Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2012

Open Access 01-12-2012 | Research article

Decision-making in healthcare: a practical application of partial least square path modelling to coverage of newborn screening programmes

Author: Katharina E Fischer

Published in: BMC Medical Informatics and Decision Making | Issue 1/2012

Login to get access

Abstract

Background

Decision-making in healthcare is complex. Research on coverage decision-making has focused on comparative studies for several countries, statistical analyses for single decision-makers, the decision outcome and appraisal criteria. Accounting for decision processes extends the complexity, as they are multidimensional and process elements need to be regarded as latent constructs (composites) that are not observed directly. The objective of this study was to present a practical application of partial least square path modelling (PLS-PM) to evaluate how it offers a method for empirical analysis of decision-making in healthcare.

Methods

Empirical approaches that applied PLS-PM to decision-making in healthcare were identified through a systematic literature search. PLS-PM was used as an estimation technique for a structural equation model that specified hypotheses between the components of decision processes and the reasonableness of decision-making in terms of medical, economic and other ethical criteria. The model was estimated for a sample of 55 coverage decisions on the extension of newborn screening programmes in Europe. Results were evaluated by standard reliability and validity measures for PLS-PM.

Results

After modification by dropping two indicators that showed poor measures in the measurement models’ quality assessment and were not meaningful for newborn screening, the structural equation model estimation produced plausible results. The presence of three influences was supported: the links between both stakeholder participation or transparency and the reasonableness of decision-making; and the effect of transparency on the degree of scientific rigour of assessment. Reliable and valid measurement models were obtained to describe the composites of ‘transparency’, ‘participation’, ‘scientific rigour’ and ‘reasonableness’.

