Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT03916926 |
| Other study ID # |
AD-2018-C1-10590 |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
September 18, 2019 |
| Est. completion date |
June 30, 2025 |
Study information
| Verified date |
May 2024 |
| Source |
Case Western Reserve University |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
The study is a cluster randomized clinical trial (RCT) to be conducted in 22 publicly
subsidized housing facilities/sites (HUD Section 202) and other low-income housing voucher
programs in NE Ohio. The facilities will be randomized to 2 arms: Arm 1 (11 sites) -
Participants will receive biannual silver diamine fluoride (SDF) versus Arm 2 (11 sites):
Participants will receive atraumatic restorative treatment (ART) with glass ionomer cement
(GIC) + biannual fluoride varnish (FV) application. A total of 550 participants (Arm 1: 275,
Arm 2: 275) will be followed for one year (baseline, 26 weeks, 52 weeks).
The protocol for each arm will address both coronal and root surface tooth decay lesions: Arm
1: The treatment will be bi-annual topical 38% SDF(Advantage Arrest, Elevate Oral Care LLC.,
West Palm Beach, FL) following manufacturer's instructions and published guidelines; Arm 2:
The treatment will be ART will be a modification where the cavity will be restored with high
viscosity glass-ionomer cement (GIC) (GC Fuji Automix LC Resin Reinforced Glass Ionomer
Restorative, Japan)). Patients will also receive biannual topical fluoride varnish treatments
(FluoriMax, Elevate) according to manufacturer's instructions.
Description:
Study Design:
A cluster randomized clinical trial (RCT) is planned in 22 publicly subsidized housing
facilities (HUD Section 202) and other low-income housing voucher programs in NE Ohio and
will be randomized to 2 arms: Arm 1 (11 sites) - Participants will receive biannual SDF; Arm
2 (11 sites): Participants will receive ART + FV biannual application. All participants will
be followed for one year and will receive a dental screening and prophylactic cleaning at all
visits.
Study Population and Setting:
The long-term goal is to reduce disparities and improve oral health equity for low-income
older adults. The immediate goal is to test interventions to address disparities. Therefore,
the population/setting will be low socioeconomic status (SES) adults ≥ 62 years of age in 6
NE Ohio counties (Cuyahoga, Lorain, Summit, Ashtabula, Geauga, and Lake) residing in HUD
Section 202 and other publicly subsidized housing facilities. Administrators have provided
letters of support confirming the availability of sites and intent to participate. The
population is diverse: African American/Black, Caucasian/White including Hispanic/Latino, and
Asian. There are 64 HUD housing sites with 3,661 tenant units in the 6 counties. An
additional 87 low-income facilities housing elderly are accounted for by regional housing
authorities, adding 11,667 units. Therefore, a total of 151 facilities and 15,328 older
adults are available.
The sample size goal of 22 sites and 550 participants will be easily achieved. Medicaid
participation among the tenants is 76% among facilities. From a prior study in these
facilities it is expected that the mean age is 74 years, 76% female, 51% Caucasian, 45%
African-American, 2% Hispanic, and 2% other racial/ethnic groups; 60% of HUD older adults had
none/rare dental visits in over 3 years.
Due to COVID-19 restrictions which prevented outside visitors/providers at the housing sites,
recruitment will take longer than the anticipated 24 months. Additionally, approximately 40
enrolled individuals did not complete the 26 week visit/second dose of treatment. We
therefore increased the total number of enrolled participants by 40. We also added four
additional housing facilities to the study to address some delays in recruitment due to the
COVID-19 pandemic.
The additional 4 sites will also be randomized to Arm 1 and Arm 2 for a total of 13 sites in
each arm
Recruitment of Participants:
Successful strategies from prior studies will be used in recruiting and input will be
solicited from the stakeholders. Strategies that will be used are as follows: Service
coordinators at all 22 facilities will serve as site liaisons. The study PI/project manager
will provide the service coordinators with an introductory letter/flyer containing study
information to be given to tenants and to be posted in public areas of the facility. Service
coordinators will first arrange an informational meeting (study dentist will give a talk on
the interventions as suggested by our stakeholders), and will arrange a second recruitment
meeting for study staff to present information regarding the study at scheduled group events
(e.g. tenant meetings, health fairs). For planning purposes, service coordinators will have a
sign-up sheet for those interested in the sessions. Study staff will schedule those who are
interested and meet the inclusion criteria for one-on-one sessions at the housing facility to
obtain informed consent and collect baseline survey data. Baseline dental exam and treatment
appointments will then be scheduled at each facility according to a designated exam day(s)
for each facility, which will occur approximately 1-2 weeks following consent and baseline
data collection. Six and twelve month dental exams/treatment and one-on-one interviews for
follow-up survey completion for each time point will also occur at the housing facility where
participants reside.
Randomization Procedures:
Randomization is at the level of the cluster (housing facility) for logistical efficiency.
This will greatly reduce the potential for error that could otherwise occur with people at
the same site assigned to different treatments. Furthermore, keeping the same treatment at
each site reduces chances of 'contamination' (i.e. participant discussing their treatment
with others). Additionally, stratified cluster randomization will be used, i.e. a block
(constrained) randomization approach in which balance over treatments is assured for 2 key
cluster-level (stratification) variables, namely facility size (>100 versus ≤100 residents),
and geographic location (Cuyahoga County vs other). The project statistician will generate
the randomization scheme for the 22 housing facilities.
