scholarly journals Measuring Subgroup Preferences in Conjoint Experiments

2019 ◽  
Vol 28 (2) ◽  
pp. 207-221 ◽  
Author(s):  
Thomas J. Leeper ◽  
Sara B. Hobolt ◽  
James Tilley
Keyword(s):  
Conjoint Analysis ◽  
Best Practice ◽  
Causal Interpretation ◽  
Reference Category ◽  
Subgroup Differences ◽  
Political Preferences ◽  
Randomized Design ◽  
Marginal Means ◽  
Arbitrary Sign ◽  
Multidimensional Objects

Conjoint analysis is a common tool for studying political preferences. The method disentangles patterns in respondents’ favorability toward complex, multidimensional objects, such as candidates or policies. Most conjoints rely upon a fully randomized design to generate average marginal component effects (AMCEs). They measure the degree to which a given value of a conjoint profile feature increases, or decreases, respondents’ support for the overall profile relative to a baseline, averaging across all respondents and other features. While the AMCE has a clear causal interpretation (about the effect of features), most published conjoint analyses also use AMCEs to describe levels of favorability. This often means comparing AMCEs among respondent subgroups. We show that using conditional AMCEs to describe the degree of subgroup agreement can be misleading as regression interactions are sensitive to the reference category used in the analysis. This leads to inferences about subgroup differences in preferences that have arbitrary sign, size, and significance. We demonstrate the problem using examples drawn from published articles and provide suggestions for improved reporting and interpretation using marginal means and an omnibus F-test. Given the accelerating use of these designs in political science, we offer advice for best practice in analysis and presentation of results.

scholarly journals Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments

2014 ◽  
Vol 22 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Jens Hainmueller ◽  
Daniel J. Hopkins ◽  
Teppei Yamamoto
Keyword(s):  
Causal Inference ◽  
Conjoint Analysis ◽  
Stated Preference ◽  
Attitudes Toward Immigrants ◽  
Multiple Treatment ◽  
Stated Preference Experiments ◽  
Randomized Design ◽  
Survey Experiments ◽  
Voter Decision Making ◽  
Identification Analysis

Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show howconjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. In conjoint analysis, respondents score a set of alternatives, where each has randomly varied attributes. Here, we undertake a formal identification analysis to integrate conjoint analysis with the potential outcomes framework for causal inference. We propose a new causal estimand and show that it can be nonparametrically identified and easily estimated from conjoint data using a fully randomized design. The analysis enables us to propose diagnostic checks for the identification assumptions. We then demonstrate the value of these techniques through empirical applications to voter decision making and attitudes toward immigrants.

scholarly journals A Review of Perspectives on the Use of Randomization in Phase II Oncology Trials

2019 ◽  
Vol 111 (12) ◽  
pp. 1255-1262 ◽  
Author(s):  
Michael J Grayling ◽  
Munyaradzi Dimairo ◽  
Adrian P Mander ◽  
Thomas F Jaki
Keyword(s):  
Phase Ii ◽  
Best Practice ◽  
Progression Free Survival ◽  
Extensive Literature ◽  
Phase Ii Trials ◽  
Free Survival ◽  
Current Trends ◽  
Randomized Design ◽  
Oncology Trial ◽  
Current Design

AbstractHistorically, phase II oncology trials assessed a treatment’s efficacy by examining its tumor response rate in a single-arm trial. Then, approximately 25 years ago, certain statistical and pharmacological considerations ignited a debate around whether randomized designs should be used instead. Here, based on an extensive literature review, we review the arguments on either side of this debate. In particular, we describe the numerous factors that relate to the reliance of single-arm trials on historical control data and detail the trial scenarios in which there was general agreement on preferential utilization of single-arm or randomized design frameworks, such as the use of single-arm designs when investigating treatments for rare cancers. We then summarize the latest figures on phase II oncology trial design, contrasting current design choices against historical recommendations on best practice. Ultimately, we find several ways in which the design of recently completed phase II trials does not appear to align with said recommendations. For example, despite advice to the contrary, only 66.2% of the assessed trials that employed progression-free survival as a primary or coprimary outcome used a randomized comparative design. In addition, we identify that just 28.2% of the considered randomized comparative trials came to a positive conclusion as opposed to 72.7% of the single-arm trials. We conclude by describing a selection of important issues influencing contemporary design, framing this discourse in light of current trends in phase II, such as the increased use of biomarkers and recent interest in novel adaptive designs.

Does Conjoint Analysis Mitigate Social Desirability Bias?

