Recasting and Book Reading Under Ideal (Dose-controlled) and Typical (Dose-variable) Conditions: The Role of Fidelity and Adherence in Production and Comprehension Outcomes for Children With DLD
Phase: NA
Conditions: Developmental Language Disorder and Language Impairment
Sponsor: University of Delaware

 
NCT ID Number  NCT05099328  
Official Title: Recasting and Book Reading Under Ideal (Dose-controlled) and Typical 
(Dose-variable) Conditions: The Role of Fidelity and Adherence in 
Production and Comprehension Outcomes for Children With DLD   
Secondary IDs: R01DC018276 [U.S. NIH Grant/Contract Award Number] 
Statistical Plan Last 
Updated 
July 15, 2025  
 

Risks to Human Subjects 
 
**Updated June 28, 2021 – the primary changes are related to the shift to teletherapy and associated 
changes in hearing screening protocols and risks to loss of confidentiality.  
 
**Updated to add changes in inclusionary criteria 
 
**Updated July 15, 2025 – Includes statistical analysis plan.  
 
Human Subjects Involvement, Characteristics, and Design  
The study is a Phase I clinical trial that employs a randomized parallel design. We plan to enroll 160 
children diagnosed with Developmental Language Disorder and their caregivers who will provide services to 
approximately half of these children. We predict that 15% of the children (N=20)  will be lost to follow-up, 
thus we enroll 160, in order to have 140 children complete the entire study and meet the planned enrollment 
goals. Children will be randomly assigned to one of four treatment conditions, thus we expect 35 
participants per condition.  Research will be carried out at the University of Delaware and the University of 
Maryland College Park.  
 
Children with Developmental Language Disorder (N = 160) 
Inclusionary & Exclusionary Criteria: 
• 
Age: 4-7 years old 
• 
Primarily English speaking: Less than 20% exposure to another language, per parent report 
• 
Document poor syntax skills: standard score below 85 on the DELV-NR composite, a dialect 
neutral assessment (Seymour, Roeper, de Villiers, & De Villiers, 2005).  
• 
Nonverbal IQ within typical range:  t-score at or above 35 on the DAS, matrices subscale, (Elliott, 
2007).  
• 
Hearing within the typical range: Pass screening using SoundScouts  (Gumbie et al., 2018; 
Mealings et al., 2020) or provide reports of passing a hearing screening at school or in a 
pediatrician’s office. *Due to COVID and limitations on in person activities, treatment may 
proceed even if these reports are not obtained if no one has concerns about hearing and we will 
track/follow up with families to obtain a clear hearing screening within 12 months of completion of 
intervention. Documentation of how many children did not complete hearing screenings will be 
retained and inform interpretation of data.   
• 
No diagnosis of Autism: Cutoff score of 15 on the SCQ, (Rutter, Bailey, & Lord, 2003) 
• 
No diagnosis of significant sensory-motor concerns or significant psychiatric disorders per parent 
report  
• 
Able to benefit from treatment: 
o Score below 60% correct on experimenter developed elicited production probes of 
passives and object relative clauses 
o Producing simple transitive sentences (SVO) and MLU of 2.5 on 100 utterance language 
sample 
 
Caregivers of Children with DLD (N=160) 
A single caregiver will be identified at the time of enrollment to receive training in study procedures. In 
general, the caregiver’s race will align with the child’s race. Overwhelmingly women are the caregiver who 
coordinate this type of care, bring children to appointments, and engage in parent provided treatment. That 
said, we do not restrict this role to parents or to women. We will take care with our materials to encourage 
the participation of any primary caregiver.  
 
Inclusionary & Exclusionary Criteria: 
• 
Basic literacy skills per self-report 
• 
Willing to participate in caregiver training and caregiver-based treatments if child is assigned to 
that condition 
• 
Willing to participate in questionnaires and structured interviews during post-test 
 
Study Procedures, Materials, and Potential Risks  

Materials. 
Screening and pre- and post-test materials will consist of commercially available standardized tests (see 
inclusionary and exclusionary criteria above) and experimenter developed probes.  The experimenter 
developed probes will utilize illustrations that are child friendly to support the elicitation and comprehension 
of passives and object relative clauses. 
 
