Preferences in the Design and Delivery of Neurodevelopmental Follow-Up Care for Children: A Systematic Review of Discrete Choice Experiments

Figures & data

Figure 1 Preferred Reporting Items for Systematic Reviews and Meta-Analyses report.

Table 1 Demographic Characteristics of the Included DCE Studies (n=16)

Countries	N
United States of America	5
Europe	5
Canada	3
Canada, United States of America	2
Australia	1
Neurodevelopmental Disorder/Delay	N
Attention-deficit/hyperactivity disorder (ADHD)	7
Children’s mental health (CMH)	2
Fragile X syndrome (FXS)	1
Autism Spectrum Disorder (ASD)	1
Idiopathic developmental disability (DD)	1
Intellectual disability (ID)	1
Prader-Willi syndrome (PWS)	1
Coexisting cognitive/intellectual and an emotional/behavior/developmental disability	1
Communication disabilities	1
Respondent Type	N
Parents	9
Caregivers	4
Professionals	2
Teachers	1

Abbreviation: N, count of studies.

Table 2 Characteristics of the Attribute Selection Process, Choice Set Generation, and Experimental Designs of the Included DCE Studies (n=16)

Author and Year	Attributes Selection Process	Number of Attributes Included	Maximum Number of Levels within Each Attribute	Number of Choice Sets per Participant	Blocks	Experimental Design Type	Type of Fractional Factorial	Algorithm (If Efficient Design)	Draws (If Bayesian Design)	Choice Task Format (Labelled/Unlabelled)	Experimental Design Approach	Coding of Categorical Variables	DCE Pilot Test (Yes/No)	Priors Used (Yes/No)	Method of Prior Selection	Survey Administration Method
Arora 2019^Citation35	Pilot study and similar experiments identified in literature	5	5	16	8	Fractional factorial	Bayesian D-efficient design	Modified Federov algorithm	500 Halton draws	Labelled	Forced choice approach	Not stated	Yes	Yes	Existing literature^a	Online
Cross 2016^Citation28	Literature review and expert opinion	6	3	9	6	Fractional factorial	D-efficient design	D-optimal algorithm	NA	Unlabelled	Forced choice approach	Effects	No	Not stated	NA	Online
Cunningham 2008^Citation27	FGD	20	4	30	No	Fractional factorial	Not stated	NA	NA	Unlabelled	Forced choice approach	Not stated	Yes	Not stated	NA	Email and paper-based
Cunningham 2009^Citation26	FGD	14	4	20	No	Fractional factorial	Not stated	NA	NA	Unlabelled	Forced choice approach	Not stated	No	Not stated	NA	Email
dosReis 2020^Citation36,^b	FGD, debriefing session	6	3	3	4	Fractional factorial	Orthogonal design (type: balance)	NA	NA	Unlabelled	Forced choice approach	Effects	No	Not stated	NA	Email, telephone
Fegert 2011^Citation46	Literature research, qualitative survey, and FGD	6	2	Not stated	No	Fractional factorial	Orthogonal design	NA	NA	Unlabelled	Forced choice approach	Not stated	No	Not stated	NA	Online, Paper
Glenngård 2013^Citation37	Previous study from the UK	5	4	8	2	Fractional factorial	Orthogonal design	NA	NA	Unlabelled	Forced choice approach	Not stated	Yes	Not stated	NA	Paper
Hugh 2022^Citation29	Questionnaire	5	Not stated	5	No	Not stated	Not stated	NA	NA	Labelled	Forced choice approach	Not stated	No	Not stated	NA	Web-based
Mühlbacher 2009^Citation38	Literature research and FGD	6	2	Not stated	No	Fractional factorial	Orthogonal design	NA	NA	Unlabelled	Not stated	Not stated	Yes	Not stated	NA	Online, Paper
Nafees 2014^Citation39	Literature review and expert opinion	7	3	18	No	Fractional factorial	Orthogonal design (type: array)	NA	NA	Unlabelled	Forced choice approach	Not stated	Yes	Not stated	NA	Online
Reiger 2009^Citation40	Literature review and expert opinion	3	4	16	No	Fractional factorial	Bayesian D-efficient design	Hierarchical Bayesian algorithm	2000 (type of draws not stated)	Unlabelled	Opt-out approach	Dummy	Yes	Not stated	NA	Email
Tsai 2021^Citation41	Communications among a community advisory board including patients, caregivers, and clinicians	4	2	12	No	Fractional factorial	D-efficient design	Not stated	NA	Unlabelled	Forced choice approach	Reverse	Yes	Yes	Not stated	Online
Waschbusch 2011^Citation42	Survey and expert discussions	18	3	27	No	Fractional factorial	Not stated	Not stated	Not stated	Unlabelled	Forced choice approach	Dummy	No	No	NA	Online
Webb 2019^Citation43	BWS, FGD, interview	9	4	12	5	Fractional factorial	D-efficient design	Not stated	NA	Labelled	Forced choice approach	Not stated	Yes	No	NA	Online
Wymbs 2016^Citation45	FGD	20	4	30	No	Fractional factorial	Not stated	Not stated	Not stated	Unlabelled	Forced choice approach	Not stated	No	No	NA	Email and paper-based
Wymbs 2017^Citation44	FGD	20	4	30	No	Fractional factorial	Not stated	Not stated	Not stated	Unlabelled	Forced choice approach	Not stated	No	No	NA	Email and paper-based