2021 ◽  
pp. 1-15
Author(s):  
Yusaku Horiuchi ◽  
Zachary Markovich ◽  
Teppei Yamamoto
Keyword(s):  
Conjoint Analysis ◽  
Social Desirability ◽  
Alternative Model ◽  
Direct Evidence ◽  
Environmental Conservation ◽  
Evidence Based ◽  
Social Desirability Bias ◽  
Sensitive Topics ◽  
Randomized Design ◽  
Sensitive Attribute

Abstract How can we elicit honest responses in surveys? Conjoint analysis has become a popular tool to address social desirability bias (SDB), or systematic survey misreporting on sensitive topics. However, there has been no direct evidence showing its suitability for this purpose. We propose a novel experimental design to identify conjoint analysis’s ability to mitigate SDB. Specifically, we compare a standard, fully randomized conjoint design against a partially randomized design where only the sensitive attribute is varied between the two profiles in each task. We also include a control condition to remove confounding due to the increased attention to the varying attribute under the partially randomized design. We implement this empirical strategy in two studies on attitudes about environmental conservation and preferences about congressional candidates. In both studies, our estimates indicate that the fully randomized conjoint design could reduce SDB for the average marginal component effect (AMCE) of the sensitive attribute by about two-thirds of the AMCE itself. Although encouraging, we caution that our results are exploratory and exhibit some sensitivity to alternative model specifications, suggesting the need for additional confirmatory evidence based on the proposed design.

scholarly journals Strategies for Referring Cancer Patients in a Smoking Cessation Program

2020 ◽  
Vol 17 (17) ◽  
pp. 6089 ◽  
Author(s):  
James M. Davis ◽  
Leah C. Thomas ◽  
Jillian E. H. Dirkes ◽  
H. Scott Swartzwelder
Keyword(s):  
Smoking Cessation ◽  
Cancer Patients ◽  
Best Practice ◽  
Smoking Cessation Program ◽  
Cancer Center ◽  
Utilization Rate ◽  
Cancer Centers ◽  
Quit Smoking ◽  
Randomized Design ◽  
Practice Advisory

Most people who smoke and develop cancer are unable to quit smoking. To address this, many cancer centers have now opened smoking cessation programs specifically designed to help cancer patients to quit. An important question has now emerged—what is the most effective approach for engaging smokers within a cancer center in these smoking cessation programs? We report outcomes from a retrospective observational study comparing three referral methods—traditional referral, best practice advisory (BPA), and direct outreach—on utilization of the Duke Cancer Center Smoking Cessation Program. We found that program utilization rate was higher for direct outreach (5.4%) than traditional referral (0.8%), p < 0.001, and BPA (0.2%); p < 0.001. Program utilization was 6.4% for all methods combined. Inferring a causal relationship between referral method and program utilization was not possible because the study did not use a randomized design. Innovation is needed to generate higher utilization rates for cancer center smoking cessation programs.

scholarly journals Estimating and Using Individual Marginal Component Effects from Conjoint Experiments

2021 ◽  
pp. 1-14
Author(s):  
Kirill Zhirkov
Keyword(s):  
Conjoint Analysis ◽  
Task Design ◽  
Individual Level ◽  
Individual Preferences ◽  
Political Preferences ◽  
Conjoint Experiment ◽  
The Individual ◽  
Heterogeneity Of Preferences ◽  
Additional Assumptions

Abstract Conjoint experiments are quickly gaining popularity as a vehicle for studying multidimensional political preferences. A common way to explore heterogeneity of preferences estimated with conjoint experiments is by estimating average marginal component effects across subgroups. However, this method does not give the researcher the full access to the variation of preferences in the studied populations, as that would require estimating effects on the individual level. Currently, there is no accepted technique to obtain estimates of individual-level preferences from conjoint experiments. The present paper addresses this gap by proposing a procedure to estimate individual preferences as respondent-specific marginal component effects. The proposed strategy does not require any additional assumptions compared to the standard conjoint analysis, although some changes to the task design are recommended. Methods to account for uncertainty in resulting estimates are also discussed. Using the proposed procedure, I partially replicate a conjoint experiment on immigrant admission with recommended design adjustments. Then, I demonstrate how individual marginal component effects can be used to explore distributions of preferences, intercorrelations between different preference dimensions, and relationships of preferences to other variables of interest.

scholarly journals Age-treatment subgroup analyses in Cochrane intervention reviews: a meta-epidemiological study

