Caries risk analysis: considering caries as an individual time‐dependent process

Kalwitzki, Matthias; Weiger, Roland; Axmann-Krcmar, D.; Rosendahl, R

doi:10.1046/j.1365-263x.2002.00342.x

Cited by 4 publications

(5 citation statements)

References 22 publications

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“…A consequence of discrepancy in timing of both tooth emergence and exfoliation [28,29], is that the tooth‐specific times of being at risk differed between individuals. Undoubtedly, omission of available time‐to‐event statistical methods dealing with caries as an individual time‐dependent process [30] is a study weakness and should be taken into account. However in this study, this factor was partly accounted for by including in analyses only those tooth groups with all teeth erupted at the age of 10.…”

Section: Discussionmentioning

confidence: 99%

The relationship between caries in the primary dentition at 5 years of age and permanent dentition at 10 years of age – a longitudinal study

Skeie

Raadal

Strand

et al. 2006

Int J Paed Dentistry

200

133

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Summary. Objectives. To explore a possible relationship between the caries experience and pattern in the primary dentition at 5 years of age and the permanent dentition at 10 years of age. Further, to examine the possibility of predicting children in a caries-risk group at 5 years verified at 10 years of age. Materials and methods. A sample of 186 children (90 males) were clinically examined as 5-year-olds and re-examined as 10-year-olds by calibrated dentists. A five-graded diagnostic system including enamel caries was used. Bitewing radiographs were taken. A true risk group of children at 10 years were defined as those with at least one dentin or filled lesion on the mesial surface of 6-year molars, and/or on incisors, and/or total DMFS (decayed, missing, and filled surfaces) more than 1 SD above the mean. The prediction was measured in terms of OR (odds ratio), sensitivity/specificity, and receiver operating characteristic curves. Results. Statistically significant correlations ( r = 0·5) were found between the caries experience in the two dentitions as well as between the primary second molars at baseline and the permanent teeth at 10 years. 'Primary second molars' and 'all primary molars' were the most powerful predictors for allocation into the risk group (24% of the sample). The highest achieved sum of sensitivity and specificity, 148%, was attained at a cut-off point above two carious surfaces in enamel and/or dentin in primary second molars. Conclusions. Statistically significant relationship in disease between the dentitions was found. More than two surfaces with caries experience in primary second molars are suggested as a clinically useful predictor at 5 years of age for being at high risk at age 10.

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Section: Discussionmentioning

confidence: 99%

The relationship between caries in the primary dentition at 5 years of age and permanent dentition at 10 years of age – a longitudinal study

Skeie

Raadal

Strand

et al. 2006

Int J Paed Dentistry

200

133

View full text Add to dashboard Cite

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“…Applications of theoretical survival methods for estimating and predicting risk have been a major challenge in dental research for decades (Kalwitzki et al, 2002). Datasets from patient-oriented dental This study's primary specific aim was to apply the clustered frailty multivariate model to evaluate heterogeneity for implant failure among subjects.…”

Section: Discussionmentioning

confidence: 99%

“…Excellent discussions on this model can be found in Hougaard (1995Hougaard ( , 2000 and Therneau and Grambsch (2000). Application of this method in patientoriented dental research to date has been sparse (Kalwitzki et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Frailty Approach for the Analysis of Clustered Failure Time Observations in Dental Research

et al. 2005

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Because dental implant failure patterns tend to cluster within subjects, we hypothesized that the risk of implant failure varies among subjects. To address this hypothesis in the setting of clustered, correlated observations, we considered a retrospective cohort study where we identified a cohort having at least one implant placed. The cohort was composed of 677 patients who had 2349 implants placed. To test the hypothesis, we applied an innovative analytic method, i.e., the Cox proportional hazards model with frailty, to account for correlation within subjects and the heterogeneity of risk, i.e., frailty, among subjects for implant failure. Consistent with our hypothesis, risk for implant failure among subjects varied to a statistically significantly degree (p=0.041). In addition, the risk for implant failure is significantly associated with several factors, including tobacco use, implant length, immediate implant placement, staging, well size, and proximity of adjacent implants or teeth.

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“…Caries risk is defined as the probability of an individual developing at least a certain number of caries lesions during specific period of time (1). Risk assessment is important step in decision-making and treatment planning.…”

Section: Introductionmentioning

confidence: 99%

Caries risk assessment models in caries prediction

Zukanović

2013

AMA

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Caries risk analysis: considering caries as an individual time‐dependent process

Cited by 4 publications

References 22 publications

The relationship between caries in the primary dentition at 5 years of age and permanent dentition at 10 years of age – a longitudinal study

The relationship between caries in the primary dentition at 5 years of age and permanent dentition at 10 years of age – a longitudinal study

Frailty Approach for the Analysis of Clustered Failure Time Observations in Dental Research

Caries risk assessment models in caries prediction

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