J Dent Res 83(Spec Iss C):C116-C118, 2004
© 2004 International and American Associations for Dental Research
The Role of Risk Factors in the Identification of Appropriate Subjects for Caries Clinical Trials: Design Considerations
M.F. Johnson
PharmaNet, Inc., 504 Carnegie Center, Princeton, NJ 08540; mjohnson{at}pharmanet.com
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ABSTRACT
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In seeking new and more effective therapies to delay or prevent caries development, investigators must design clinical trials focused on high-risk populations with a predictable incidence of caries over a limited period of time. In children and adolescents, the strongest predictors of caries incidence appear to be baseline levels of caries activity (present caries, e.g., dmfs, DMFT, caries lesions in first molars). Other predictors of caries risk typically include oral hygiene level, counts of cariogenic micro-organisms in plaque and saliva, fluoride history, sucrose intake, and parent’s socio-economic level. This paper will briefly review existing literature to address the most useful and relevant prognostic factors for predicting future caries onset. The relative merits of identifying high-risk subjects based on these factors, either singly or in combination, will be explored in terms of statistical efficiency. Particular attention will focus on the advantages of covariate adjustment in the context of survival-based methods for the analysis of caries data. Further, with the advent of more sophisticated diagnostic procedures (e.g., quantitative light fluorescence) to screen and monitor study subjects for caries activity, there is the potential for earlier states of lesion initiation and progression (or regression) to be detected, with, therefore, improved experimental sensitivity to treatment effects. The validity of risk assessment and outcome measurement on the basis of these new diagnostic tools vs. more conventional methods will be examined.
KEY WORDS: risk factors • caries clinical trials
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INTRODUCTION
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The paper summarizes the oral presentation made to the International Consensus Workshop on Caries Clinical Trials.
The term ‘risk factor’ will be used here to refer to any aspect or baseline characteristic of the study population that affects the likelihood of observing the clinical event of interest, in this case, dental caries. Baseline characteristics in general, and risk factors in particular, play a major role in identifying appropriate subjects for caries clinical trials. From a statistical standpoint, proper adjustment for risk factors (baseline variables that have prognostic value) will improve the efficiency of significance tests and also improve the validity (reduce bias) of estimated treatment effects. But the topic has a much broader meaning when we consider the modern environment, the low prevalence of dental caries in the industrial world, and the difficulty of setting up studies in what some may consider a ‘rare disease’ setting.
This paper will begin with a brief review of caries studies published over the last few decades to identify, from an historical perspective, population characteristics that were considered important potential risk factors, i.e., variables that changed the probability of a caries event. Types of analysis will also be discussed that take advantage of risk factors as blocking factors to improve the efficiency of statistical tests. Attention will focus on the relative merits of identifying ‘high-risk’ subjects for inclusion in clinical trials and the impact this will have on study designs in the future. In this context, some of the newer diagnostic methods will be considered for use in screening and monitoring study subjects for caries activity. These methods have the potential to detect earlier states of lesion initiation and progression, and this paper will examine the validity of assessing risk and measuring outcome on the basis of these methods vs. more conventional methods. These concepts will lead to some general recommendations about study design vis-à-vis the selection of high-risk subjects.
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CHALLENGES IN THE DESIGN OF CARIES TRIALS
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The nature of clinical trials in any therapeutic area must evolve to keep up with changes in diagnostic tools, advances in treatment options, and variations in disease prevalence or etiology. Studies of dental caries are no exception. We have seen this in many other therapeutic areas, particularly studies in cardiovascular and infectious diseases, where a vast array of marketed products detracts from our ability to recruit a representative cross-section of patients, or where we are facing a different etiology of disease, highly resistant to existing therapies.
Studies in dental caries face a similar challenge. The design of clinical trials to find new products for the treatment and prevention of dental caries must adapt to the changing environment. Traditional methods used to diagnose caries lesions during the high-prevalence caries era will no longer be the sole basis for caries detection. With the increased presence of fluoride and improved oral hygiene (especially in developed countries), caries lesions now progress much more slowly and cavitation occurs much later compared with the early high-prevalence phase of anti-caries product development. Likewise, we face trouble with the placebo-controlled design, now that the standard of care includes the use of a medicated dentifrice. In view of these changes, we may need to re-define what we mean by ‘high-risk’ subjects and the manner in which we identify them for clinical studies.
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THE ROLE OF RISK FACTORS
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Risk factors play an important role in the design and interpretation of clinical trials. Again, from a statistical perspective, it is important to account for the influence of baseline characteristics and disease severity in the analysis of clinical data, either through least-squares covariate adjustment or through stratification. Depending on the correlation between baseline factors and the response (typically measured as either caries increments, incidence densities to account for time at risk, or time to caries progression, however defined), appropriate adjustment for covariate effects will typically improve the efficiency of statistical tests and produce more valid (i.e., unbiased) estimates of treatment efficacy.
