Tag Archives: Data

Patient and Public Involvement in Data Collection

Further to last fortnight’s News blog article [1] I have found a further study in which patients participated in data collection.[2] This paper, by and large, corroborates the procedural requirements for public and patient involvement in data collection that I had specified. For example, it was necessary for lay observers to undergo DBS checks; the ethics approval form had to include lay observers; and training had to be arranged for the lay observers. Recruitment of lay observers proved more difficult than anticipated. The lay observers had a positive experience and brought a different perspective to the research according to feedback. The extent to which observer perspective is a good thing is, however, contestable. Generally I think the role of the observer is to collect data for analysis, and not colour it with a ‘perspective’. The professional researchers on the project felt that having lay researchers involved increased their workloads. The thorny issues of payment and selection do not seem to have been fully discussed in this paper. Also not discussed was the idea that, in qualitative research, respondents may be less inhibited to disclose information to a lay observer. Let the debate continue!

— Richard Lilford, CLAHRC WM Director

References:

  1. Lilford RJ. Patient and Public Involvement: Direct Involvement of Patient Representatives in Data Collection. NIHR CLAHRC West Midlands News Blog. 4 August 2017.
  2. Garfield S, Jheeta S, Jacklin A, Bischler A, Norton C, Franklin BD. Patient and public involvement in data collection for health services research: a descriptive study. Res Involve Engage. 2015; 1: 8.
Advertisements

Patient and Public Involvement: Direct Involvement of Patient Representatives in Data Collection

It is widely accepted that the public and patient voice should be heard loud and clear in the selection of studies, in the design of those studies, and in the interpretation and dissemination of the findings. But what about involvement of patient and the public in the collection of data? Before science became professionalised, all scientists could have been considered members of the public. Robert Hooke, for example, could have called himself architect, philosopher, physicist, chemist, or just Hooke. Today, the public are involved in data collection in many scientific enterprises. For example, householders frequently contribute data on bird populations, and Prof Brian Cox involved the public in the detection of new planets in his highly acclaimed television series. In medicine, patients have been involved in collecting data; for example patients with primary biliary cirrhosis were the data collectors in a randomised trial.[1] However, the topic of public and patient involvement in data collection is deceptively complex. This is because there are numerous procedural safeguards governing access to users of the health service and that restrict disbursement of the funds that are used to pay for research.

Let us consider first the issue of access to patients. It is not permissible to collect research data without undergoing certain procedural checks; in the UK it is necessary to be ratified by the Disclosure and Barring Service (DBS) and to have necessary permissions from the institutional authorities. You simply cannot walk onto a hospital ward and start handing out questionnaires or collecting blood samples.

Then there is the question of training. Before collecting data from patients it is necessary to be trained in how to do so, covering both salient ethical and scientific principles. Such training is not without its costs, which takes us to the next issue.

Researchers are paid for their work and, irrespective of whether the funds are publically or privately provided, access to payment is governed by fiduciary and equality/diversity legislation and guidelines. Access to scarce resources is usually governed by some sort of competitive selection process.

None of the above should be taken as an argument against patients and the public taking part in data collection. It does, however, mean that this needs to be a carefully managed process. Of course things are very much simpler if access to patients is not required. For example, conducting a literature survey would require only that the person doing it was technically competent and in many cases members of the public would already have all, or some, of the necessary skills. I would be very happy to collaborate with a retired professor of physics (if anyone wants to volunteer!). But that is not the point. The point is that procedural safeguards must be applied, and this entails management structures that can manage the process.

Research may be carried out by accessing members of the public who are not patients, or at least who are not accessed through the health services. As far as I know there are no particular restrictions on doing so, and I guess that such contact is governed by the common law covering issues such as privacy, battery, assault, and so on. The situation becomes different, however, if access is achieved through a health service organisation, or conducted on behalf of an institution, such as a university. Then presumably any member of the public wishing to collect data from other members of the public would fall under the governance arrangements of the relevant institution. The institution would have to ensure not only that the study was ethical, but that the data-collectors had the necessary skills and that funds were disbursed in accordance with the law. Institutions already deploy ‘freelance’ researchers, so I presume that the necessary procedural arrangements are already in place.

This analysis was stimulated by a discussion in the PPI committee of CLAHRC West Midlands, and represents merely my personal reflections based on first principles. It does not represent my final, settled position, let alone that of the CLAHRC WM, or any other institution. Rather it is an invitation for further comment and analysis.

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Browning J, Combes B, Mayo MJ. Long-term efficacy of sertraline as a treatment for cholestatic pruritus in patients with primary biliary cirrhosis. Am J Gastroenterol. 2003; 98: 2736-41.

