"Yes", "No" or "Yes, but"? Multinomial modelling of NICE decision-making
Helen Angela Dakin1, Nancy J Devlin, Isaac A O Odeyemi
1Abacus International, 3-4 Market Square, Bicester, Oxon OX26 6AA, UK. helen.dakin@abacusint.com
Health Policy (Amsterdam, Netherlands)
|October 11, 2005
Summary
This study models National Institute for Health and Clinical Excellence (NICE) decisions beyond simple accept/reject. It reveals factors influencing routine, restricted, or non-recommendations for health technologies.
Area of Science:
- Health economics
- Health technology assessment
- Decision analysis
Background:
- The National Institute for Health and Clinical Excellence (NICE) provides crucial guidance on health technologies within the UK National Health Service (NHS).
- NICE's decision-making process considers clinical evidence and cost-effectiveness, but the explicit weighting and trade-offs of these factors remain unclear.
- Previous models simplified NICE decisions to a binary accept/reject outcome, potentially overlooking nuanced outcomes.
Purpose of the Study:
- To propose and validate an alternative model for NICE decision-making that captures the "yes, but..." nature of many appraisals.
- To analyze NICE decisions categorized as "recommended for routine use," "recommended for restricted use," or "not recommended."
Main Methods:
- Utilized multinomial logistic regression to model NICE appraisal decisions as a single choice among three categories.
- Evaluated the impact of clinical evidence quantity/quality, cost-effectiveness, decision date, availability of alternatives, budget impact, and technology type.
Main Results:
- Interventions with more randomized trials were more likely to receive routine recommendations.
- Higher cost-effectiveness ratios increased the likelihood of rejection over restricted use, but did not differentiate between routine and restricted use.
- Pharmaceuticals, early appraisals, and technologies with more systematic reviews were less likely to be rejected. Patient group submissions favored routine recommendations.
Conclusions:
- Modeling NICE decisions as three distinct outcomes (routine, restricted, not recommended) provides a more accurate representation than binary analyses.
- Specific factors influence the choice between restricted use and rejection, while others affect the distinction between routine and restricted use, highlighting the complexity of NICE appraisals.
More Related Videos
Related Concept Videos
01:09Decision Making: P-value Method
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
01:14Decision Making: Traditional Method
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
01:20Decision Making
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
Automatic decision-making is fast, intuitive, and relies on gut feelings...
01:22Multi-input and Multi-variable systems
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
In the absence of...
01:29Contingency Table
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...