In addition, the baseline data allow for tests of treatment by covariate interaction and thus address the consistency of treatment effects across relevant population subgroups. In the presence of interaction, we can examine treatment effects within relevant baseline strata to determine if efficacy differs qualitatively or quantitatively for specific high- or low-risk subgroups. Risk factors also have value for assessing the impact of loss to follow-up on the analysis of caries increments and will often suggest the need for more sophisticated longitudinal data analysis to account properly for variation in the at-risk period (Beck et al., 1997). For instance, it has been shown that, in the elderly (more so than in children), subjects who fail to return for final dental examinations are materially different from more compliant subjects who return and have complete follow-up information, in terms of both overall health condition and oral health status. Demographic information, baseline caries status, and medical history are all critical data for examination of the impact of subject attrition on estimates of treatment outcome. Finally, these sorts of baseline measurements can be explored as prognostic factors. If valuable as predictors of caries onset, the measurements could well serve as selection criteria in future caries trials.
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BENEFITS OF STRATIFICATION
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As mentioned, blocking or stratification on important risk factors will typically improve the efficiency of statistical tests. Relative efficiency may be estimated by the ratio of variance estimators, i.e.,
where the numerator (sest2) is the pooled variance of the response variable across treatments estimated without stratification or blocking factors included in the model, and the denominator (s2) is the pooled variance estimated with blocking factors included in the model. In a randomized blocks design, the higher the correlation between responses within blocks, the greater the gain in efficiency. As Fleiss (1986) has noted, RE should be at least 125–130% to make the effort of blocking or stratification worthwhile.
Several population characteristics and study design features have influenced clinical outcome in studies of fluoride dentifrices over the past few decades (Stookey et al., 1993). Table 1
provides a list of these factors. Many of these are known risk factors that have served as effective stratification variables in previous trials, most notably age. Caries studies typically focus on children, ranging in age anywhere from 6 to 16 yrs, but age-related factors (such as product usage habits, ability to follow instructions, caries present in erupted teeth, oral hygiene, etc.) are the underlying risks affecting trial results. Likewise, gender differences and socio-economic level have a bearing on caries risk (boys typically have higher rates than girls, as do children in public vs. private school). Other variables affecting trial outcome are subject exclusion criteria (e.g., confounding medical problems or orthodontic appliances), drop-out rates (often a source of bias when attrition is high and differs between groups), and, most importantly, the caries prevalence/incidence in the population. This last point is critical, because experimental sensitivity depends on the level of measurable disease. Latin America is a good site for caries study today, since the caries prevalence there has reached a level similar to that in the US in the mid-1960s. On the other hand, US caries prevalence has dropped over three-fold since that time, just as it has in other Westernized countries. Other design factors that affect study outcome include: length of study, fluoride exposure, brushing compliance, pre-stratification criteria (age, gender, and baseline DMFS scores are essential for balanced designs), examination methods, and statistical power/sample size considerations.
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NEW DEVELOPMENTS IN RISK ASSESSMENT
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Recent dental research in the area of risk assessment is focusing on the evaluation of new, technologically advanced methods for the diagnosis of caries, perhaps in the hope that their improved sensitivity and specificity (compared with those of conventional methods) will increase the chance of detecting small treatment effects or discriminate between competing treatment modalities, even in low-risk populations. These methods may also help to identify caries development sooner, long before caries is clinically evident. Ultimately, we want to develop treatments that will delay or suppress caries development, allowing newer diagnostic methods to provide an earlier signal or marker of impending caries development. The use of survival or time-to-event methodologies is clearly relevant in this area, including the work of Hannigan et al.(2000), exploiting the use of the log-logistic model for clustered survival data, and that of Hujoel and co-workers (1994), applying the Poisson regression model to caries incidence. Cox proportional hazards regression analysis also makes sense to explore caries risk factors, as recently used to determine the relationship between salivary mutans streptococci (MS) counts and caries incidence in Japanese preschoolers (Ansai et al., 2000). Each of these models allows for the inclusion of subject- and surface-specific explanatory variables to identify risk factors that affect caries development. Risk factors identified through the use of survival methods can guide the selection of appropriate high-risk subpopulations for future study—in this case, the subset of tooth surfaces and subjects likely to benefit from preventive therapies.
For example, a recent three-year study in older adults (age 60+) used the Poisson regression model to show that the risk of coronal caries was increased in subjects with high baseline root DMFS, high counts of mutans streptococci and lactobacilli, male gender, and Asian ethnicity (Powell et al., 1998). Relative risks ranged from 1.2 to 2 for these factors, indicating only moderate effects on incidence. But knowledge of these relationships will strengthen the analysis of treatment effects or even play a role in subject selection for caries trials. Similar factors were associated with an increased risk of root-surface caries (baseline coronal DMFS, high bacterial counts, and Asian ethnicity). Again, the study confirms the value of baseline DMFS and salivary variables, along with ethnicity, as a useful design aspect for study of dental caries in the elderly. As in other therapeutic areas, we will have to change the notion that caries prevention needs to be based on large population-based cohort trials in favor of more focused trials in targeted sub-populations of high-risk subjects.
