HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Services Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (30) Citing Articles via Google Scholar Google Scholar Articles by Chuang, S.K. Articles by Dodson, T.B. Search for Related Content PubMed PubMed Citation Articles by Chuang, S.K. Articles by Dodson, T.B.

Risk Factors for Dental Implant Failure: A Strategy for the Analysis of Clustered Failure-time Observations

¹ Department of Oral Health Policy and Epidemiology, Harvard School of Dental Medicine, 188 Longwood Avenue, Boston, MA 02115;
² Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115; and
³ Department of Oral Health Policy and Epidemiology, Harvard School of Dental Medicine, 188 Longwood Avenue, Boston, MA 02115;
⁴ Department of Oral and Maxillofacial Surgery, Harvard School of Dental Medicine and Massachusetts General Hospital, 55 Fruit Street, Warren 1201, Boston, MA 02114;

* corresponding author, PO Box 67376, Chestnut Hill Station, Chestnut Hill, MA 02467, schuang{at}hsph.harvard.edu

This study’s objective was to identify, in a statistically valid and efficient manner, the risk factors associated with dental implant failure. We hypothesize that factors exist which can be modified by clinicians to enhance outcome. A retrospective cohort study design was used. Cohort members had one implant placed. Risk factors were classified as demographic, health status, implant-, anatomic-, or prosthetic-specific, and reconstructive variables. The outcome variable was implant failure. The cohort was composed of 677 patients who had 2349 implants placed. Based on the adjusted multivariate model, factors associated with implant failure were tobacco use, implant length, staging, well size, and immediate implants (p 0.05). In the setting of correlated survival observations, we recommend adjusting for the correlation of the observations to provide statistically valid and efficient results. Three of the identified factors—tobacco use, immediate implants, and implant staging—potentially may be modified to enhance implant survival.

KEY WORDS: survival analysis • dental implants • risk factors • follow-up study • correlation and dependence • Cox regression analysis • clustered survival data • marginal approach

This article has been cited by other articles:

				S.-K. Chuang and T. Cai Predicting Clustered Dental Implant Survival Using Frailty Methods. J. Dent. Res., December 1, 2006; 85(12): 1147 - 1151. [Abstract] [Full Text] [PDF]

				M.C.M. Wong, K.F. Lam, and E.C.M. Lo Bayesian Analysis of Clustered Interval-censored Data J. Dent. Res., September 1, 2005; 84(9): 817 - 821. [Abstract] [Full Text] [PDF]

				M. M. Walsh and J. A. Ellison Treatment of Tobacco Use and Dependence: The Role of the Dental Professional J Dent Educ., May 1, 2005; 69(5): 521 - 537. [Abstract] [Full Text] [PDF]

				S.K. Chuang, T. Cai, C.W. Douglass, L.J. Wei, and T.B. Dodson Frailty Approach for the Analysis of Clustered Failure Time Observations in Dental Research J. Dent. Res., January 1, 2005; 84(1): 54 - 58. [Abstract] [Full Text] [PDF]

				S.K. Chuang, L. Tian, L.J. Wei, and T.B. Dodson Predicting Dental Implant Survival by Use of the Marginal Approach of the Semi-parametric Survival Methods for Clustered Observations J. Dent. Res., December 1, 2002; 81(12): 851 - 855. [Abstract] [Full Text] [PDF]