Screening 
After providing informed consent, parents will complete a case history form, the FOCUS questionnaire, the 
SCQ, and the BRIEF.  An SLP or research associate will administer standardized and non-standardized 
assessments, including the DAS-2, matrices subscale, the DELV, the PPVT-5, the EVT-3, and a language 
sample. All information obtained will be coded with an alphanumeric code to protect confidentiality and 
stored on secure university servers.  A 10-item elicited production probe will be completed at this point to 
determine if the child may advance to the pre-test stage.  
 
Pre- & Post-Test 
Children who qualify as DLD will be administered 2 20-item production probes to elicit passives and relative 
clauses. They will participate in a hearing screening, They will complete 2 blocks of 24-item comprehension 
probes (48 items total) to assess online comprehension of active/passive and object/subject relative clauses 
using a 2AFC eye tracking task administered via PCIbex.  Additional language sample data will be collected 
to ensure sufficiently large samples for analysis. Audio files, transcription, visual fixation data, and scoring 
will be coded with an alphanumeric code to protect confidentiality and stored on secure university servers.   
 
Treatment  
Children will be randomly assigned to one of four conditions: recasts delivered by parents, recasts delivered 
by the clinician, syntax stories read by the parent, and syntax stories read by the clinician.  In the recast 
condition, adults will interact with children and recast (restate, with a correction) a child’s utterance as either 
a passive or object relative clause, with the goal of recasting 960 times over the course of the study (1 
recast/minute, for 60 minutes, 16 sessions).  In the syntax story condition, adults will read illustrated stories 
containing 30 exemplars of the target structure to children at least 32 times for a total of 960 exposures).  
These treatments will either be carried out by lab staff or by caregivers. During the treatment period, 
adherence, fidelity and language environment data will be collected using durable, long life audio recorders 
(LENAs) in conjunction with time logging/attendance records. In the home condition, training will be 
provided to the caregivers as to how to complete the treatment protocol via zoom. Caregivers will also be 
asked to log when treatment is carried out to focus the LENA analyses.   
 
Assessment of the constructs within the Theoretical Domains Framework (TDF) 
Motivation, self-efficacy, effort, and treatment efficacy will be assessed using rating scales administered to 
children (e.g., how fun was today’s session? how hard did you work today?) clinicians (e.g., how effective 
did you feel? how many times did you have to provide redirection?) and caregivers (e.g., does your child 
like coming to treatment? how effective did you feel today?). These will be tailored to align with the 
treatment and delivery process. In addition, we will assess parent and clinician knowledge of the treatment 
through written tests of the grammatical targets and techniques used and observations of them providing 
treatment in the lab. Caregivers will complete a questionnaire designed to assess barriers within the TDF 
adapted from Huijg et al. (2014), see appendix.  Finally, we will conduct structured interviews with parents 
during the post-test phase using an ethnographic interview style.   
 
Risks 
The primary risks associated with the study are loss of privacy or confidentiality, anxiety, and boredom and 
fatigue. The study procedures are entirely behavioral in nature and the risks do not exceed those 
experienced in everyday life. There is also the risk of being assigned to the less effective arms of the RCT.  
 
Alternative Treatments 
Children may obtain therapy for their language impairment via free publicly available services or via private 
pay for treatment. We do not discourage this and will actively connect children not receiving services with 
Child Find and counsel parents about what treatment they can obtain via private pay.  Some families may 
choose to only participate in our study and others may participate in other therapies. We do not restrict this 

choice and document it only. It is unlikely that children will be receiving treatment for the particular 
structures being studied, but where possible, we will document IEP goals that intersect with the targeted 
structures and take this into account when interpreting the data.   
 
Adequacy of Protection Against Risks  
 
a. Informed Consent and Assent  
Participants will reach out to our labs after receiving information about the study from a flier in the 
community, their speech pathologist, school, or other community broker.  Once they contact a lab, we 
will describe the study to them, proceed through a brief phone screening, and invite participants to 
schedule an appointment over zoom at a time convenient to the family. We will request that the child, as 
well as the participating caregiver, and a guardian who can sign consent forms attend (these may or 
may not be the same people) the first appointment. At the first appointment, we will present information 
about the timeline and participation required for the study, describe study procedures, and answer any 
questions about the study. Families will be invited to sign consent forms for both adults and children. 
Written consent will be obtained from adults on their own behalf and from a parent or guardian of the 
child on behalf of the child.  Informed consent documents will include appropriate information to allow 
participants to opt in to data sharing as described in the Data/Resource Sharing and Dissemination 
Plans.   
 