Notes: ^aValue of priors: each of the care types = 0.1, cash compensation attribute = −0.1. ^bPilot study.

Abbreviations: DCE, discrete choice experiment; FGD, focus group discussion; BWS, best-worst scale; NA, not applicable.

Table 3 Characteristics of the Analysis Methods and Most Preferred Attributes Identified in the Included DCE Studies (n=16)

Author and Year	Analysis Methods (Model Type)	Preference Heterogeneity (Yes/No)	Utility Specification	Variable Distribution	Post Estimation Calculations	Most Preferred Attributes Identified (Results)
Arora 2019^Citation35	Mixed logit and LCA	Yes	ASC	Normal distribution	WTA	Social support. WTA = $A35.96
Cross 2016^Citation28	Mixed logit	Yes	Not stated	Not stated	No	Controlling behavior, caring for oneself
Cunningham 2008^Citation27	MNL and LCA	Yes	Not stated	Not stated	No	Step-by-step solutions to behavioral or emotional problems, weekly meetings with other parents and coaching calls from a therapist
Cunningham 2009^Citation26	LCA	Yes	Not stated	Not stated	No	Active learning materials with parenting groups and therapist coaching calls supporting the knowledge transfer process
dosReis 2020^Citation36,^a	MNL	Not stated	Not stated	Not stated	No	Using fewer medications, maintaining decision-making authority
Fegert 2011^Citation46	Random-effects logit	Not stated	Not stated	Not stated	No	Social situation, emotional state
Glenngård 2013^Citation37	Random-effects logit	Not stated	Not stated	Not stated	WTP	Effectiveness, side-effects, and number of dosing per day. WTP = €790 for adolescents. Could not calculate for children.
Hugh 2022^Citation29	MNL	Yes	Not stated	Not stated	No	Naturalistic intervention (NI)
Mühlbacher 2009^Citation38	Logit/Probit	Not stated	Not stated	Not stated	No	Social situation, emotional state
Nafees 2014^Citation39	MNL	Yes	Not stated	Not stated	No	Degree of symptom control
Reiger 2009^Citation40	Mixed logit	Yes	Not stated	Normal distribution: attribute representing the number of children tested. Fixed: other attributes	WTP	Receiving a genetic diagnosis, waiting less time and spending less income. WTP = CDN$1118
Tsai 2021^Citation41	Mixed logit	Yes	Not stated	Not stated	No	Without hyperphagia = accept greater weight gain and a higher risk of skin rash. With hyperphagia = accept a higher risk of liver damage
Waschbusch 2011^Citation42	Mixed logit and LCA	Yes	Not stated	Not stated	No	Treatments with behavior therapy, desire to avoid medication
Webb 2019^Citation43	Mixed logit	Yes	ASC	Normal distribution	No	Future skills and abilities, child’s determination and persistence, communication ability with AAC, and receptive and expressive language
Wymbs 2016^Citation45	Mixed logit	Not stated	Not stated	Not stated	No	Individual parent training
Wymbs 2017^Citation44	Mixed logit	Not stated	Not stated	Not stated	No	Group parent training (when individual PT is not available)

