Kaplan-Meier Analysis of Dental Implant Survival: A Strategy for Estimating Survival with Clustered Observations

Abstract

The study's purposes were to estimate dental implant survival in a statistically valid manner and to compare three models for estimating survival. We estimated survival using three different statistical models: (1) randomly selecting one implant per patient; (2) utilizing all implants, assuming independence among implants from the same subject; and (3) utilizing all implants, assuming dependence among implants from the same subject. The cohort was composed of 660 patients who had 2286 implants placed. Due to the high success rates of implants, the five-year survival point and standard error estimates varied little among the three models. Patients at high risk for implant failure (smokers) manifested greater variation in the standard error estimates among the three models, 8.2%, 4.0%, and 5.6%, respectively. To obtain statistically valid survival confidence intervals when performing Kaplan-Meier survival analyses, we recommend adjusting for dependence when there are multiple observations within the same subject.

Higher surgeon and hospital volume improves long‐term survival after radical cystectomy

“…23 However, these analyses did not account for clustering of data or adjuvant chemotherapy in a time-dependent manner, both of which may contribute to false-positive findings. 17,18,20 Finally, Birkmeyer et al assessed the association between hospital volume and late survival in 2514 patients who underwent cystectomy using Surveillance, Epidemiology, and End Results (SEER)-Medicare linked data but failed to demonstrate a significant association in adjusted analyses. 24 However, their HR of 0.90 suggested a 10% risk reduction associated with high-volume hospitals, which is similar to the findings of the current study.…”

“…We used marginal Cox proportional hazards models with robust variance estimation to account for the lack of independence due to the clustering of patients by either hospitals or surgeons. 17,18 In all analyses, volume was modeled as a continuous variable. Multicollinearity, defined as a variance inflation factor (VIF) of > 10, was determined for all variables to ensure collinearity did not affect the regression models.…”

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“…It is pertinent to mention that simply designing a perfect implant does not diminish the need to examine the cause(s) of implant failure, although science has yet to designate an exact reason for rejection [77]. However, consensus is when establishment and/or maintenance of osseointegration is jeopardized or impaired, at early stages of bone healing, implant mobility is rendered as the epitome of unsuccessful implant surgery [78][79][80].…”

Recurrence of inguinal hernia in general and hernia specialty hospitals in Ontario, Canada

“…Failure to account for this clustering at all levels of our analyses may have resulted in Type 1 error by decreasing the standard errors around our model parameter estimates. This would also have decreased the corresponding P-values (86,87).…”