Children under 7 are generally assumed not to be able to give consent or to understand written assent 
documents. We will obtain verbal and nonverbal assent from the child instead. Activities will be child 
friendly and we will move at the child’s pace, interspersing demanding activities with breaks. We will 
invite children to participate in activities (would you like to play a pointing game?) up to three times. If 
children decline to participate or indicate via body language that they are not providing assent, we will 
represent activities at a later time and work with parents to identify appropriate times of day (e.g., after 
lunch rather than before) and ways of delivering the activities to make them acceptable to the child (on 
the floor rather than at a table, with appropriate movement breaks, with a caregiver present). Our lab 
reserves the right to discontinue participation when we judge there to be a lack of assent, even if 
parents would like them to continue to participate.  
 
b. Protections Against Risk  
The study procedures are entirely behavioral in nature and the risks do not exceed those experienced in 
everyday life.  
 
As with any study, there is a risk of loss of privacy and confidentiality. This is perhaps increased due to 
the use of the LENA recorders and online data collection methods.  We can mitigate against this by 
training parents to control the LENA device, allowing them to start and pause the recorder as needed. 
We also use encrypted (HIPAA compliant) versions of zoom to ensure patient privacy is maintained.  
We disclose to families during the consent process that we are mandatory reporters for child abuse and 
that we may come to know things that require reporting via the LENA recordings.  Confidentiality and 
privacy are managed within each lab in general using the following strategies.  Screenings and training 
in the treatment provision are provided at locations and times selected by the families. We use 
alphanumeric codes to identify participants and code all data. Data are stored on university servers that 
are accessible via using university IDs and two-factor authentication. Servers are encrypted.   
 
Parents may experience some anxiety about their child’s performance on assessments for enrollment in 
the study or about the gains their child makes during the study. Generally, families are referred to our 
lab and already receiving services, but sometimes a child’s language impairment is first diagnosed by 
us.  We provide counseling to the parents and interpret standardized test results. We make referrals to 
public services as are appropriate. Families may receive a report describing their child’s test results that 
they may share with professionals if they desire. Parents may also experience anxiety about their own 
performance during the study. We reassure parents about how change is measured during treatment 
and that learning to do a new thing can be challenging for anyone.  
 

Children may experience boredom and fatigue. We develop child-friendly activities and move at the 
child’s pace.   
 
There is a risk, as in any RCT, that a child will be assigned to the less effective treatment. Freedman 
(1987) argues that this risk is acceptable as long as equipoise (or an authentic belief of potentially equal 
benefit from each treatment at the outset) is held. That does not exist here, as we explicitly express a 
belief in the proposal that the caregiver delivered recast therapy will be less effective. Johnson, Lilford, 
and Brazier (1991) argue that it is not the individual investigator who must have equipoise but rather the 
collective clinical community as a whole that must have some uncertainty as to which of multiple 
treatments is the most effective. In our own (nonsystematic) conversations with practicing clinicians, 
they firmly express the belief that recasting by anyone will lead to better outcomes than syntax stories. 
We believe we have made a convincing argument in the other direction. Thus we believe the field is in a 
state of collective equipoise on this issue and the RCT is ethically justified.  
 
Nonetheless, we acknowledge the risk of being assigned to the least effective treatment is experienced 
at the level of the individual participant. We mitigate against this risk in the following ways:  
1) Individuals are encouraged to continue to access the public services to which they are entitled.  
2) Our labs actively assist families in making the public and private contacts and provides written 
diagnostic reports to assist in family self-advocacy.   
3) While the resources do not exist within this trial to give everyone in-lab recast therapy, the 
resources do exist to provide parents with the at-home materials and training that they did not 
receive. Thus, for parents in the recast at home or the two in lab conditions, we will offer the 
materials for syntax stories. For parents who were in the syntax stories or the two in lab conditions, 
we will also offer them the training for at home recasting. Parents may opt in to these trainings after 
post testing as an additional benefit. 
 
c. Vulnerable Subjects, if relevant to your study. Children are considered vulnerable participants. Their 
inclusion is justifiable because the questions are about language learning in children with language 
impairment. The protocol presents minimal risk and some reasonable expectation of direct benefit, given 
that the treatments have previously been shown to benefit children with DLD (recast therapy) and typical 
children (book reading and recast therapy). Thus, work with a vulnerable population is justified.  
 
