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Can Doctors and Nurses help Dialysis patients recover? John Pickering Nov 07

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In the case of dialysis dependent acute kidney injury patients this is a question which Dr Dinna Cruz  and colleagues (University of California San Diego) are asking and seeking opinions from both nephrologists and non-nephrologist doctors and nurses involved in care of dialysis patients.  It was a question which arose out of discussions at this year’s Continuous Renal Replacement Therapies conference (CRRT 2014). Personally, I think it is a brilliant starting point for research to go out and seek the opinion of those “at the coal face” actually treating patients. If that includes you, please take a moment to complete the survey. If it includes someone you know, please pass this request to participate on.  Here is Dr Cruz’s request:

Currently there is much interest regarding the recovery aspect of AKI. A specific area of interest is how to enhance recovery in patients who remain dialysis-dependent at the time of discharge. It is hypothesized that patients with potential for renal recovery may require a different care plan than the “usual” ESRD patient.

Therefore we are asking your opinion regarding the post-discharge care of such patients, using this short survey. It will take only a few minutes of your time, and represents a starting point for developing potential strategies for these patients. We think it is very important to have the input of specialists from different healthcare settings and countries to give a more balanced view.

Kindly complete the survey appropriate for your specialty, then please share both these links with other colleagues so we get more responses from around the world

For nephrologists:

https://www.surveymonkey.com/s/postdischAKIcare_neph

For non-nephrologists, including acute and chronic dialysis nurses:

https://www.surveymonkey.com/s/postdischAKIcare

Thank you very much for your help!

Source: Anna Frodesiak-Wikimedia Commons

Source: Anna Frodesiak-Wikimedia Commons

Tagged: Acute Kidney Injury, Acute Renal Failure, chronic kidney injury, Dialysis, nephrologists, Nephrology, nurses, Research, survey, UC San Diego

$20bn for Medical Research! John Pickering May 15

Alas, not in New Zealand, but close … our Australian counterparts in medical research appear on the face of it to have scored big in what appears otherwise to be a grim Australian budget.  An AUD$20bn medical research “future fund” is to be established. This effectively means that by 2022-3 there will be twice the current budget available for medical research per annum (i.e. about $1bn).  How this will be divided up remains to be seen, but I note that Prof Mike Daub of Curtin University is suspicious that it is “Medical Research” not “Health and Medical Research.”

If this truly is a massive boost to medical research in Australia, what could it mean to New Zealand?

A negative possibility is that because there are already issues with recruiting medical specialists who wish to undertake research in New Zealand and because the Australian NHMRC already has successful contestable grant funding rates about twice that of New Zealand’s HRC (~16% cf ~7%), I expect there would be more one-way traffic of scientists to Australia. It is imperative that this be avoided, for all our health’s sake.

If, though, the funding recognises the value of collaborative research then it may be possible for New Zealand scientists to work more closely with their Australian counterparts on projects of mutual interest.  To that end, the New Zealand Government has (now) a great opportunity under CER to facilitate collaboration.  Perhaps, a dedicated fund that would support New Zealand researchers financially to play a role in Australian led research.  Apart from the high quality of NZ researchers (!), New Zealand should appeal to Australia because of the better integration of our health systems, especially with respect to tracing patient hospital events nationally, and because of the lower costs of doing research here.  Furthermore, health consumers in New Zealand demand the best (I know I do!) and the best is only available through research – ultimately more research across the ditch will benefit us here.  Thanks Tony.

______

ps. Catching the early flight to Sydney tomorrow to share some Trans-Tasman love and collaborate with my medical research colleagues at the Prince of Wales Hospital and the Royal Brisbane & Women’s Hospital.

Tagged: Australia, budget, future fund, HRC, medical research, NHMRC, Tony Abbott

A day to celebrate John Pickering Mar 13

If it weren’t for your kidneys where would you be?

You’d be in the hospital or infirmary,

If you didn’t have two functioning kidneys.

(with apologies to John Clarke aka Fred Dagg)

Happy World Kidney Day everyone.

This blog started off life as $100 Dialysis because I believe that if we can make a computer for $100 then surely we can do the same for dialysis!  Dialysis is a life saver, yet its cost kills as so many can not afford the treatment.

There’s some good news in the dialysis world.

Schematics of the zeolite nanonfibres and how they may look in practice

Schematics of the zeolite nanonfibres and how they may look in practice

Just last week the MANA – International Centre for Materials NanoArchitectionics announced  they have developed a method to remove waste from the blood using an easy-to-produce nanofibre mesh.  Importantly, they claim it is cheap to produce.  Details were published in Biomaterials Science (free access).  Despite the photograph, there have been no human studies yet, but I expect that won’t be too long in the future.

