Ignite Creation Date:
2024-05-06 @ 4:14 PM
Last Modification Date:
2024-10-26 @ 2:07 PM
Study NCT ID:
NCT04928209
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-06-05
First Post:
2021-06-09
Brief Title:
Teletherapy in Children Who Are Deaf and Hard of Hearing
Sponsor:
University of California San Francisco
Organization:
University of California San Francisco
Study Overview
Official Title:
Teletherapy to Address Language Disparities in Deaf and Hard-of-hearing Children
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
This study seeks to determine the effectiveness of speechlanguage teletherapy to address disparities in speech and language outcomes in children who are deaf or hard-of-hearing DHH The investigators will enroll DHH children aged 0-27 months 140 children who are publicly insured will be randomized to receive usual clinical care or to be given access to an 18-month course of speech-language teletherapy program 70 children who are privately insured will also be enrolled and will receive usual care Children will undergo at baseline and every 9 months thereafter to a study endpoint of 18 months for a total of 3 timepoints a battery of in-person and parent-report assessments designed to provide a comprehensive measurement of the childs auditory function speech verbal- and non-verbal communication spoken language and quality of life
Detailed Description:
210 children aged 0-27 months with confirmed permanent hearing loss will be recruited from OtolaryngologyAudiology clinics at multiple pediatric hospitals All provide care for a broadly diverse DHH population for the disparities of interest Children who come into OtolaryngologyAudiology clinics will be screened for eligibility Once eligibility is confirmed enrollment will be offered Once enrolled all groups will undergo comprehensive speech language and quality-of-life assessments at baseline and every 9 months thereafter at each site Assessments will include measures of language speech vocabulary hearing-related quality of life parental self-efficacy and early intervention benefit The investigators will additionally measure therapy utilization and baseline demographic and clinical characteristics
Children will be allocated to one of the following three study arms 1 Low-UC low-income children randomized to receiving Usual Care 2 Low-TT low-income children randomized to receive Usual Care plus Access to Supplemental Teletherapy and 3 High-UC higher-income children receiving Usual Care Low-TT group will receive an 18-month course of teletherapy at their home
The overall goal of this study is to learn whether improving access to teletherapy for children who are DHH can reduce disparities in language outcomes Ongoing engagement with Parent and Stakeholder advisors will occur throughout the study to ensure patient-centeredness and dissemination potential
Specific Aim 1Primary Analysis I
Demographic and other baseline data including family characteristics data will be listed and summarized descriptively by the treatment arm Categorical data will be presented as frequencies and percentages For continuous data mean standard deviation median interquartile range minimum and maximum will be presented The Full Analysis Set FAS comprises all children to whom trial treatment has been assigned by randomization According to the intent-to-treat principle analysis will be completed based on the treatment arm and strata to which children are assigned through the randomization procedure
The primary objective of this trial is to compare auditory comprehension AC at 18 months for low-income children receiving TT v UC The following statistical hypotheses will be tested to address the primary objective Ho θ1 0 vs HA θ1 0 where θ1 is the difference between Low-TT and Low-UC at 18 months The primary analysis to test this hypothesis and compare the two treatment arms will consist of a t-test generated from a linear regression model of the primary endpoint AC adjusted for stratification factors The difference between treatments will be calculated along with its 95 CI A total of 140 low-income children will need to be randomized 11 70 per arm to TT or UC to achieve 90 statistical power to detect an effect size of 075 estimated from Table 1 using public insurance as a proxy for income assuming a common standard deviation of 213 and difference of 16 87 vs 71 assuming a smaller difference of the primary endpoint PLS-5 AC at 18 months This sample size was adjusted for the drop-out of 20 and variance inflation factor of 01 501-1 to account for factors adjusted for in the model squared multiple correlation coefficient of 01 This calculation was based on a two-sample t-test assuming equal variances assuming the larger of the two standard deviations of 213 in Table 1 and a two-sided alpha level of 0017 Bonferroni adjusted 0053 co-primary hypotheses
Specific Aim 1Primary Analysis II III
While the primary question of this study is to address whether teletherapy improves language outcomes and access to specialty services for disadvantaged families the investigators also aim to study whether teletherapy can close the language outcomes gap between low and higher-income families A non-randomized cohort study of families with higher income but retaining them as an additional comparison is justified given that the families with higher income likely have access to supplemental services if desired whether children are in the study or not The investigators will be accruing higher-income patients in parallel to the RCT of low-income children Therefore it is important to test the statistical hypotheses Ho θ2 0 vs HA θ2 0 where θ2 is the difference at 18 months in AC between low-income children receiving TT and higher-income children receiving UC and Ho θ3 0 vs HA θ3 0 where θ3 is the difference at 18 months in AC between low-income children receiving UC and higher-income children receiving UC