Potential Benefits of the Proposed Research to Research Participants and Others  
 
Recast Therapy. Recasting is a well-known, evidence based treatment for language impairment  that has 
been documented as providing gains in the area of vocabulary, MLU, morpheme accuracy, and complex 
syntax, with a good effect size (Hedge’s g = 0.75-1; Cleave, Becker, Curran, Owen Van Horne, & Fey, 
2015). It is often used both alone and as part of a larger therapy package, including Focused Stimulation 
(Girolametto, Pearce, & Weitzman, 1996), Enhanced Milieu Therapy (Kaiser & Roberts, 2013; Kaiser & 
Hester, 1994), and Dialogic Reading (Whitehurs, Arnold, Epstein, Angeli, Smith, & Fischel, 1995). Efficacy 
can be enhanced by treating a single target (i.e., focused stimulation rather than general stimulation; Cleave 
et al., 2015), ensuring adequate input variability (Meyers-Denman & Plante, 2016), and ensuring that the 
child is fully attending (Plante et al., 2014).  A typical effective dose 600-1000 recasts provided over 10-20 
hours of treatment. Parents can be trained to provide this treatment (Fey, Krulik, Frome Loeb, & Procter-
Williams, 1999; Proctor-Williams, Fey, & Frome Loeb, 2001).  Most prior work has focused on morphology.  
We have published on the use of recast therapy for complex syntax (Curran & Owen Van Horne, 2019) and 
our own preliminary data, which was focused on complex syntax instruction shows very large effect sizes, 
d= 1.84, as compared to children treated on other targets.  
 
Syntax Stories Preliminary evidence for the efficacy of syntax stories largely comes from the typical 
literature. Vasilyeva et al. (2006) exposed 4-year-olds to  520 examples of passives (e.g., the girl was 
kicked by the boy) through 10 36-sentence stories, each containing 25 exemplars. Hesketh, Serratrice, and 
Ashworth (2015, 2016) carried out similar studies, exposing children to 300 exemplars of  coordinated 
clauses (and), adverbial clauses (when, because), embedded clauses (She said “He is happy”) , or reported 
speech (She told us that he is happy) by way of 10 short stories read aloud to the class. In all three studies, 
TD preschool and school-age children showed improvement on the trained structure. In our own preliminary 

data, 9 children with DLD and 9 TD children, 3-5 years old, had syntax stories read to them by caregivers 
several time a week, for an average of 583 (range 408-816) exposures to each target. Results indicate 
increased production probe accuracy for treated forms only, with gains evident for both TD and DLD 
children (Treated-Control, TD: p = .01, d = 1.02; DLD: p = .005, d = 1.32). Although TD children make 
greater absolute gains, the DLD children have lower pre-test scores and more consistent (less variable) 
gains and thus show larger effect sizes.  
 
Thus, there is reason to expect that children will acquire new grammatical forms and increase their mean 
length of utterance over the course of the study.  The degree of benefit obtained will likely vary by child and 
by the degree to which parents faithfully implement the treatment. There is less known about degree of 
benefit via teletherapy but preliminary evidence from our own lab suggests that it is approximately equally 
effective for children who participate in online activities consistently.  Given that risks are minimal, this is a 
reasonable trade off.  At the conclusion of the study, parents will receive the materials used to train the at-
home conditions in book reading and recasting if they so desire.  
 
Importance of the Knowledge to be Gained  
Understanding how these two different treatments are differently administered by lab staff and parents will 
inform our understanding of the effectiveness of these treatments under everyday delivery conditions and 
provide a foundation for improving implementation of treatments to improve grammar outcomes. Linking 
changes in grammatical processing to treatment outcomes will help us to understand whether commonly 
used therapies like recasting have an effect on both comprehension and production or only on production.  
These results have the potential to change how treatment is provided to children affected by DLD (7% of the 
population).  
 