Analytic plan:
Each primary outcome will be compared between the SDF and ART+FV groups. For tooth pain, a
95% confidence interval (CI) based on a t-test for the difference in mean responses (SDF
minus ART+FV) will be computed. If this confidence interval lies within the interval (-∞, 8)
we may conclude 'non-inferiority' of SDF relative to ART+FV treatment. The confidence
interval may secondarily be examined to assess possible superiority of one intervention over
the other. For arrest rate, a 95% CI for the difference in rates (based on a z statistic)
will be computed. If this confidence interval lies within the interval (-0.09, 1) we may
conclude 'non-inferiority' of the SDF relative to ART+FV treatment. As above, possible
superiority of one intervention over the other may also be assessed. For other outcomes,
computing 95% CI for differences in means (or proportions for binary outcomes) will also be
used. These secondary outcomes will be assessed in an exploratory manner for possibly
superiority or inferiority based on appropriate margins.
To corroborate initial results, a generalized estimating equations (GEE) approach will be
used. For each outcome, a GEE (marginal) model will be fit that includes a treatment
indicator and prognostic variables (including sociodemographic variables, medical conditions,
and oral health behaviors). Appropriate link functions (e.g., logit link for binary outcomes
and identity link for continuous outcomes) will be specified and an exchangeable working
correlation matrix used to allow for correlations within site. The arrest outcome will be
analyzed as a binary outcome (as described in the sample size section), and secondarily as
the number of arrested lesions assuming an appropriate distribution (e.g., negative binomial)
and link function (e.g., log link). Robust t tests with correction for a small number of
clusters will be used to test for treatment effects and corresponding 95% confidence
intervals computed.
Secondarily, the above GEE approach will be extended to analyze the repeated (baseline, 26
and 52 week) measures for each outcome. The models for each outcome will include the same
prognostic variables as before, as well as time and a time by treatment interaction.
Correlations among the repeated measures will be allowed, e.g., using a first-order
autocorrelation structure. If a substantial within-facility correlation is found it would be
necessary to incorporate facility as a second cluster levels (within which person - the first
cluster level - is nested). Additionally, estimation and testing (via a robust t-test) the
interaction term to compare trends over time for the two interventions will also be employed.
If the use of two cluster levels is not found to be feasible in the GEE approach, a
generalized mixed effects model approach will be considered.
Causal Inference standards: The use of randomization and adjustment in regression models
should be sufficient to provide causally interpretable intervention effect estimates; special
causal inference techniques such as propensity score or instrumental variable methods often
indicated for observational studies, will therefore not be necessary for the data analysis.
Biases will be avoided in causal inferences about the intended interventions by using an
intent-to-treat approach, in which individuals are analyzed according to randomized groups
without regard to (extent of) treatment actually received.
Sensitivity Analyses: Sensitivity of conclusions to model assumptions will be checked as well
as methodological decisions such as outcome definition. In particular, for the arrest
outcome, the primary analysis is based on a binary indicator of 'complete' arrest of all
lesions for an individual. Results will be corroborated from this approach with an
alternative approach analyzing number of lesions arrested. For the GEE analyses, planned for
all outcomes, sensitivity will be assessed in part by using alternative working covariance
structures and alternative sets of predictors. For some outcomes, alternative distributions
will be considered; for example, for number of arrested lesions, possible distributions
include Poisson, negative binomial, zero-inflated Poisson (ZIP), and zero-inflated negative
binomial (ZINB). Sensitivity to assumptions regarding missing data will also be investigated
as described further below.
Missing Data. (a) Methods to prevent and monitor missing data: All questionnaires (using
tablets) and dental assessment forms (using paper), will be managed in an electronic database
(REDCap) by study staff. Weekly quality control checks will be run for outliers, entry
errors, missing data, and potential data anomalies. Statistical analyses, summary and missing
data reports will be generated by the study biostatistician monthly during the study. (b)
Statistical Methods to Handle Missing Data and Account for Statistical Uncertainty Due to
Missingness: The primary analyses, which use GEE based on all available observations, assume
outcomes are missing completely at random (MCAR). As an initial assessment of missing data
patterns, intervention groups (including by relevant subgroups and time point) will be
compared with regard to missing outcome rates. A test for a relationship between missingness
and outcomes at early time points will evaluate the plausibility of the MCAR assumption.
Multiple imputation methods (an established method) will be used to help assure valid
inferences. (d) Plans to Record and Report Dropout and Missing Data: The trial data will be
managed using REDCap software, currently running at Case Western Reserve University (CWRU).
REDCap is a secure web-based application providing an intuitive interface for validated data
entry, audit trails for tracking data manipulation and export procedures, and automated
export procedures for seamless data downloads to common statistical packages. Missing data
reports will be generated weekly from REDCap for timely resolution and reporting purposes.
(e) Plans to Examine Sensitivity of Inferences to Missing Data Methods As noted above,
multiple imputation will be used to obtain valid inferences in the presence of data that are
not missing completely at random. Predictive mean matching with an appropriate prediction
model will be used depending on the outcome (e.g. logistic regression for binary outcomes,
linear regression for continuous and count outcomes). Alternative imputation models will be
used as part of sensitivity analyses.