Dr Victor Gura and the Wearable Artificial Kidney (WAK)

Dr Victor Gura and the Wearable Artificial Kidney (WAK)

In the meantime, the FDA gave approval last month for human trials of a wearable dialysis device produced by Blood Purification Technologies Inc (the WAK).

New Zealand, and Dunedin and Christchurch in particular, lead the way in Home Dialysis.  One Dunedin tradesman has even taken Home Dialysis a step further and turned it into portable dialysis by dialysing in his work van during his lunch hour. Of course, those needing a holiday may go on the road in specially equipped camper vans (http://www.kidneys.co.nz/Kidney-Disease/Holiday-Dialysis/).

Cause for celebration in the New Zealand kidney community was the gong (Office of the New Zealand Order of Merit) given to Adrian Buttimore who for 40 years managed Christchurch’s dialysis service.

These are just a few pieces of good news as doctors and scientists work around the world to improve the lives of dialysis patients.

_________________

Hot off the Press… I couldn’t resist adding this…. Pee, the answer to the world’s energy problems. http://www.bbc.com/future/story/20140312-is-pee-power-really-possible

 

Tagged: Dialysis, Home dialysis, Kidney, Nephrology, pee, Urine, Wearable Artificial Kidney, World Kidney Day

How Academia Resembles a Drug Gang John Pickering Dec 02

John Pickering:

Worth a read for those interested in how academia works.
In the NZ context, I wonder how people see this. Is there a small cartel controlling the lives of the rest who plug away looking for grants in the hope of making the breakthrough?
Note: The increase in percentage of PhDs between 2000 and 2011 in NZ in the graph in this article is distorted by the large influx of international students in the late 90s and early 00s. This was further exacerbated by the change in rules to allow international PhD students to pay domestic and not international fees.

Originally posted on Alexandre Afonso:

In 2000, economist Steven Levitt and sociologist Sudhir Venkatesh published an article in the Quarterly Journal of Economics about the internal wage structure of a Chicago drug gang. This piece would later serve as a basis for a chapter in Levitt’s (and Dubner’s) best seller Freakonomics. [1] The title of the chapter, “Why drug dealers still live with their moms”, was based on the finding that the income distribution within gangs was extremely skewed in favor  of those at the top, while the rank-and-file street sellers earned even less than employees in legitimate low-skilled activities, let’s say at McDonald’s. They calculated 3.30 dollars as the hourly rate, that is, well below a living wage (that’s why they still live with their moms). [2]

If you take into account the risk of being shot by rival gangs, ending up in jail or being beaten up by your own hierarchy, you…

View original 2,017 more words

Should scientists respond to pseudo-science? John Pickering Sep 11

Do not answer a fool according to his folly, or you yourself will be just like him.

Answer a fool according to his folly, or he will be wise in his own eyes. (Proverbs 26:4, 5 NIV)

The editors of this particular list of proverbs were not fools – they knew they appeared contradictory.  Their purpose is to get us chewing over how we decide when we should speak up and when we shouldn’t.  When I heard these proverbs on Sunday my mind wandered (sorry Rev) immediately to my fellow science bloggers and the choices we make to respond or not respond to pseudo-science.  When we respond we do so wth hope.  Hope that the second proverb applies and the fool will recognise their own folly rather than keep on believing in their own wisdom.  A question I have for my fellow bloggers, how often does this actually take place?  I suspect, rarely.  At what point are we casting “pearls before swine”?  How do we know?

Perhaps more importantly, other than wasting our own time, could we be doing more harm than good (the first proverb)? By putting our scientific standing behind our reponses could we be enhancing the reputation of the pseudo-scientist in their own eyes or, worse, the eyes of readers? I think scientists are still paying the price for the over-confidence in science as solution to the world’s problems.  This has lead to some skepticism and a willingness to look at solutions that are not “main-stream” (especially if government funded or big-pharma).  By responding to the obvious nonesense, do we merely spread it further?

Some pseudo-science is addressing issues which also have non-scientific ethical issues that need to be respected.  Furthermore, the pseudo-science proponent may hold similar hopes to their scientist critic – eg hope for improved health.  I’m thinking particularly of issues such as vaccination or additives to food or water in which we need to weigh up the rights individuals with our responsibilites to others. Here, a scientist may express their opinion and their methodology of arriving at that opinion, but they need to tread very carefully not to appeal to Science with a capital “S” as if that is the ultimate standard against which all ethical decisions should be measured.

