Preferences for new and existing contraceptive products

Fiebig, Denzil G.; Knox, Stephanie; Viney, Rosalie; Haas, Marion; Street, Deborah J.

doi:10.1002/hec.1686

Cited by 36 publications

(33 citation statements)

References 14 publications

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“…Under the assumption that individuals are rational, DCEs can approximate which choice people would make given the option in reality [9,10]. PPA is common outside of health and is increasingly being applied by health economists to predict demand for a range of health-related choices, including HIV prevention products [11], contraceptive services [12], vaccination [13], and migraine treatments [9].…”

Section: Introductionmentioning

confidence: 99%

“…DCE predictions can be particularly useful for estimating the uptake of new products or services where observational data, from trials or pilot projects, are not available [12,13]. In the absence of observational data, for example in from a pragmatic trial or demonstration project, "expert opinion" is often used to generate uptake predictions, which are then commonly used to inform impact or cost-effectiveness models [14].…”

Section: Introductionmentioning

confidence: 99%

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How well do discrete choice experiments predict health choices? A systematic review and meta-analysis of external validity

et al. 2018

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Discrete choice experiments (DCEs) are economic tools that elicit the stated preferences of respondents. Because of their increasing importance in informing the design of health products and services, it is critical to understand the extent to which DCEs give reliable predictions outside of the experimental context. We systematically reviewed the literature of published DCE studies comparing predictions to choices made in reality; we extracted individual-level data to estimate a bivariate mixed-effects model of pooled sensitivity and specificity. Eight studies met the inclusion criteria, and six of these gave sufficient data for inclusion in a meta-analysis. Pooled sensitivity and specificity estimates were 88% (95% CI 81, 92%) and 34% (95% CI 23, 46%), respectively, and the area under the SROC curve (AUC) was 0.60 (95% CI 0.55, 0.64). Results indicate that DCEs can produce reasonable predictions of health-related behaviors. There is a great need for future research on the external validity of DCEs, particularly empirical studies assessing predicted and revealed preferences of a representative sample of participants.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How well do discrete choice experiments predict health choices? A systematic review and meta-analysis of external validity

et al. 2018

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“…For example, if data are being pooled across very different subsamples of respondents, scale heterogeneity would be an obvious concern; see for example [29]. If one is simulating the impact of the introduction of a new product or treatment as in [34] then it would be prudent to avoid MNL and allow for flexible substitution patterns.…”

Section: Post Estimationmentioning

confidence: 99%

“…The first is to simply harness the first (best) choice in each choice set, ignoring the additional choice data using models outlined above in Section 2.2 (as done by [33,34]). Alternatively, the additional preference information obtained from a BWDCE can be used to estimate discrete choice models by noting that the best and worst choice questions produce an implied rank order over alternatives which can be modelled with rank ordered logit (ROL) (e.g.…”

Section: Best-worst and Best-best Discrete Choice Modelsmentioning

confidence: 99%

Discrete Choice Experiments: A Guide to Model Specification, Estimation and Software

2017

Self Cite

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We provide a user guide on the analysis of data (including best-worst and best-best data) generated from discrete choice experiments (DCEs), comprising a theoretical review of the main choice models followed by practical advice on estimation and post estimation. We also provide a review of standard software. In providing this guide we endeavor not only to provide guidance on choice modeling, but to do so in a way that provides researchers to the practicalities of data analysis. We argue that choice of modeling approach depends on: the research questions; study design and constraints in terms of quality/quantity of data and that decisions made in relation to analysis of choice data are often interdependent rather than sequential. Given the core theory and estimation of choice models is common across settings, we expect the theoretical and practical content of this paper to be useful not only to researchers within but also beyond health economics. Compliance with Ethical StandardsNo funding was received for the preparation of this paper. Emily Lancsar is funded by an ARC Fellowship (DE1411260). Emily Lancsar, Denzil Fiebig and Arne Risa Hole have no conflicts of interest. 2 Key Points for Decision Makers We provide a user guide on the analysis of data, including best-worst and best-best data, generated from DCEs, addressing the questions of DCE  We provide a theoretical overview of the main choice models and review three standard statistical software packages: Stata; Nlogit; and Biogeme.  Choice of modeling approach depends on the research questions; study design and constraints in terms of quality/quantity of data and decisions made in relation to analysis of choice data are often interdependent rather than sequential.  A health based DCE example for which we provide the data and estimation code is used throughout to demonstrate the data set up, variable coding, various model estimation and post estimation approaches.3

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“…Another direction in the development of combined hormonal contraceptives is to introduce new forms of administration. Until recently, the vast majority of general public as well as some physicians could not imagine combined hormonal contraception in any form other than the daily pill [2].…”

Section: Introductionmentioning

confidence: 99%

Combined hormone contraceptive choice experience in Czech Republic

Fait

2011

Open Medicine

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AbstractThe aim of the prospective, multicenter project was to evaluate the effect of standardized information on the decision of woman when selecting application routes for combined hormonal contraceptives (CC). Selection the route of CC’s administration before and after consultation with the physician was evaluated on the group of 1326 women in 125 centres in the Czech Republic using a questionnaire. Analysis of the difference between the intended (4,1%) and selected (33,9%, CI 95% 31%–38%, 451 women) contraception shows that the vaginal ring preference increased by 29,8% (CI 97,5% 26,9%–32,8%, p < 0,0001). The difference for the weekly patch after (5,7%) and prior (4,2%) to the counseling of 1,4% was borderline statistically significant (CI 97,5%–0,002%–3,0%, p = 0,05). Preference of COC remained practically unchanged at 53,5%. Vaginal ring was selected by 45,2% of undecided women and 28,0% of women, who considered other than combined hormonal contraception. Easibility of application, efficacy and cycle control are the most important predictors for contraception choice. Following expert advice, including information on all forms of combined oral contraceptives, more than 33% of women chose the latest application form of combined hormonal contraceptives - vaginal ring.

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Preferences for new and existing contraceptive products

Cited by 36 publications

References 14 publications

How well do discrete choice experiments predict health choices? A systematic review and meta-analysis of external validity

How well do discrete choice experiments predict health choices? A systematic review and meta-analysis of external validity

Discrete Choice Experiments: A Guide to Model Specification, Estimation and Software

Combined hormone contraceptive choice experience in Czech Republic

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