Higher-income children will be matched on hearing-level and enrollment sites For these latter comparisons control variables will also consist of baseline characteristics known to be associated with AC which includes clinical attributes and demographic disparities and potential differences will be assessed using paired tests To assess whether outcome trajectories differ by study group the investigators will use mixed-effects linear for continuous outcomes and logistic for dichotomous secondary outcomes regression analyses The investigators will flexibly model trajectories by testing whether including quadratic or cubic terms for time up to 3 visits baseline 9 months and 18 months or random slopes for individuals improve the model fit and include them if indicated by a significant P005 likelihood ratio test The overall difference will be assessed using an F-test and post-estimation t-test in SAS v 94 The investigators will also explore the rate of change of scores over time All of the secondary outcomes will be assessed using this mixed modeling approach including QOL outcomes The investigators will also assess the model fit eg residuals and assess whether transforming the outcomes eg log transformations provides the best fit To account for matching the investigators will also include a random effect
The investigators will also assess whether the baseline values are subject to confounding by isolating within-person changes The benefit of mixed-effects models is that such models produce unbiased estimates even when some individuals have missing observations adjust for differential loss to follow-up accommodate irregular time measurements and account for clustering of individuals as required in this study A two-sided p-value less than 0017 will be considered statistically significant Bonferroni corrected for three hypotheses 0053 Estimates and associated 95 confidence intervals including corrected intervals for multiple testing will be reported
210 children will be recruited 70 higher-income and 140 low-income with 70 receiving TT and 70 receiving UC as described above Estimated statistical power to compare the higher-income children to low-income children receiving TT and UC will be assessed via a repeated measures design with 3 visits It assumes an ANOVA F-test with these three groups The investigators assumed a correlation of 09 between-subject variance at 022 error variance of 2 and Bonferroni corrected alpha level 0017 that resulted in 92 statistical power The investigators expect that the pair-wise t-test comparison at 18 months between Low-UC and High-UC would achieve at least 90 statistical power to detect a difference of 275 Table 1 For pair-wise t-test of Low-TT and High-UC the investigators estimate from Table 1 a detectable difference of 101 resulting in 81 statistical power All of the above calculations assume a 20 drop-out collinearity adjustment of 01 and for pair-wise comparisons an intraclass correlation coefficient of 04 These sample size calculations were performed using Stata 151
Specific Aim 2Retrospective secondary analyses
Demographic and other baseline data will be listed and summarized descriptively by utilization group TT or UC Categorical and continuous data will be summarized as described in Aim 1 A linear model similar to Aim 1 will be used to assess the association between AC primary outcome and each of the six disparities at 18 months Heterogeneous treatment effects HTEs will be assessed using the standard HTE approach an interaction between the utilization group and each disparity Exploration of whether disparities act alone or in combination 2 disparities such as those who are Spanish speaking and receive public insurance will be assessed Potential confounders described above and other demographic disparities that may influence the model results will be assessed by performing sensitivity analyses The investigators will also explore whether the interaction is time-varying by fitting an interaction of time by treatment by disparity using the mixed model approach described in Aim 1 The investigators performed simulations to explore the potential statistical power to detect HTE Each simulation was repeated 1000 times to provide precise estimates and the investigators included a total of six disparities which is consistent with the analysis plan The investigators varied the number of disparities that had known differential HTE between 2 to 5 disparities and the disparities that did not ie no differential HTE as well as the effect size 04 to 10 A total sample size of 210 children was assumed The investigators expect approximately 78 children to utilize TT and 132 children to utilize UC The investigators also assumed a Bonferroni correction 00080056 disparities to help protect against family-wise error rate FWER of 005 The investigators included the main effects in the linear regression model ie utilization TT vs UC group disparity dichotomized and the interaction effect between the disparity and utilization group The results from this simulation indicated that there was over 80 statistical power to detect known HTE for 2 disparities regardless of the effect size ES 04 or ES 10 respectively while controlling the FWER at 5 for the remaining 4 disparities without HTE present To detect 5 known disparities there was 68 and 83 statistical power when the ES 04 and ES10 respectively The investigators used SAS v94 to estimate the statistical power The 20 dropout rate is based on the historical 18-month follow-up rate 80 in the UCSF DHH clinic for low-income children who are DHH Travel and time will be significant for the 3 required study assessments but will be compensated information acquired from these assessments will also be a valuable contributor to clinical care and will be shared with the clinical care and Early Intervention teams The assessments will also be aligned with the childs standard clinical care which requires in-person audiology visits every 6-9 months As a comparative effectiveness study the investigators