Conclusions

The structural equation model was among the first applications of PLS-PM to coverage decision-making. It allowed testing of hypotheses in situations where there are links between several non-observable constructs. PLS-PM was compatible in accounting for the complexity of coverage decisions to obtain a more realistic perspective for empirical analysis. The model specification can be used for hypothesis testing by using larger sample sizes and for data in the full domain of health technologies.
Appendix
Available only for authorised users
Literature
1.
Sorenson C: Use of comparative effectiveness research in drug coverage and pricing decisions: a six-country comparison. Issue Brief (Commonw Fund). 2010, 91: 1-14.
2.
Taylor RS, Drummond MF, Salkeld G, Sullivan SD: Inclusion of cost effectiveness in licensing requirements of new drugs: the fourth hurdle. BMJ. 2004, 329: 972-975. 10.1136/bmj.329.7472.972.CrossRefPubMedPubMedCentral
3.
Chapman GB, Sonnenberg FA: Decision Making in Health Care. 2000, Cambridge University Press, Cambridge
4.
Rogowski WH, Hartz SC, John JH: Clearing up the hazy road from bench to bedside: a framework for integrating the fourth hurdle into translational medicine. BMC Health Serv Res. 2008, 8: 194-10.1186/1472-6963-8-194.CrossRefPubMedPubMedCentral
5.
Guasch JL, Hahn RW: The costs and benefits of regulation: implications for developing countries. World Bank Research Observer. 1999, 14: 137-158. 10.1093/wbro/14.1.137.CrossRef
6.
Wettermark B, Godman B, Neovius M, Hedberg N, Mellgren TO, Kahan T: Initial effects of a reimbursement restriction to improve the cost-effectiveness of antihypertensive treatment. Health Policy. 2010, 94: 221-229. 10.1016/j.healthpol.2009.09.014.CrossRefPubMed
7.
Lundkvist J, Jonsson B, Rehnberg C: The costs and benefits of regulations for reimbursement of new drugs. Health Policy. 2006, 79: 337-344. 10.1016/j.healthpol.2006.02.004.CrossRefPubMed
8.
Stafinski T, Menon D, Philippon DJ, McCabe CJ: Health technology funding decision-making processes around the world: the same, yet different. PharmacoEconomics. 2011, 29: 475-495. 10.2165/11586420-000000000-00000.CrossRefPubMed
9.
Vuorenkoski L, Toiviainen H, Hemminki E: Decision-making in priority setting for medicines – a review of empirical studies. Health Policy. 2008, 86: 1-9. 10.1016/j.healthpol.2007.09.007.CrossRefPubMed
10.
Dakin HA, Devlin NJ, Odeyemi IA: 'Yes', 'No' or 'Yes, but'? Multinomial modelling of NICE decision-making. Health Policy. 2006, 77: 352-367. 10.1016/j.healthpol.2005.08.008.CrossRefPubMed
11.
Devlin N, Parkin D: Does NICE have a cost-effectiveness threshold and what other factors influence its decisions? A binary choice analysis. Health Econ. 2004, 13: 437-452. 10.1002/hec.864.CrossRefPubMed
12.
Segal L, Dalziel K, Duncan M: Fixing the game: are between-silo differences in funding arrangements handicapping some interventions and giving others a head-start?. Health Econ. 2009, 19: 449-465.CrossRef
13.
Harris AH, Hill SR, Chin G, Li JJ, Walkom E: The role of value for money in public insurance coverage decisions for drugs in Australia: a retrospective analysis 1994–2004. Med Decis Making. 2008, 28: 713-722. 10.1177/0272989X08315247.CrossRefPubMed
14.
Clement FM, Harris A, Jing JL, Yong K, Lee KM, Manns BJ: Using effectiveness and cost-effectiveness to make drug coverage decisions: A comparison of Britain, Australia, and Canada. JAMA. 2009, 302: 1437-1443. 10.1001/jama.2009.1409.CrossRefPubMed
15.
Kanavos P, Nicod E, van den Aardweg S, Pomedli S: The impact of health technology assessments: an international comparison. Euro Observer. 2010, 12: 1-7.
16.
Mason AR, Drummond MF, Ramsey S, Campbell J, Raisch D: Comparison of Anticancer Drug Coverage Decisions in the United States and United Kingdom: Does the Evidence Support the Rhetoric?. J Clin Oncol. 2010, 28: 3234-3238. 10.1200/JCO.2009.26.2758.CrossRefPubMed
17.
Daniels N, Sabin J: The ethics of accountability in managed care reform. Health Aff. 1998, 17: 50-64. 10.1377/hlthaff.17.5.50.CrossRef
18.
Erntoft S: Pharmaceutical priority setting and the use of health economic evaluations: a systematic literature review. Value Health. 2011, 14: 587-599. 10.1016/j.jval.2010.10.036.CrossRefPubMed
19.