An Intriguing Suggestion to Link Trial Data to Routine Data

When extrapolating from trial data to a particular context, it is important to compare the trial population to the target population. Given sufficient data, it is possible to examine treatment effect across important subgroups of patients. Then the trial results can be related to a specific sub-group, say with less severe disease than the average in the trial. One problem is that trial data are collected with greater diligence than routine data. Hence a suggestion to link trial data to routine data collected on the same patients. That way one can compare subgroups of trial and non-trial patients recorded in a broadly similar (i.e. routine) way.[1] This strikes me as a half-way house to the day when (most) trial data are collected by routine systems, and trials are essentially nested within routine data-collection systems.

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Najafzadeh M, Schneeweiss S. From Trial to Target Populations – Calibrating Real-World Data. N Engl J Med. 2017; 376: 1203-4.

Ethics of Using Other Researcher’s Data

It is good practice to make data collected in research projects available to others. A recent editorial in the New England Journal of Medicine [1] says that just using such data is ‘parasitical’, and suggest that researchers who use archived data should collaborate with the researchers who collected the original data. The CLAHRC WM Director disagrees. While there may be times when collaboration with the originators of the data is a good idea, it should not be expected or required. The original researchers are ‘invested’, and in many occasions it is in the public and scientific interest for the new investigations to maintain independence. It is best that data ‘gifted’ to the research community should be just that – a gift. Also, the original researchers might have lost interest, retired or died. Reinhart and Rogoff’s magisterial database of economic data is a case in point.[2] Independent re-analysis of the data they had collected and magnanimously made available to other researchers sometimes produced conclusions different to the original.

— Richard Lilford, CLAHRC WM Director

References:

  1. Longo DL, & Drazen JM. Data Sharing. New Engl J Med. 2016; 374: 276-7.
  2. Reinhart CM, & Rogoff KS. Growth in a Time of Debt. Am Econ Rev. 2010; 100(2): 573-8.

I Agree with Fiona

Dr Fiona Godlee, Editor-in-Chief of the BMJ, recently published a piece arguing that ‘data transparency is the only way‘.[1] This News Blog has featured a number of posts where many large RCTs have left a matter in contention – deworming children, clot busters for stroke, and vitamin A prophylaxis in children. When this happens, a dispute typically opens up about nuances in the handling of the data that might have introduced bias; bias so small that it is only material when the trials are large and hence the confidence limits narrow. The right policy is to stick the anonymised data in the public domain so that everyone can have a go at it. What is not okay, is to assume that one lot have the moral high ground – not industry, nor academics, nor editors, nor even CLAHRC Directors!

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Godlee F. Data transparency is the only way. BMJ. 2016; 352: i1261.

Another Study of Studies: Effectiveness using Routinely Collected Health Data vs. RCT Data

News Blog readers will be familiar with previous meta-epidemiological studies comparing effectiveness of the same treatment when evaluated using Routinely Collected Data (RCD) vs. prospective experiments (RCTs).[1] [2] Here is another such study from John Ioannidis, the world’s number one clinical epidemiologist – masterful.[3]
The RCD studies all:

  1. Preceded their complimentary RCTs
  2. Used prediction score modelling, and
  3. Had mortality as their main outcome.

Sixteen primary routine database studies were complimented by a mean of just over two subsequent RCTs examining the same clinical question. The findings here are not as sanguine regarding database studies as those cited in previous posts. The direction of effect was different in five (30%) of the 16 studies; confidence intervals in nine (59%) of the database studies did not include the observed effect in complimentary RCTs and, where they differed, the database studies tended to over-estimate treatment effects relative to the RCT estimate by a substantial 30%. This re-informs the perceived wisdom – experimental studies are the gold standard and database studies should not supplant them.

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Lilford RJ. Yet Again RCTs Fail to Confirm Promising Effects of Dietary Factors in Observational Studies. CLAHRC WM News Blog. 25 September 2015.
  2. Lilford RJ. Very Different Results from RCT and Observational Studies? CLAHRC WM News Blog. 25 September 2015.
  3. Hemkens LG, Contopoulos-Ioannidis DG, Ioannidis JPA. Agreement of treatment effects for mortality from routinely collected data and subsequent randomized trials: meta-epidemiological survey. BMJ. 2016; 352: i493.

Big Data in Health Improvement

Big data – which the CLAHRC WM Director takes to be large datasets (terabytes or more) that have been collected from more than one source – have great potential use in health improvement according to a recent article.[1] However, the article provides few examples, and those that are mentioned are parochial, relating how an individual hospital improved performance on the basis of local audit. Hardly big data. A better example would come from improvements in diabetes care in Scotland based on the National data-linkage system.[2] The CLAHRC WM Director suspects there are many others and asks readers to send examples.

— Richard Lilford, CLAHRC WM Director

References:

  1. Wyber R, Vaillancourt S, Perry W, Mannava P, Folaranmi T, Celi LA. Big data in global health: improving health in low- and middle-income countries. Bull World Health Organ. 2015; 93(3): 203-8.
  2. Cunningham S, McAlpine R, Leese R, et al. Using web technology to support population-based diabetes care. J Diabetes Sci Technol. 2011; 5(3): 523-34.