accept contamination between groups and an unblinded design families may make different decisions regarding what exact services to pursue depending on whether were assigned to the teletherapy group or not Families may be influenced by their Early Intervention centers and community support groups contamination via these forums may affect service choices external to this study and diminish effect size relative to our retrospective data Services will be carefully measured for all groups The investigators will explore whether therapy utilization is linked to the outcomes possibly as a mediator To show that therapy utilization is a mediator of the intervention at 18 months the investigators will assess whether it has a main or interaction effect on the primary outcome The investigators will estimate the direct and indirect effects of our regression model when exposure-mediator interaction is present To account for possible effects of crossover and contamination the investigators will consider performing marginal structural modeling as a sensitivity analysis
Children will be allocated to one of the following three study arms 1 Low-UC low-income children randomized to receiving Usual Care 2 Low-TT low-income children randomized to receive Usual Care plus Access to Supplemental Teletherapy and 3 High-UC higher-income children receiving Usual Care Low-TT group will receive an 18-month course of teletherapy at their home
The overall goal of this study is to learn whether improving access to teletherapy for children who are DHH can reduce disparities in language outcomes Ongoing engagement with Parent and Stakeholder advisors will occur throughout the study to ensure patient-centeredness and dissemination potential
Specific Aim 1Primary Analysis I
Demographic and other baseline data including family characteristics data will be listed and summarized descriptively by the treatment arm Categorical data will be presented as frequencies and percentages For continuous data mean standard deviation median interquartile range minimum and maximum will be presented The Full Analysis Set FAS comprises all children to whom trial treatment has been assigned by randomization According to the intent-to-treat principle analysis will be completed based on the treatment arm and strata to which children are assigned through the randomization procedure
The primary objective of this trial is to compare auditory comprehension AC at 18 months for low-income children receiving TT v UC The following statistical hypotheses will be tested to address the primary objective Ho θ1 0 vs HA θ1 0 where θ1 is the difference between Low-TT and Low-UC at 18 months The primary analysis to test this hypothesis and compare the two treatment arms will consist of a t-test generated from a linear regression model of the primary endpoint AC adjusted for stratification factors The difference between treatments will be calculated along with its 95 CI A total of 140 low-income children will need to be randomized 11 70 per arm to TT or UC to achieve 90 statistical power to detect an effect size of 075 estimated from Table 1 using public insurance as a proxy for income assuming a common standard deviation of 213 and difference of 16 87 vs 71 assuming a smaller difference of the primary endpoint PLS-5 AC at 18 months This sample size was adjusted for the drop-out of 20 and variance inflation factor of 01 501-1 to account for factors adjusted for in the model squared multiple correlation coefficient of 01 This calculation was based on a two-sample t-test assuming equal variances assuming the larger of the two standard deviations of 213 in Table 1 and a two-sided alpha level of 0017 Bonferroni adjusted 0053 co-primary hypotheses
Specific Aim 1Primary Analysis II III
While the primary question of this study is to address whether teletherapy improves language outcomes and access to specialty services for disadvantaged families the investigators also aim to study whether teletherapy can close the language outcomes gap between low and higher-income families A non-randomized cohort study of families with higher income but retaining them as an additional comparison is justified given that the families with higher income likely have access to supplemental services if desired whether children are in the study or not The investigators will be accruing higher-income patients in parallel to the RCT of low-income children Therefore it is important to test the statistical hypotheses Ho θ2 0 vs HA θ2 0 where θ2 is the difference at 18 months in AC between low-income children receiving TT and higher-income children receiving UC and Ho θ3 0 vs HA θ3 0 where θ3 is the difference at 18 months in AC between low-income children receiving UC and higher-income children receiving UC
Higher-income children will be matched on hearing-level and enrollment sites For these latter comparisons control variables will also consist of baseline characteristics known to be associated with AC which includes clinical attributes and demographic disparities and potential differences will be assessed using paired tests To assess whether outcome trajectories differ by study group the investigators will use mixed-effects linear for continuous outcomes and logistic for dichotomous secondary outcomes regression analyses The investigators will flexibly model trajectories by testing whether including quadratic or cubic terms for time up to 3 visits baseline 9 months and 18 months or random slopes for individuals improve the model fit and include them if indicated by a significant P005 likelihood ratio test The overall difference will be assessed using an F-test and post-estimation t-test in SAS v 94 The investigators will also explore the rate of change of scores over time All of the secondary outcomes will be assessed using this mixed modeling approach including QOL outcomes The investigators will also assess the model fit eg residuals and assess whether transforming the outcomes eg log transformations provides the best fit To account for matching the investigators will also include a random effect