Hair J, Ringle CM, Sarstedt M: PLS-SEM: Indeed a Silver Bullet. J Marketing Theory Pract. 2011, 19: 139-152. 10.2753/MTP1069-6679190202.CrossRef
20.
Henseler J, Ringle CM, Sinkovics RR: The use of partial least squares path modeling in international marketing. Advances in International Marketing. Edited by: Sinkovics RR, Ghauri PN. 2009, Emerald, Bingley, 277-320. Volume 20CrossRef
21.
Downey AM, Sharp DJ: Why do managers allocate resources to workplace health promotion programmes in countries with national health coverage?. Health Promot Int. 2007, 22: 102-111. 10.1093/heapro/dam002.CrossRefPubMed
22.
Walter Z, Lopez MS: Physician acceptance of information technologies: role of perceived threat to professional autonomy. Decis Support Syst. 2008, 46: 206-215. 10.1016/j.dss.2008.06.004.CrossRef
23.
Ringle CM, Wende S, Will A: SmartPLS 2.0. , , , http://​www.​smartpls.​com
24.
Fischer KE, Leidl R, Rogowski WH: A structured tool to analyse coverage decisions: development and feasibility test in the field of cancer screening and prevention. Health Policy. 2011, 101: 290-299. 10.1016/j.healthpol.2011.03.002.CrossRefPubMed
25.
Lovaglio P: Model building and estimation strategies for implementing the Balanced Scorecard in Health sector. Qual Quant. 2011, 45: 199-212. 10.1007/s11135-010-9364-2.CrossRef
26.
Vigneau E, Qannari E: Clustering of Variables Around Latent Components. Communications in Statistics - Simulation and Computation. 2003, 32: 1131-1150. 10.1081/SAC-120023882.CrossRef
27.
Hutton J, McGrath C, Frybourg JM, Tremblay M, Bramley-Harker E, Henshall C: Framework for describing and classifying decision-making systems using technology assessment to determine the reimbursement of health technologies (fourth hurdle systems). Int J Technol Assess Health Care. 2006, 22: 10-18.CrossRefPubMed
28.
Persad G, Wertheimer A, Emanuel EJ: Principles for allocation of scarce medical interventions. Lancet. 2009, 373: 423-431. 10.1016/S0140-6736(09)60137-9.CrossRefPubMed
29.
30.
Baltussen R, Niessen L: Priority setting of health interventions: the need for multi-criteria decision analysis. Cost Eff Resour Alloc. 2006, 4: 14-10.1186/1478-7547-4-14.CrossRefPubMedPubMedCentral
31.
Sibbald SL, Singer PA, Upshur R, Martin DK: Priority setting: what constitutes success? A conceptual framework for successful priority setting. BMC Health Serv Res. 2009, 9: 43-10.1186/1472-6963-9-43.CrossRefPubMedPubMedCentral
32.
Golan O, Hansen P, Kaplan G, Tal O: Health technology prioritization: which criteria for prioritizing new technologies and what are their relative weights?. Health Policy. 2011, 102: 126-135. 10.1016/j.healthpol.2010.10.012.CrossRefPubMed
33.
Mitton CR, McMahon M, Morgan S, Gibson J: Centralized drug review processes: are they fair?. Soc Sci Med. 2006, 63: 200-211. 10.1016/j.socscimed.2005.11.049.CrossRefPubMed
34.
Menon D, Stafinski T: Role of patient and public participation in health technology assessment and coverage decisions. Expert Rev Pharmacoecon Outcomes Res. 2011, 11: 75-89. 10.1586/erp.10.82.CrossRefPubMed
35.
Abelson J, Giacomini M, Lehoux P, Gauvin FP: Bringing 'the public' into health technology assessment and coverage policy decisions: from principles to practice. Health Policy. 2007, 82: 37-50. 10.1016/j.healthpol.2006.07.009.CrossRefPubMed
36.
Bryan S, Williams I, McIver S: Seeing the NICE side of cost-effectiveness analysis: a qualitative investigation of the use of CEA in NICE technology appraisals. Health Econ. 2007, 16: 179-193. 10.1002/hec.1133.CrossRefPubMed
37.
Fischer KE, Grosse SD, Rogowski W: The Role of Health Technology Assessment in Coverage Decisions on Newborn Screening. Int J Technol Assess Health Care. 2011, 27: 305-312. 10.1017/S0266462311000444.CrossRef
38.
SAS Institute Inc: SAS version 9.2. 2008, Cary, NC, USA
39.
Götz O, Liehr-Gobbers K, Krafft M: Evaluation of structural equation models using the partial least squares (PLS) approach. Handbook of Partial Least Squares. Edited by: Esposito Vinzi V, Chin WW, Henseler J, Wang H. 2010, Springer Handbooks of Computational Statistics, Springer Berlin Heidelberg, 691-711.CrossRef
40.
Tenenhaus M, Vinzi VE, Chatelin Y-M, Lauro C: PLS path modeling. Computational Statistics & Data Analysis. 2005, 48: 159-205. 10.1016/j.csda.2004.03.005.CrossRef
41.