As for alternative diagnostic methods, they will be useful only if they are truly valid measures of caries risk and offer a reliable means to select high-risk subjects and/or identify caries earlier than traditional methods. Diagnostic techniques that have been explored for their ability to improve the quality of caries prediction, in addition to visual inspection, include radiography, fiber optic transillumination (FOTI), electrical resistance measurements (ERM), and quantitative laser/light-induced fluorescence (QLF). The question remains, Will the use of these techniques to monitor caries progression alter the way we select patients for study or somehow change the risk profile of patients identified for caries studies? The literature is not clear on this.
Table 2 summarizes the range of published correlations between each diagnostic test outcome and actual lesion depth, as validated by histology or measures of mineral loss in studies published since 1992 (Verdonschot et al., 1999). For approximal surfaces, FOTI had the highest correlation with lesion depth (r values of 0.87 and 0.92). For occlusal surfaces, visual inspection had a variable range of correlations with histology, depending on the scoring system used, whereas ERM performed quite well, with the correlation of 0.82. Diagnosis of smooth-surface caries was best for QLF (r = 0.78–0.86).
In general, the new quantitative methods (FOTI, ERM, QLF) show high correlation with lesion depth and therefore would be quite suitable for monitoring small changes in lesions over time. Of course, their use in clinical trials would not only affect the outcome measures (e.g., time to progression instead of DMFT increments) but could also affect the risk assessment models. That is, the strongest predictors of caries incidence, such as present caries activity (typically measured as baseline dmfs, DMFT, caries lesions in first molars), may have little bearing on the risk of caries progression defined by the more high-tech procedures. Weak factors for caries prediction (e.g., cariogenic micro-organisms in plaque and saliva, saliva flow, and plaque tests) may perform better as predictors of more sensitive outcome measures. For example, the signs of caries progression determined through the use of the quantitative laser-induced fluorescence (QLF) procedure may have baseline ‘risk’ indicators somewhat different from those predicting increments in clinically assessed DMFS as the outcome for analysis. So, as in other therapeutic areas, the challenge will be to validate these methods as potential ‘surrogate markers’ of caries development. This will involve further exploration of their sensitivity and specificity, as well as investigation into risk factors that may influence their accuracy and predictive value for caries onset.
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RECOMMENDATIONS
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Studies evaluating products that delay or prevent caries development will be most sensitive to treatment effects if they are conducted in populations that are ‘enriched’ with high-risk subjects. Using conventional methods to quantify caries increments, such studies should be conducted in areas or countries with high caries incidence (e.g., Latin America, Central/Eastern European countries, Japan). This would typically include regions with limited fluoride exposure and poor oral hygiene, or in special populations of subjects selected for their pre-disposition to caries development. Consideration should be given to the use of new diagnostic methods in the screening, stratification, and monitoring process, provided that these methods are sufficiently validated, i.e., that they have high sensitivity and specificity for caries detection. Future studies are likely to require baseline DMFT or DMFS as covariates or stratification variables. But, in addition, the newer, more sophisticated diagnostic test results may reveal additional criteria for selecting and categorizing subjects at high risk for caries progression. The definition of such ‘risk thresholds’ requires validation against known outcomes, and this process of risk assessment will take further research. Finally, to target the high-risk groups for future study and prevention programs, we should continue to explore the use of statistical models that allow us to forecast risk-groups with high caries activity. This will include use of more powerful multivariate regression models to adjust for prognostic factors while evaluating changes in caries status over time.
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FOOTNOTES
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Presented at the International Consensus Workshop on Caries Clinical Trials, Glasgow, Scotland, January 7–10, 2002
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REFERENCES
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Ansai T, Tahara A, Ikeda M, Katoh Y, Miyazaki H, Takehara T (2000). Caries risk in Japanese pre-school children. Pediatric Dent 22:377–380.
Beck JD, Lawrence HP, Koch GG (1997). Analytic approaches to longitudinal caries data in adults. Community Dent Oral Epidemiol 25:42–51.[ISI][Medline]
Fleiss JL (1986). The design and analysis of clinical experiments. New York: J. Wiley & Sons.
Hannigan A, O’Mullane DM, Barry D, Schafer F, Roberts AJ (2000). A caries susceptibility classification of tooth surfaces by survival time. Caries Res 34:103–108.[ISI][Medline]
Hujoel PP, Isokangas PJ, Tiekso J, Davis S, Lamont RJ, DeRouen TA, et al. (1994). A re-analysis of caries rates in a preventive trial using Poisson regression models. J Dent Res 73:573–579.[Abstract/Free Full Text]
Powell LV, Leroux BG, Persson RE, Kiyak HA (1998). Caries risk in the elderly. Community Dent Oral Epidemiol 26:170–176.[ISI][Medline]
Stookey GK, DePaola PF, Featherstone JDB, Fejerskov O, Moller IJ, Rotberg S, et al. (1993). A critical review of the relative anticaries efficacy of sodium fluoride and sodium monofluorophosphate dentifrices. Caries Res 27:337–360.[ISI][Medline]
Verdonschot EH, Angmar-Månsson B, ten Bosch JJ, Deery CH, Huysmans MCD, Pitts NB, et al. (1999). Developments in caries diagnosis and their relationship to treatment decisions and quality of care. Caries Res 33:32–40.[ISI][Medline]
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C6 - C14.
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ABSTRACT
INTRODUCTION