 
Statistical Design and Power  
 
Randomization  
To guard against bias, children are randomly assigned to condition. A randomization scheme was pre-
allocated at the beginning of the study by Henry May at CRESP and uploaded to RedCap using a blinded 
allocation module. Allocation is requestable as children qualify for the study from CRESP.  This ensures 
that the assessor at pre-test is blind to allocation.  To ensure approximately equal allocation to each 
condition at each site, separate randomization schemes are used for each site. Flow through the study and 
drop outs at each phase are tracked following EQUATOR reporting guidelines.  
 

 
 
Data Reduction  
 
Estimated Dose   
We compute estimated dose provided as follows: 
Dose =  number of sessions reported by families * percent sessions documented based on 
recordings * recasts/sentences read verbatim observed in transcribed sessions  
 
Production Data  
The dependent variable is production accuracy scored item by item as correct (1), incorrect (0), or 
unscorable (missing). Individual items are additionally coded as the syntactic type elicited (passive voice or 
object relative clause) and as treated/untreated clause type since that is counterbalanced across 
participants.   
 
Eye-tracking Data  
Eye-movements are continuously sampled at the rate of 500 Hz (every 2 ms) from trial onset to the onset of 
the pointing prompt. Fixations are coded as present (looks to an area of interest on screen) or missing 
(looks away from these interest areas, track loss, blinking). Present looks are recoded as Agent fixations or 
Patient fixations based on the looks’ relation to target sentences.  “Agent fixations” are looks to areas of 
interest where first-mentioned noun phrases are agents (i.e., correct interpretation for actives and subject 
relative clauses). “Patient fixations” are looks to areas of interest where first-mentioned noun phrases are 
patients (i.e., correct interpretation for passives and object relative clauses). Based on pilot data, 
linguistically mediated eye-movements in responses to the speech stream occur primarily during a 2-second 
window from the disambiguating verb morphology. Also, consistent with prior work (Huang et al., 2013; 
Huang et al., 2017), our pilot data indicate that interpretations of canonical structures (i.e., actives) occur 
faster than noncanonical structures (i.e., passives). On this basis, we will run separate analyses over an 
“early window” (e.g., first 1000 ms after disambiguation) and “late window” (e.g., second 1000 ms after 
disambiguation). To examine sensitivity to the timing of window transitions, and to better pinpoint typical 
timing of stimulus effects, we will explore a range of time values distinguishing potential early and late 
windows. 
Family contacts lab, 
Study explanation, 
Phone Screening
Rule out or Not 
interested 
Consent, Child Screened 
in Person
Rule out 
Child Qualifies
At home +Recast
Counterbalance 
assignment  for 
Treated/Untreated 
Structure 
At home + Syntax 
Stories
Counterbalance 
assignment  for 
Treated/Untreated 
Structure 
In Lab + Recast
Counterbalance 
assignment  for 
Treated/Untreated 
Structure 
In Lab + Syntax Stories
Counterbalance 
assignment  for 
Treated/Untreated 
Structure 
Pretest Completed
Allocation Requested 
from CRESP

 
Within these windows, we will first calculate Agent Preference for each item and then use this value to 
derive Structural Sensitivity for each set of sentences (active/passive, subject/object relative clause). 
First, for each test point (pre- and post-) and sentence type, we will determine whether canonical and 
noncanonical structures elicit different interpretations. Agent Preference will be calculated as an averaged 
proportion of Agent looks over Agent plus Patient looks. Values greater than 0.5 indicate a preference to 
look at the Agent while values less than 0.5 indicate a preference for the Patient.  
 
Second, for each time window (early and late), we will assess the extent to which Treatment Condition 
affects interpretation of the treated noncanonical structure. Our dependent variable will be Structural 
Sensitivity, calculated as an averaged difference score of Agent Preference in the canonical structure 
(SRC, active) minus the noncanonical structure for each structure (ORC, passive) for each child. This 
measure is selected rather than Agent Preference because it captures the degree to which a child 
differentiates the two structures from each other. Sensitivity to passive/object relative clause cues will be 
evident in values greater than 0, with higher values mapping onto increased sensitivity. Each child will 
receive a single Structural Sensitivity score for active/passives and a single Structural Sensitivity score for 
subject/object relative clauses at each test point.  
 
