Related Experiment Video
Updated: Feb 7, 2026

Myocardial Infarction and Functional Outcome Assessment in Pigs
Published on: April 25, 2014
ASSESSING THE VALUE OF INNOVATIVE MEDICAL DEVICES AND DIAGNOSTICS: THE IMPORTANCE OF CLEAR AND RELEVANT CLAIMS OF
Bruce Campbell1, Mark Campbell1, Lee Dobson1
1NICE.
Objectives:
Large numbers of new medical devices and diagnostics are developed and health services need to identify which ones offer real advantages. The National Institute for Health and Care Excellence (NICE) has introduced a system for assessing technologies that are often notified by companies, based on claims made for their benefits to patients, the National Health Service, and the environment.
Methods:
Detailed scrutiny of claims made for the benefits of products and the corresponding evidence, seeking associations between these and the selection of products for full evaluation to produce NICE guidance.
Results:
Between 2009 and 2015 a NICE committee considered 169 technologies, of which it selected 74 (44 percent) for full evaluation, based on the claims of benefit and the evidence available. An average of 7.5 claims were made per technology; the total number did not influence selection but presence of studies supporting all the claims (p < .001) or any of the claims (p < .05) had a positive influence, as did claims for quicker patient recovery (p < .001). A greater number of studies to support the claims made selection more likely (p < .001), as did cohort studies (p < .05) and surveys (p < .05) but, unexpectedly, not randomized trials. The Medical Device Directive class had no influence.
Conclusions:
This study presents categories of claims that may be useful to those developing new products and to others engaged in health technology assessment. It illustrates the importance of relevant evidence and of having a clear vision of the place of new products in care pathways from an early stage.
Related Concept Videos
Nephrotic Syndrome II : Assessment and Medical Management
Benefits of Self-Esteem
Testing a Claim about Mean: Known Population SD
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
Testing a Claim about Population Proportion
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Testing a Claim about Standard Deviation
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Testing a Claim about Mean: Unknown Population SD
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...