BMC Medicine ◽  
2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Patrick Liu ◽  
John P. A. Ioannidis ◽  
Joseph S. Ross ◽  
Sanket S. Dhruva ◽  
Anita T. Luxkaranayagam ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Best Practice ◽  
Meta Analysis ◽  
Clinical Care ◽  
Subgroup Differences ◽  
Cochrane Reviews ◽  
Subgroup Analyses ◽  
Interaction Testing ◽  
Meta Analyses ◽  
Google Search

Abstract Background There is growing interest in evaluating differences in healthcare interventions across routinely collected demographic characteristics. However, individual subgroup analyses in randomized controlled trials are often not prespecified, adjusted for multiple testing, or conducted using the appropriate statistical test for interaction, and therefore frequently lack credibility. Meta-analyses can be used to examine the validity of potential subgroup differences by collating evidence across trials. Here, we characterize the conduct and clinical translation of age-treatment subgroup analyses in Cochrane reviews. Methods For a random sample of 928 Cochrane intervention reviews of randomized trials, we determined how often subgroup analyses of age are reported, how often these analyses have a P < 0.05 from formal interaction testing, how frequently subgroup differences first observed in an individual trial are later corroborated by other trials in the same meta-analysis, and how often statistically significant results are included in commonly used clinical management resources (BMJ Best Practice, UpToDate, Cochrane Clinical Answers, Google Scholar, and Google search). Results Among 928 Cochrane intervention reviews, 189 (20.4%) included plans to conduct age-treatment subgroup analyses. The vast majority (162 of 189, 85.7%) of the planned analyses were not conducted, commonly because of insufficient trial data. There were 22 reviews that conducted their planned age-treatment subgroup analyses, and another 3 reviews appeared to perform unplanned age-treatment subgroup analyses. These 25 (25 of 928, 2.7%) reviews conducted a total of 97 age-treatment subgroup analyses, of which 65 analyses (in 20 reviews) had non-overlapping subgroup levels. Among the 65 age-treatment subgroup analyses, 14 (21.5%) did not report any formal interaction testing. Seven (10.8%) reported P < 0.05 from formal age-treatment interaction testing; however, none of these seven analyses were in reviews that discussed the potential biological rationale or clinical significance of the subgroup findings or had results that were included in common clinical practice resources. Conclusion Age-treatment subgroup analyses in Cochrane intervention reviews were frequently planned but rarely conducted, and implications of detected interactions were not discussed in the reviews or mentioned in common clinical resources. When subgroup analyses are performed, authors should report the findings, compare the results to previous studies, and outline any potential impact on clinical care.

Meeting the Best Practice for Hearing Aid Verification in Children: Challenges and Future Directions

2019 ◽  
Vol 28 (4) ◽  
pp. 877-894
Author(s):  
Nur Azyani Amri ◽  
Tian Kar Quar ◽  
Foong Yen Chong
Keyword(s):  
Clinical Practice ◽  
Hearing Aids ◽  
Best Practice ◽  
Hearing Aid ◽  
Evidence Based ◽  
Future Directions ◽  
Major Barrier ◽  
Klang Valley ◽  
Age Range ◽  
Fitting Hearing Aids

Purpose This study examined the current pediatric amplification practice with an emphasis on hearing aid verification using probe microphone measurement (PMM), among audiologists in Klang Valley, Malaysia. Frequency of practice, access to PMM system, practiced protocols, barriers, and perception toward the benefits of PMM were identified through a survey. Method A questionnaire was distributed to and filled in by the audiologists who provided pediatric amplification service in Klang Valley, Malaysia. One hundred eight ( N = 108) audiologists, composed of 90.3% women and 9.7% men (age range: 23–48 years), participated in the survey. Results PMM was not a clinical routine practiced by a majority of the audiologists, despite its recognition as the best clinical practice that should be incorporated into protocols for fitting hearing aids in children. Variations in practice existed warranting further steps to improve the current practice for children with hearing impairment. The lack of access to PMM equipment was 1 major barrier for the audiologists to practice real-ear verification. Practitioners' characteristics such as time constraints, low confidence, and knowledge levels were also identified as barriers that impede the uptake of the evidence-based practice. Conclusions The implementation of PMM in clinical practice remains a challenge to the audiology profession. A knowledge-transfer approach that takes into consideration the barriers and involves effective collaboration or engagement between the knowledge providers and potential stakeholders is required to promote the clinical application of evidence-based best practice.