Analytic Approach  
For both comprehension and production outcomes, we utilize generalized linear mixed models to examine 
change over time for each condition. The adoption of generalized linear mixed models (GLMM) with random 
effects for subjects and items as the statistical approach has advantages in that we do not need to exclude 
children for failing to produce scorable responses given that this model can account for uneven response 
rates across children (Baayen, Davidson, & Bates, 2008) and it accounts for item level variance that can 
affect results.  
 
Production probe accuracy will be analyzed with a logit link function. Use of a logit link function for 
production data also closely mirrors the underlying data structure in which individual items are scored as 
correct or incorrect. Structural sensitivity will be analyzed with linear mixed-effects models.  Maximal models 
will include random intercepts (and possibly random slopes) for subjects, items, and clause type (Barr, 
Levy, Scheepers, & Tily, 2013), and parameter-specific p-values will be estimated with degrees of freedom 
determined through Satterthwaite approximation (Luke, 2017). 
 
We will model comprehension and production outcomes separately, though we make similar predictions as 
to the effect of treatment on each outcome measure. Prior to carrying out the main analyses, we will check 
for differences attributable to participant sex and race (relevant biological variables). These will be included 
in future analyses if necessary, but prior work suggests that they are not likely to influence outcomes.  
 
We will also use generalized additive mixed models (GAMMs) to model longitudinal patterns in eyetracking 
data (i.e., looks to the agent) to examine and visualize timing of stimulus effects and changes in focus at 
key points during and after presentation of the prompt. GAMMs model fixations as an intensive time series 
(Cho et al., 2018, 2022) and allow identification of key points of separation. Moreover, the GAMMs models 
do not require prespecification of windows of time, providing more flexibility and sensitivity when modeling 
differences in eyetraking over time and between groups. The specification of the GAMMs models will mirror 
the GLMM equations below, except that smoothing functions will be used for an additional TIME predictor to 
fit a spline regression for each time series. 
 
Aim 1. Determine the relative differences in the efficacy of illustrated syntax stories and recast 
therapy when treatment is dose controlled and delivered by highly trained SLPs in the laboratory.  
 
We will first confirm that we achieved control of dose in the two in-lab conditions. We will carry out an 
unpaired two-tailed t-test comparing the estimated dose delivered for each condition. We anticipate that the 
two conditions will not differ and that the distributions will show high degrees of overlap (p>.5, d<.3).  
 
To test the key question in Aim 1, we will compare the relative effect of treatment condition by testing for 
differences between the treatment effect estimates of stories in the lab and recasts in the lab conditions. 

Well-matched groups at pretest will be evident if the two conditions do not differ at pre-test. If one treatment 
condition is more effective than the other, this should be evidenced by an interaction between test point and 
treatment contrast effects that are significant at the p<.05 level; if, as we hypothesize, the two conditions are 
equally effective there will be no significant interaction between test point and treatment.  We are powered 
to detect an effect size difference of .35 (2 raw score points) or larger on production. 
 
Likelihood of a Correct Production = TestPoint *TreatmentCondition 
+ (1|item)+ (clause type |subject) 
Structural Sensitivity = TestPoint *TreatmentCondition  
+ (clause type |subject) 
 
In the somewhat likely event that the interaction between time point and treatment is not significant, we can 
directly test for treatment efficacy by comparing the effect of treatment on the treated vs. untreated 
structures.  We expect that the model estimate for Treated or Untreated clause will be significant at p<0.05 
level, favoring the treated clauses, demonstrating that treatment was effective and ruling out maturational 
effects. 
 
Likelihood of a Correct Production = TestPoint *TreatedOrUntreated  
+ (1|item)+ (clause type |subject) 
Structural Sensitivity = TestPoint *TreatedOrUntreated  
+ (clause type |subject) 
 
Aim 2. Determine whether the influence of delivery method (illustrated syntax stories or recast 
therapy) on dose delivered (number of exposures to the target) is of sufficient magnitude that 
changes in child outcomes are observed when treatment is delivered by caregivers. 
 