Note: ^aPilot study.

Abbreviations: LCA, latent class analysis; MNL, multinomial logit; ASC, Alternative specific constant; WTA, Willingness to accept; WTP, Willingness to pay.

Figure 2 Types of analysis methods identified in the included studies [n = 16].

Abbreviations: MNL, multinomial logit; LCA, latent class analysis.

Table 4 Quality Assessment of the Included DCE Studies (n=16)

	Choice Task Design				Experimental Design	Conduct				Analysis
	Choice of Attributes and Levels Grounded in Qualitative Work with Target Population	No Conceptual Overlap Between Attributes	Uni-Dimensional Attributes	Inclusion of an Opt-Out or Status Quo Option or Justification of Forced Choice	Experimental Design Optimal or Statistically Efficient	Piloting Conducted Amongst Target Population	Target Population(s) Appropriate for Research Objective	Sampling Frame Representative of Target Population	Response Rate Sufficient to Minimise Response Bias	Any Pooled Analysis from Different Subgroups Appropriate	Econometric Model Appropriate for Choice Task Design	Econometric Model Accounts for Serial Correlation of Choices	Relative Attribute Effects Compared Using a Common Metric
Arora 2019^Citation35
Cross 2016^Citation28
Cunningham 2008^Citation27
Cunningham 2009^Citation26
dosReis 2020^Citation36
Fegert 2011^Citation46
Glenngård 2013^Citation37
Hugh 2022^Citation29
Mühlbacher 2009^Citation38
Nafees 2014^Citation39
Reiger 2009^Citation40
Tsai 2021^Citation41
Waschbusch 2011^Citation42
Webb 2019^Citation43
Wymbs 2016^Citation45
Wymbs 2017^Citation44

Notes: White, criteria met (manuscript’s text sufficiently confirmed the criteria); Black, criteria not met (not reported or no evidence to justify the criteria in text); Grey, not enough evidence to justify the criteria.

Arora S, Goodall S, Viney R, Einfeld S. Using discrete-choice experiment methods to estimate the value of informal care: the case of children with intellectual disability. Pharmacoeconomics. 2019;37(4):501–511. doi:10.1007/s40273-018-0637-2

 PubMed Web of Science ®Google Scholar

Cross J, Yang J-C, Johnson FR, et al. Caregiver preferences for the treatment of males with fragile X syndrome. J Dev Behavl Pediatr. 2016;37(1):71–79. doi:10.1097/DBP.0000000000000234

 PubMed Web of Science ®Google Scholar

Cunningham CE, Deal K, Rimas H, et al. Modeling the information preferences of parents of children with mental health problems: a discrete choice conjoint experiment. J Abnorm Child Psychol. 2008;36(7):1123–1138. doi:10.1007/s10802-008-9238-4

 PubMed Web of Science ®Google Scholar

Cunningham CE, Deal K, Rimas H, Chen Y, Buchanan DH, Sdao-Jarvie K. Providing information to parents of children with mental health problems: a discrete choice conjoint analysis of professional preferences. J Abnorm Child Psychol. 2009;37(8):1089–1102. doi:10.1007/s10802-009-9338-9