A Guide to Coding and Billing for the Audiological Management of Patients Receiving Ototoxic Medical Treatments

2019 ◽  
Vol 4 (5) ◽  
pp. 936-946
Author(s):  
Dawn Konrad-Martin ◽  
Neela Swanson ◽  
Angela Garinis
Keyword(s):  
Otoacoustic Emissions ◽  
Best Practice ◽  
Private Insurance ◽  
Monitoring Program ◽  
Current Method ◽  
International Classification Of Diseases ◽  
Ethical Practice ◽  
Party Payer ◽  
High Frequency Audiometry ◽  
Icd 10

Purpose Improved medical care leading to increased survivorship among patients with cancer and infectious diseases has created a need for ototoxicity monitoring programs nationwide. The goal of this report is to promote effective and standardized coding and 3rd-party payer billing practices for the audiological management of symptomatic ototoxicity. Method The approach was to compile the relevant International Classification of Diseases, 10th Revision (ICD-10-CM) codes and Current Procedural Terminology (CPT; American Medical Association) codes and explain their use for obtaining reimbursement from Medicare, Medicaid, and private insurance. Results Each claim submitted to a payer for reimbursement of ototoxicity monitoring must include both ICD-10-CM codes to report the patient's diagnosis and CPT codes to report the services provided by the audiologist. Results address the general 3rd-party payer guidelines for ototoxicity monitoring and ICD-10-CM and CPT coding principles and provide illustrative examples. There is no “stand-alone” CPT code for high-frequency audiometry, an important test for ototoxicity monitoring. The current method of adding a –22 modifier to a standard audiometry code and then submitting a letter rationalizing why the test was done has inconsistent outcomes and is time intensive for the clinician. Similarly, some clinicians report difficulty getting reimbursed for detailed otoacoustic emissions testing in the context of ototoxicity monitoring. Conclusions Ethical practice, not reimbursement, must guide clinical practice. However, appropriate billing and coding resulting in 3rd-party reimbursement for audiology services rendered is critical for maintaining an effective ototoxicity monitoring program. Many 3rd-party payers reimburse for these services. For any CPT code, payment patterns vary widely within and across 3rd-party payers. Standardizing coding and billing practices as well as advocacy including letters from audiology national organizations may be necessary to help resolve these issues of coding and coverage in order to support best practice recommendations for ototoxicity monitoring.

Business: The Electronic Health Record: Front Line Experience

2011 ◽  
Vol 21 (1) ◽  
pp. 18-22
Author(s):  
Rosemary Griffin
Keyword(s):  
Health Care ◽  
Information Technology ◽  
Electronic Health Record ◽  
Financial Incentives ◽  
Best Practice ◽  
Health Care Systems ◽  
The United States ◽  
Health Record ◽  
Health Trends ◽  
Electronic Health

National legislation is in place to facilitate reform of the United States health care industry. The Health Care Information Technology and Clinical Health Act (HITECH) offers financial incentives to hospitals, physicians, and individual providers to establish an electronic health record that ultimately will link with the health information technology of other health care systems and providers. The information collected will facilitate patient safety, promote best practice, and track health trends such as smoking and childhood obesity.

Reliability of the Conversational Interaction Coding Form When Applied to Natural Conversation of Individuals With Aphasia

2010 ◽  
Vol 20 (4) ◽  
pp. 125-135 ◽  
Author(s):  
Ashley Pozzolo Coote ◽  
Jane Pimentel
Keyword(s):  
Best Practice ◽  
Interobserver Reliability ◽  
Training Session ◽  
Clinical Tool ◽  
Group Setting ◽  
Conversational Interaction ◽  
Video Recordings ◽  
Aphasia Therapy ◽  
Natural Conversation ◽  
Point To Point

Purpose: Development of valid and reliable outcome tools to document social approaches to aphasia therapy and to determine best practice is imperative. The aim of this study is to determine whether the Conversational Interaction Coding Form (CICF; Pimentel & Algeo, 2009) can be applied reliably to the natural conversation of individuals with aphasia in a group setting. Method: Eleven graduate students participated in this study. During a 90-minute training session, participants reviewed and practiced coding with the CICF. Then participants independently completed the CICF using video recordings of individuals with non-fluent and fluent aphasia participating in an aphasia group. Interobserver reliability was computed using matrices representative of the point-to-point agreement or disagreement between each participant's coding and the authors' coding for each measure. Interobserver reliability was defined as 80% or better agreement for each measure. Results: On the whole, the CICF was not applied reliably to the natural conversation of individuals with aphasia in a group setting. Conclusion: In an extensive review of the turns that had high disagreement across participants, the poor reliability was attributed to inadequate rules and definitions and inexperienced coders. Further research is needed to improve the reliability of this potentially useful clinical tool.

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