Patient Selection</topic><topic>Patients</topic><topic>Registries</topic><topic>Reoperation - statistics & numerical data</topic><topic>Research Design</topic><topic>Robotic surgery</topic><topic>Robotic Surgical Procedures - statistics & numerical data</topic><topic>Robotics</topic><topic>Statistical analysis</topic><topic>Survival</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hickey, Matthew D.</creatorcontrib><creatorcontrib>Anglin, Carolyn</creatorcontrib><creatorcontrib>Masri, Bassam</creatorcontrib><creatorcontrib>Hodgson, Antony J.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Immunology Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Clinical orthopaedics and related research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hickey, Matthew D.</au><au>Anglin, Carolyn</au><au>Masri, Bassam</au><au>Hodgson, Antony J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>How Large a Study Is Needed to Detect TKA Revision Rate Reductions Attributable to Robotic or Navigated Technologies? A Simulation-based Power Analysis</atitle><jtitle>Clinical orthopaedics and related research</jtitle><addtitle>Clin Orthop Relat Res</addtitle><date>2021-11-01</date><risdate>2021</risdate><volume>479</volume><issue>11</issue><spage>2350</spage><epage>2361</epage><pages>2350-2361</pages><issn>0009-921X</issn><eissn>1528-1132</eissn><abstract>Robotic and navigated TKA procedures have been introduced to improve component placement precision in the hope of improving implant survivorship and other clinical outcomes. Although numerous comparative studies have shown enhanced precision and accuracy in placing components, most comparative studies have not shown that such interventions result in improved implant survival. Given what we know about effect sizes from large arthroplasty registries, large cohort studies, and large randomized controlled trials (RCTs), we wondered how large randomized trials would need to be to detect such small differences, and if the number is very high, what that would tell us about the value of these treatments for preventing revision surgery.
In this simulation study, we asked: Given that survivorship differences between technology-assisted TKA (TA-TKA, which we defined as either navigated or robot-assisted TKA) and conventional TKA are either small or absent based on large arthroplasty registries, large cohort studies, and large RCTs, how large would randomized trials need to be to detect small differences between TA-TKA and conventional TKA if they exist, and how long would the follow-up period need to be to have a reasonable chance to detect those differences?
We used estimated effect sizes drawn from previous clinical and registry studies, combined with estimates of the accuracy and precision of various navigation and robotic systems, to model and simulate the likely outcomes of potential comparative clinical study designs. To characterize the ranges of patients enrolled and general follow-up times associated with traditional RCT studies, we conducted a structured search of previously published studies evaluating the effect of robotics and navigation on revision rates compared with that of conventional TKA. The structured search of the University of British Columbia's library database (which automatically searches medical publication databases such as PubMed, Embase, Medline, and Web of Science) and subsequent searching through included studies' reference lists yielded 103 search results. Only clinical studies assessing implant survival differences between patient cohorts of TA-TKA and conventional TKA were included. Studies analyzing registry data, using cadaver specimens, assessing revision TKA, conference proceedings, and preprint services were excluded. Twenty studies met all our inclusion criteria, but only one study reported a statistically significant difference between the conventional and robotic or navigated groups. Next, we generated a large set of patients with simulated TKA (1.5 million), randomly assigning each simulated patient a set of patient-specific factors (age at the index surgery, gender, and BMI) drawn from data from registries and published information. We divided this set of simulated procedures into four groups, each associated with a coronal alignment precision reported for different types of surgical procedures, and randomly assigned each patient an overall coronal alignment consistent with their group's precision. TA procedures were modeled based on the alignment precision that an intervention could deliver, regardless of whether the technology used was navigation- or robot-assisted. To evaluate the power associated with using different cohort sizes, we ran a Monte Carlo simulation generating 3000 simulated populations that were drawn (with replacement) from the large set of simulated patients with TKA. We simulated the time to revision for aseptic loosening for each patient, computed the corresponding Kaplan-Meier survival curves, and applied a log-rank test to each study for statistical differences in revision rates at concurrent follow-up timepoints (1-25 years). From each simulation associated with a given cohort size, we determined the percentage of simulated studies that found a statistically significant difference at each follow-up interval. For each alternative precision, we then also calculated the expected reduction in revision rates (effect size) attributable to TA-TKA intervention and the number needed to treat (NNT) using TA-TKA to prevent one revision at 2, 5, 10, and 15 years after index surgery for the entire set of Kaplan-Meier survival analyses.
The results from our simulation found survivorship differences favoring TA-TKA ranging from 1.4% to 2.0% at 15 years of follow-up. Comparative studies would need to enroll between 2500 and 4000 patients in each arm of the study, depending on the precision of the navigated or robotic procedure, to have an 80% chance of showing this reduction in revision rates at 15 years of follow-up. For the highest precision simulated intervention, the NNT using TA-TKA to prevent one revision was 1000 at 2 years, 334 at 5 years, 100 at 10 years, and 50 at 15 years post-index surgery.
Based on these simulations, it appears that TA-TKA interventions could potentially result in a relative reduction in revision rates as large as 27% (from 7.5% down to about 5.5% at 15 years for the intervention with the most precise coronal alignment); however, since this 2% absolute reduction in revision rates is relatively small in comparison with the baseline success rate of TKA and would not be realized until 15 years after the index surgery, traditional RCT studies would require excessively large numbers of patients to be enrolled and excessively long follow-up times to demonstrate whether such a reduction actually exists.
Given that the NNTs to avoid revisions at various time points are predicted to be high, it would require correspondingly low system costs to justify broad adoption of TA-TKA based on avoided revision costs alone, though we speculate that technology assistance could perhaps prove to be cost effective in the care of patients who are at an elevated risk of revision.</abstract><cop>United States</cop><pub>Wolters Kluwer</pub><pmid>34351313</pmid><doi>10.1097/CORR.0000000000001909</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Aged Aged, 80 and over Arthroplasty Arthroplasty, Replacement, Knee - statistics & numerical data Bone implants Clinical Studies as Topic - methods Clinical trials Computer Simulation Female Humans Intervention Joint surgery Male Middle Aged Monte Carlo simulation Patient Selection Patients Registries Reoperation - statistics & numerical data Research Design Robotic surgery Robotic Surgical Procedures - statistics & numerical data Robotics Statistical analysis Survival |
| title | How Large a Study Is Needed to Detect TKA Revision Rate Reductions Attributable to Robotic or Navigated Technologies? A Simulation-based Power Analysis |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T04%3A46%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=How%20Large%20a%20Study%20Is%20Needed%20to%20Detect%20TKA%20Revision%20Rate%20Reductions%20Attributable%20to%20Robotic%20or%20Navigated%20Technologies?%20A%20Simulation-based%20Power%20Analysis&rft.jtitle=Clinical%20orthopaedics%20and%20related%20research&rft.au=Hickey,%20Matthew%20D.&rft.date=2021-11-01&rft.volume=479&rft.issue=11&rft.spage=2350&rft.epage=2361&rft.pages=2350-2361&rft.issn=0009-921X&rft.eissn=1528-1132&rft_id=info:doi/10.1097/CORR.0000000000001909&rft_dat=%3Cproquest_pubme%3E2558452216%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2558080150&rft_id=info:pmid/34351313&rfr_iscdi=true |