|
|
Journal of Biostatistics and Epidemiology، جلد ۳، شماره ۱، صفحات ۷-۱۲
|
|
|
| عنوان فارسی |
|
|
| چکیده فارسی مقاله |
|
|
| کلیدواژه‌های فارسی مقاله |
|
|
| عنوان انگلیسی |
Multilevel modeling of clustered grouped survival data of dental implant failures; a Bayesian approach |
|
| چکیده انگلیسی مقاله |
Background & Aim: In clinical dental studies, each participant has usually several visits, and since the review and ongoing monitoring of the subjects are often expensive or even impossible, so people are examined periodically during regularly pre-scheduled visits. Therefore, discrete or grouped clustered failure time data are collected. We aimed to show the use of Monte Carlo Markov Chain (MCMC) and the non-informative prior in a Bayesian framework in multilevel modeling of clustered grouped survival data. Methods & Materials: A two-level model with additive variance components model for the random effects was considered. Both the grouped proportional hazards model and logistic regression with logit link function model were used. Using grouped proportional hazards method, we could approximate intracluster correlation of the log failure times. The statistical package OpenBUGS was adopted to estimate the parameter of interest based on the MCMC method. A cohort study was used in which 1011 persons visited at clinic dentistry of Tehran University of Medical Sciences, Iran, between the years 2002 and 2013 for dental implant and 2368 implants were placed for them in total. Clinical status of dental implants was evaluated in three periods after placement, thus clustered grouped failure times of the dental implants were recorded. Results: The grouped proportional hazards model showed that clustering effect among the log failure times of the different implants from the same person was fairly strong (correlation = 0.99). Complication and biomaterial variables had no effect on the implant failure, and there was no difference in the failure times related to the molar, premolar, canine, primary, and incisor since 95% credible interval (CI) included 0. The CI related to the gender and place of teeth not including 0, so these variables were significant in the model. The estimates of the baseline parameters (γ1, γ2, and γ3) were increasing indicating increasing hazard rates from interval 1-3. Results of logistic regression were similar to grouped proportional hazards model with wider confidence intervals. Conclusion: The use of MCMC approach and non-informing prior in Bayesian framework to mimic maximum likelihood estimations in a frequentist approach in multilevel modeling of clustered grouped survival data can be easily applied with the use of the software OpenBUGS |
|
| کلیدواژه‌های انگلیسی مقاله |
|
|
| نویسندگان مقاله |
عباس رحیمی فروشانی | abbas rahimiforoushani
department of epidemiology and biostatistics, school of public health, tehran university of medical sciences, tehran, iran
سازمان اصلی تایید شده: دانشگاه علوم پزشکی تهران (Tehran university of medical sciences)
نوشین اکبری شارک | nooshin akbari sharak
department of epidemiology and biostatistics, school of public health, tehran university of medical sciences, tehran, iran
سازمان اصلی تایید شده: دانشگاه علوم پزشکی تهران (Tehran university of medical sciences)
محمدجواد خرازی فرد | mohammadjavad kharazi fard
|
|
| نشانی اینترنتی |
http://jbe.tums.ac.ir/index.php/jbe/article/view/134 |
| فایل مقاله |
اشکال در دسترسی به فایل - ./files/site1/rds_journals/362/article-362-478101.pdf |
| کد مقاله (doi) |
|
| زبان مقاله منتشر شده |
en |
| موضوعات مقاله منتشر شده |
|
| نوع مقاله منتشر شده |
Original Article(s) |
|
|
|
برگشت به:
صفحه اول پایگاه |
نسخه مرتبط |
نشریه مرتبط |
فهرست نشریات
|