We will first determine whether dose differs across the two at-home conditions. We will carry out an 
unpaired two-tailed t-test comparing the estimated dose delivered for each condition. We anticipate that the 
two conditions will differ and that the distributions will show low degrees of overlap (p<.05, d>.5).  
 
To test the key question in Aim 2,  we will test for a dose by test point or dose by treatment condition by test 
point interaction for stories at home and recasts at home conditions. Well-matched groups at pretest will be 
evident if the two conditions do not differ at pre-test. If one treatment condition is more effective than the 
other, this should be evidenced by an interaction between test point and treatment contrast effects that are 
significant at the p<.05 level; if, as we hypothesize, dose is a critical component of this difference, a two- or 
three-way interaction between time and dose or between time, treatment and dose will be observed.  We 
would interpret the two-way interaction as indicating that dose, rather than treatment is critical for obtaining 
the key outcomes. A three- way interaction suggests that the delivery method modifies the effectiveness of 
the total dose.   
 
Likelihood of a Correct Production = TestPoint *TreatmentCondition*EstimatedDose 
+ (1|item)+ (clause type |subject) 
Structural Sensitivity = TestPoint *TreatmentCondition*EstimatedDose  
+ (clause type |subject) 
 
In the event that the interaction between time point and treatment is not significant, we can directly test for 
treatment efficacy by comparing the effect of treatment on the treated or untreated structures.  We expect 
that the model estimate for Treated vs. Untreated clause will be significant at p<0.05 level, favoring the 
treated clauses, demonstrating that treatment was effective and ruling out maturational effects. 
 
Likelihood of a Correct Production = TestPoint *TreatedOrUntreated  
+ (1|item)+ (clause type |subject) 
Structural Sensitivity = TestPoint *TreatedOrUntreated  
+ (clause type |subject) 
 
Power Analysis for Production  

Power analyses were carried out using Monte Carlo simulation (5,000 iterations) via SAS 9.4 and PROC 
GLIMMIX using residual pseudolikelihood estimation of a binary logistic mixed effects model with item 
responses (n=20) nested within tests (n=4 representing test point and clause type repeated measures) that 
are nested within children (n=120) who have been randomly assigned to treatment conditions, 
counterbalanced across the 2 clauses. Random effects are included for items within clauses, as well as four 
correlated child-level random effects (i.e., for each of the four repeated measures). A single model was 
used to estimate power in alignment with the overall design of the study in which children are randomly 
assigned to one of FOUR conditions. Power for the more focused models described above can be inferred 
from the power estimates for the treatment contrasts below. By adopting this randomization scheme and 
model design, we retain the ability to test unplanned contrasts that go beyond the planned analyses for the 
Aims if we so chose (e.g., recasts in the lab vs. recasts at home) in order to carry out secondary analysis 
associated with cost effectiveness, dose, or home environment.  
 
Test scores were simulated based on means and standard deviations of pretest and posttest scores from a 
previous study. Pretest scores for both clauses were sampled from a Normal distribution with M=3.0 and 
SD=2.5, rounding to the nearest integer, with a minimum score of 0 and maximum score of 7 items correct 
(corresponding to the >40% correct study eligibility criteria). Posttest scores for untreated clauses were 
sampled from a Normal distribution with a correlation of .90 with pretest scores and M=3.0 and SD=2.5, 
rounding to the nearest integer, with a minimum score of 0. Posttest scores for treated clauses were 
sampled from a Normal distribution with a correlation of .90 with pretest scores, rounding to the nearest 
integer, with a minimum score of 0, and M=5.5 and SD=4.0 for the syntax stories at home condition 
(ES=+1.0), M=4.0 and SD=4.0 for the parent recasts condition (ES=+0.4), M=6.0 and SD=3.5 for the syntax 
stories in the lab condition (ES=+1.2), and M=8.0 and SD=3.5 for the lab recasts condition (ES=+2.0).  
 