Here endeth the sermon.  Let us chew.

Tagged: proverb, pseudoscience, Science

Prostate cancer and omega 3 John Pickering Jul 12

The media is in a feeding frenzy with reports of a link between Omega 3 and Prostate Cancer.  Here’s a sample:

Link Between Omega-3 Fatty Acids and Increased Prostate Cancer Risk Confirmed (Science Daily)
Omega-3 supplements ‘could raise prostate cancer risk’ (Telegraph)
Omega-3 supplements linked to prostate cancer (Fox)
Omega 3 could increase cancer risk (TV3)

So, what’s the fuss?  The fuss is about a study published online yesterday in the Journal of the National Cancer Institute:

Brasky, T. M., Darke, A. K., Song, X., Tangen, C. M., Goodman, P. J., Thompson, I. M., et al. (2013). Plasma Phospholipid Fatty Acids and Prostate cancer Risk in the SELECT trial. Journal Of The National Cancer Institute, 1–10. doi:10.1093/jnci/djt174/-/DC1

The article is behind a paywall, so I’m not sure how many of the journalists have bothered to read it instead of relying on press releases.  I’ve access to the paper through my university, so here is a synopsis for the lay reader (bearing in mind I am not an expert in either omega 3 or cancer).

The thinking in the general public: Prostate cancer bad, Omega 3 good, therefore Omega 3 may prevent/delay prostate cancer

The thinking of the scientists: Is there a link between phospholipids (including omega 3) and prostate cancer?

The subjects studied:  Participants were enrolled in a trial of Vitamin E supplementation verse Placebo.  They were all male, from the US, Canada or Peurto Rico, aged 50+ if black (the medical literature uses this description), or 55+ if not, had no history of prostate cancer and with a PSA (prostate-specific antigen) test of <4ng/ml at the start of the study.  They were enrolled between July 2001 and May 2004.  While 35,533 men were enrolled in the trial, in this study only 2273 were studied.  These consisted of 834 patients who had prostate cancer diagnosed prior to 1 January 2008 and 1364 “matched” subjects who had no prostate cancer diagnosed in that time.  This is called a case-controlled study.  The “matching” is a statistical process whereby they make sure the two groups being compared (those with and without cancer) have certain demographic features in common on average.  In this case the groups had similar age ranges and similar ethnicities.  The cancer group was further divided into those with low and those with high grade cancers.

The methods:  Blood samples taken when patients were recruited and the total fatty acid content along with 4 types of Omega-3 fatty acids, 2 types of Omega-6 fatty acids, and 3 types of Trans-fatty acids were measured. The mean (average) proportions of each of the types of fatty acids (compared with total fatty acid) were compared between the No cancer and the Prostate Cancer groups.

The results:  Those with cancer had on average a greater proportion of each of  three of the kinds of Omega-3 fatty acids than those without cancer.  The p values were 0.03, <0.001, 0.006 (see here for an explanation of p values).  The p values for the two Omega-6 were higher (therefore more likely to be arrived at by chance) at 0.17 each.  The Trans-Fatt p values were 0.048, 0.08, 0.002. At this point it is very important to remember that not all those with cancer had high proportions of Omega-3 – it was the average that was higher.  An analysis comparing the 25% of subjects with the lowest Omega-3 (combination of the three Omega-3s) values with those with the highest 25% showed that the risk of prostate cancer was between 9 and 88% greater (with 95% confidence that this was not just by chance), ie a Hazard Ratio of 1.43 (95%CI 1.01 to 1.88).  Considering only those with the highest grade of cancer the Hazard Ratio was 1.71 (95%CI 1.0 to 2.94).

The authors performed a multivariable analysis.  That is when they check to see if other factors may be influencing the results.  They say that for Omega-3:

The continuous multi-variable-adjusted hazard ratios predicting total, …prostate cancer risk, [was] 1.16 (95% CI = 0.98 to 1.36),

This means that Omega-3 proportions changed the risk of getting prostate cancer by between a 2% decrease (100*(1-0.98)) and 36% increase (100*(1.346-1)) when other factors (not stated what) are accounted for.  This is what the 95% CI (Confidence interval) suggests.  The 1.16 is merely somewhere near the middle of the change in risk (16% higher).  It is the confidence interval that matters.  When it crosses 1, as it does here, it is not normally considered very important (ie not “statistically significant” as is often said).