The investigators will also assess whether the baseline values are subject to confounding by isolating within-person changes The benefit of mixed-effects models is that such models produce unbiased estimates even when some individuals have missing observations adjust for differential loss to follow-up accommodate irregular time measurements and account for clustering of individuals as required in this study A two-sided p-value less than 0017 will be considered statistically significant Bonferroni corrected for three hypotheses 0053 Estimates and associated 95 confidence intervals including corrected intervals for multiple testing will be reported
210 children will be recruited 70 higher-income and 140 low-income with 70 receiving TT and 70 receiving UC as described above Estimated statistical power to compare the higher-income children to low-income children receiving TT and UC will be assessed via a repeated measures design with 3 visits It assumes an ANOVA F-test with these three groups The investigators assumed a correlation of 09 between-subject variance at 022 error variance of 2 and Bonferroni corrected alpha level 0017 that resulted in 92 statistical power The investigators expect that the pair-wise t-test comparison at 18 months between Low-UC and High-UC would achieve at least 90 statistical power to detect a difference of 275 Table 1 For pair-wise t-test of Low-TT and High-UC the investigators estimate from Table 1 a detectable difference of 101 resulting in 81 statistical power All of the above calculations assume a 20 drop-out collinearity adjustment of 01 and for pair-wise comparisons an intraclass correlation coefficient of 04 These sample size calculations were performed using Stata 151
Specific Aim 2Retrospective secondary analyses
Demographic and other baseline data will be listed and summarized descriptively by utilization group TT or UC Categorical and continuous data will be summarized as described in Aim 1 A linear model similar to Aim 1 will be used to assess the association between AC primary outcome and each of the six disparities at 18 months Heterogeneous treatment effects HTEs will be assessed using the standard HTE approach an interaction between the utilization group and each disparity Exploration of whether disparities act alone or in combination 2 disparities such as those who are Spanish speaking and receive public insurance will be assessed Potential confounders described above and other demographic disparities that may influence the model results will be assessed by performing sensitivity analyses The investigators will also explore whether the interaction is time-varying by fitting an interaction of time by treatment by disparity using the mixed model approach described in Aim 1 The investigators performed simulations to explore the potential statistical power to detect HTE Each simulation was repeated 1000 times to provide precise estimates and the investigators included a total of six disparities which is consistent with the analysis plan The investigators varied the number of disparities that had known differential HTE between 2 to 5 disparities and the disparities that did not ie no differential HTE as well as the effect size 04 to 10 A total sample size of 210 children was assumed The investigators expect approximately 78 children to utilize TT and 132 children to utilize UC The investigators also assumed a Bonferroni correction 00080056 disparities to help protect against family-wise error rate FWER of 005 The investigators included the main effects in the linear regression model ie utilization TT vs UC group disparity dichotomized and the interaction effect between the disparity and utilization group The results from this simulation indicated that there was over 80 statistical power to detect known HTE for 2 disparities regardless of the effect size ES 04 or ES 10 respectively while controlling the FWER at 5 for the remaining 4 disparities without HTE present To detect 5 known disparities there was 68 and 83 statistical power when the ES 04 and ES10 respectively The investigators used SAS v94 to estimate the statistical power The 20 dropout rate is based on the historical 18-month follow-up rate 80 in the UCSF DHH clinic for low-income children who are DHH Travel and time will be significant for the 3 required study assessments but will be compensated information acquired from these assessments will also be a valuable contributor to clinical care and will be shared with the clinical care and Early Intervention teams The assessments will also be aligned with the childs standard clinical care which requires in-person audiology visits every 6-9 months As a comparative effectiveness study the investigators accept contamination between groups and an unblinded design families may make different decisions regarding what exact services to pursue depending on whether were assigned to the teletherapy group or not Families may be influenced by their Early Intervention centers and community support groups contamination via these forums may affect service choices external to this study and diminish effect size relative to our retrospective data Services will be carefully measured for all groups The investigators will explore whether therapy utilization is linked to the outcomes possibly as a mediator To show that therapy utilization is a mediator of the intervention at 18 months the investigators will assess whether it has a main or interaction effect on the primary outcome The investigators will estimate the direct and indirect effects of our regression model when exposure-mediator interaction is present To account for possible effects of crossover and contamination the investigators will consider performing marginal structural modeling as a sensitivity analysis
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None