Grosse SD: Economic evaluations of newborn screening. Economic evaluation in Child Health. Edited by: Ungar WJ. 2009, Oxford University Press, New York, 113-132.CrossRef
42.
43.
Hulland J: Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strateg Manag J. 1999, 20: 195-204. 10.1002/(SICI)1097-0266(199902)20:2<195::AID-SMJ13>3.0.CO;2-7.CrossRef
44.
OECD: Chapter 4: Decision making and implementation: an analysis of survey results. Health Technologies and Decision Making. 2005, , Paris, 71-94.CrossRef
45.
Barbieri M, Hawkins N, Sculpher MJ: Who Does the Numbers? The Role of Third-Party Technology Assessment to Inform Health Systems' Decision-Making about the Funding of Health Technologies. Value Health. 2009, 12: 193-201. 10.1111/j.1524-4733.2008.00441.x.CrossRefPubMed
46.
Stafinski T, Menon D, Davis C, McCabe C: Role of centralized review processes for making reimbursement decisions on new health technologies in Europe. Clinicoecon Outcomes Res. 2011, 3: 117-186.CrossRefPubMedPubMedCentral
47.
Scholl I, Koelewijn-van Loon M, Sepucha K, Elwyn G, Legare F, Harter M, Dirmaier J: Measurement of shared decision making – a review of instruments. Z Evid Fortbild Qual Gesundhwes. 2011, 105: 313-324. 10.1016/j.zefq.2011.04.012.CrossRefPubMed
48.
Wu WW: Linking Bayesian networks and PLS path modeling for causal analysis. Expert Syst Appl. 2010, 37: 134-139. 10.1016/j.eswa.2009.05.021.CrossRef
49.
Jakobowicz E, Derquenne C: A modified PLS path modeling algorithm handling reflective categorical variables and a new model building strategy. Comput Stat Data An. 2007, 51: 3666-3678. 10.1016/j.csda.2006.12.004.CrossRef
50.
Rigdon EE, Ringle CM, Sarstedt M: Structural modeling of heterogeneous data with partial least squares. Review of Marketing Research. Edited by: Malhotra NK. 2010, Emerald Group Publishing Limited, Armonk, 255-296. Volume 7
51.
Deb P, Gallo WT, Ayyagari P, Fletcher JM, Sindelar JL: The effect of job loss on overweight and drinking. J Health Econ. 2011, 30: 317-327. 10.1016/j.jhealeco.2010.12.009.CrossRefPubMedPubMedCentral
52.
Sibbald SL, Gibson JL, Singer PA, Upshur R, Martin DK: Evaluating priority setting success in healthcare: a pilot study. BMC Health Serv Res. 2010, 10: 131-10.1186/1472-6963-10-131.CrossRefPubMedPubMedCentral
53.
Rid A: Justice and procedure: how does 'accountability for reasonableness' result in fair limit-setting decisions?. J Med Ethics. 2009, 35: 12-16. 10.1136/jme.2008.024430.CrossRefPubMed
54.
Gauvin FP, Abelson J, Giacomini M, Eyles J, Lavis JN: 'It all depends': conceptualizing public involvement in the context of health technology assessment agencies. Sco Sci Med. 2010, 70: 1518-1526.CrossRef
55.
McMahon M, Morgan S, Mitton C: The Common Drug Review: a NICE start for Canada?. Health Policy. 2006, 77: 339-351. 10.1016/j.healthpol.2005.08.006.CrossRefPubMed
56.
Morgan SG, McMahon M, Mitton C, Roughead E, Kirk R, Kanavos P, Menon D: Centralized drug review processes in Australia, Canada, New Zealand, and the United Kingdom. Health Aff (Millwood). 2006, 25: 337-347. 10.1377/hlthaff.25.2.337.CrossRef
Metadata
Title
Decision-making in healthcare: a practical application of partial least square path modelling to coverage of newborn screening programmes
Author
Katharina E Fischer
Publication date
01-12-2012
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2012
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/1472-6947-12-83

Other articles of this Issue 1/2012

BMC Medical Informatics and Decision Making 1/2012 Go to the issue

Research article

Identification of methicillin-resistant Staphylococcus aureus within the Nation’s Veterans Affairs Medical Centers using natural language processing

Correspondence

Evaluating genomic tests from bench to bedside: a practical framework

Research article

Implementation of automated reporting of estimated glomerular filtration rate among Veterans Affairs laboratories: a retrospective study

Correspondence

An evaluation of the content and quality of tinnitus information on websites preferred by General Practitioners

Research article

Applying data mining techniques to improve diagnosis in neonatal jaundice

Research article

What is needed to implement a computer-assisted health risk assessment tool? An exploratory concept mapping study