Based on the above effect size estimates, power for detecting main effects in Aim 1 exceeds .95 for the 
syntax stories at home, syntax stories in the lab, and lab recasts. Power is .61 for detecting main effects of 
parent recasts (i.e., the effect of parent recasts would need to exceed .50 to be detected with 80% power). 
Power for treatment effect contrasts is .75 to detect differences in syntax stories at home and parent recasts 
effects, .90 to detect differences in parent recasts and syntax stories in the lab effects, and .99 to detect 
differences in parent recasts and lab recasts effects. Other contrasts (e.g., syntax stories at home vs. 
syntax stories in the lab) have power less than 80% if the expected effect sizes hold true (i.e., +1.0 vs. 
+1.2); differences in effects would need to exceed 0.35 logits (2 raw score points) in order reach statistical 
significance with 80% power.  
 
The influence of dose (Aim 2) was simulated by predicting different dose distributions for each condition. 
These distributions were simulated and then we randomly sampled from the distribution for each case. 
Hypothesized distributions of dose lead to substantial changes in the result, and thus we make our method 
of construction these distributions clear here. We assume that a minimum dose is achieved during the 
training visits (home condition) or by attending at least 1 session (lab condition) for all children. The two in-
lab conditions are highly similar and both assume that 25% of participants attend between 1 and 12 
sessions, 25% attend between 12 and 16, and the remainder have perfect attendance. This is a 
conservative estimate based on attendance data from our summer camp study.  
 
The two at-home conditions require slightly more explanation. We assume for recasts that the first quartile 
has a dose of up to 1 recast/5 min for 60 min per week. The next quartile extends up to 1 recast/5 min for 
120 min per week. The third quartile ranges up to a dose of 1 recast every 3 min for 120 min per week and 
the last quartile assumes target delivery. Similarly, syntax stories at home escalates from reading 1x/week 
to reading 2x/week to reading 4x/week (as asked) to reading 1 syntax story daily as the maximum. Several 
families in the pilot data for the syntax stories read the provided syntax stories daily.   
 
Table 1. Hypothesized dose delivered by condition, in quartiles  
Recasts in lab  Stories in lab 
Recasts at home 
Stories at home 
minimum 
60 
60 
60 
60 
1st quartile 
768 
768 
96 
240 
median 
912 
930 
192 
460 
3rd quartile 
960 
960 
320 
960 
maximum 
1008 
960 
960 
1680 
 

Next the gain obtained from each dose was estimated by multiplying the dose provided by a value drawn 
from a uniform distribution ranging from .0005 to .006, to account for the fact that some children will be 
more responsive to any given dose than others. Finally, the gain was added to the pretest scores to yield 
posttest values.  Simulations were checked to ensure that final post-test means and standard deviations for 
the treated structure were in line with estimates for post-test gains used for Aim 1 simulations. Planned 
statistical analysis were carried out over the simulated data and these simulations were repeated 1000 
times.  
 
Simulations that include dose indicate power of .93 for models that include dose and time. Effects of 
treatment, dose, and power combined are not detectable because this estimation of dose confounds the 
distribution of dose with treatment delivery approach, as we hypothesize in the strategy.  It suggests that 
three-way interactions will only be detectible if the delivery method modifies the benefit obtained from each 
exposure to the target form.  
 
Power Analysis for Eye Tracking 
 
Power analyses were carried out using G*Power (Faul, Erdfelder, Lang, & Buchner, 2007). Our preliminary 
data focused on eye-movements during a single test point, thus we do not have relevant estimates of 
fixation changes at multiple time points or following interventions. Consequently, we based our sample size 
on power analyses for production (n=120) and calculated the smallest changes in Structural Sensitivity that 
will be detectable with varying levels of power.  
 
In our preliminary data (n=15), children with DLD revealed M = 0.02 and SD = 0.17 in the late window. With 
the proposed sample size, effect sizes of d=.30 will be detectible with 80% power, d=.34 with 90% power, 
and d=.42 with 99% power. These correspond to increases in Structural Sensitivity of M=.05, M = .06, and 
M = .07 respectively. For reference the TD children in our Preliminary Data have a Structural Sensitivity 
score that is M=.29 larger than that of the children with DLD. Based on this benchmark, improvements 
across time points that are 17-24% in magnitude would be detectable in the current design. This suggests 
that this study is adequately powered to detect relatively small changes in linguistically mediated fixations.