 PubMed Web of Science ®Google Scholar

dosReis S, Ng X, Frosch E, Reeves G, Cunningham C, Bridges JF. Using best–worst scaling to measure caregiver preferences for managing their child’s ADHD: a pilot study. Patient. 2015;8(5):423–431. doi:10.1007/s40271-014-0098-4

 PubMedGoogle Scholar

Fegert JM, Slawik L, Wermelskirchen D, Nuebling M, Muehlbacher A. Assessment of parents’ preferences for the treatment of school-age children with ADHD: a discrete choice experiment. Expert Rev Pharmacoecon Outcomes Res. 2011;11(3):245–252. doi:10.1586/erp.11.22

 PubMed Web of Science ®Google Scholar

Glenngård AH, Hjelmgren J, Thomsen PH, Tvedten T. Patient preferences and willingness-to-pay for ADHD treatment with stimulants using discrete choice experiment (DCE) in Sweden, Denmark and Norway. Nord J Psychiatry. 2013;67(5):351–359. doi:10.3109/08039488.2012.748825

 PubMed Web of Science ®Google Scholar

Hugh ML, Johnson LD, Cook C. Preschool teachers’ selection of social communication interventions for children with autism: an application of the theory of planned behavior. Autism. 2022;26(1):188–200. doi:10.1177/13623613211024795

 PubMed Web of Science ®Google Scholar

Mühlbacher AC, Rudolph I, Lincke H-J, Nübling M. Preferences for treatment of attention-deficit/hyperactivity disorder (ADHD): a discrete choice experiment. BMC Health Serv Res. 2009;9(1):1–10. doi:10.1186/1472-6963-9-149

 PubMedGoogle Scholar

Nafees B, Setyawan J, Lloyd A, et al. Parent preferences regarding stimulant therapies for ADHD: a comparison across six European countries. Eur Child Adolesc Psychiatry. 2014;23(12):1189–1200. doi:10.1007/s00787-013-0515-6

 PubMed Web of Science ®Google Scholar

Regier D, Friedman J, Makela N, Ryan M, Marra C. Valuing the benefit of diagnostic testing for genetic causes of idiopathic developmental disability: willingness to pay from families of affected children. Clin Genet. 2009;75(6):514–521. doi:10.1111/j.1399-0004.2009.01193.x

 PubMed Web of Science ®Google Scholar

Tsai J-H, Crossnohere NL, Strong T, Bridges JF. Measuring meaningful benefit-risk tradeoffs to promote patient-focused drug development in Prader-Willi syndrome: a discrete-choice experiment. MDM Policy Pract. 2021;6(2):23814683211039457. doi:10.1177/23814683211039457

 Google Scholar

Waschbusch DA, Cunningham CE, Pelham WE Jr, et al. A discrete choice conjoint experiment to evaluate parent preferences for treatment of young, medication naive children with ADHD. J Clin Child Adoles Psychol. 2011;40(4):546–561. doi:10.1080/15374416.2011.581617

 PubMed Web of Science ®Google Scholar

Webb EJ, Lynch Y, Meads D, et al. Finding the best fit: examining the decision-making of augmentative and alternative communication professionals in the UK using a discrete choice experiment. BMJ open. 2019;9(11):e030274. doi:10.1136/bmjopen-2019-030274

 PubMed Web of Science ®Google Scholar

Wymbs FA, Cunningham CE, Chen Y, et al. Examining parents’ preferences for group and individual parent training for children with ADHD symptoms. J Clin Child Adoles Psychol. 2016;45(5):614–631. doi:10.1080/15374416.2015.1004678

 PubMed Web of Science ®Google Scholar

Wymbs FA, Chen Y, Rimas HM, Deal K, Waschbusch DA, Pelham WE. Examining parents’ preferences for group parent training for ADHD when individual parent training is unavailable. J Child Fam Stud. 2017;26(3):888–904. doi:10.1007/s10826-016-0588-1

 Web of Science ®Google Scholar