The authors then conducted a meta-analysis for the Relative Risk of getting prostate cancer for two types of Omega-3 (DHA and EPA) and Omega-3 total fatty acid.  A meta-analysis is where they gather up all the studies and combine the results together.  In this case there were 7 studies (including the present one) which reported DHA and EPA and 4 which reported totals.  The results were

EPA:  RR = 1.07 (95%CI 0.95 to 1.21)
DHA: RR=1.16 (95%CI 1.03 to 1.31)
Total: RR=1.14 (95% CI 0.99 to 1.32)

Remember it is the 95% CI that is most important.  In this case only DHA creeps above 1 for the 95% CI.  Remember also that RR (Relative Risk) is a comparison of the rates of cancer between those with the level of Omega-3 among the lowest 20% and among the highest 20%.

The Conclusions:  The authors conclude

…these findings contradict the expectation that high consumption of long-chain ω-3 fatty acids and low consumption of ω-6 fatty acids would reduce the risk of prostate cancer.

This sounds reasonable under the assumption that consuming omega-3 (eg in supplements) actually increases the proportion of omega-3 in the blood.  They also state

It is unclear why high levels of long-chain ω-3 PUFA would increase prostate cancer risk,

What the media said:  TV3 borrowing from Sky, had a graphic with the word “Supplements” prominent and they talked of a 71% increased risk of high grade prostate cancer and 43% increased risk overall.  As we’ve seen these numbers are not what is relevant, the confidence intervals are – this adds a lot more uncertainty to the results (but not such good TV).  Also, they ignored the meta-analysis entirely (numbers not so big or interesting). They said nothing about the age range etc.  Finally, and most importantly, the study was not a study of supplements!  We have no idea why some participants had higher Omega-3 than others.  Some may have been because of supplements, some because of fish eating, some simply because of their own body composition and own metabolism.

My conclusion:  The study did not show that supplementation of Omega-3 is risky.  Nor did it show that supplementation is beneficial. It simply was not a study of supplementation. It did show that elevated proportions of Omega-3 fatty acids are possibly associated with increased risk of prostate cancer in men 50+ (black) and 55+ (non-black). Remember, too, that this is talking about relative risk.  The overall prostate cancer risk during the study period was just 2.35%.  If I’ve done my math right, then those in the top 25% of Omega-3 have an absolute risk of 2.77% (95%CI 2.12% to 3.65%).

 

Tagged: cancer, Fatty acids, Omega-3, Phospholipids, prostate cancer, supplements

Nelson Mandela is on dialysis John Pickering Jul 06

CNN is reporting Nelson Mandela is on dialysis. http://t.co/HZTIlmGrtO.  This means he is suffering from Acute Kidney Injury, the disease I study.  Having to have dialysis is very serious. Unfortunately, survival rates are only about 50% by this stage, less in the very elderly.  Dialysis is not a treatment, merely a support for the kidney to try and give them time to recover  function on their own and  a means to remove toxins from the body.

 

Tagged: Acute Kidney Injury, Dialysis, Kidney Attack, Nelson Mandela

The legend of Chris Martin: Part I John Pickering Jul 05

His innings may be over, but the legend lives on.  Chris Martin retired this week from international cricket. He was a legend with ball and he was a legend with bat, for quite different reasons.  His Test batting average of 2.36 was the worst ever of any international cricketer who batted in more than 15 innings.  But his average does not tell the whole story.  Indeed, the legend of Chris Martin’s batting is a long tale which will require several blog posts to tell.  We need to answer some important questions, “What was his best average?”, “Was it better for his partners to slog or should they have respected his abilities more?”  Along the way I hope that you will pick up on some techniques which will help you interpret those pesky statistics, or to present your own data.

Rule #1:  Always visualise your data

Christ Martin's batting innings by innings. Data source: CricInfo

Christ Martin’s batting innings by innings.
Data source: CricInfo

The best place to begin any quest is with a graph.  Here is a graph showing all 104 of Chris’s innings in chronological order.  On it is represented the scores when he was Out (red lines) and the scores when he was Not Out (blue lines).  Funnily enough he was out and not out exactly 52 times each.  We can see immediately that the peak of his batting performance was a score of 12 Not Out which occurred approximately half-way through his career.  His best form seems to be innings 30 to 34 where he went undefeated in 5 successive innings scoring 17 runs.  On the other hand he had several bad runs where he was Out for zero (red marks below the zero line).  One of the interesting things is that his first 4 innings may have given a false impression of his batting prowess.  In his first innings he scored 7, well above his eventual average of 2.36.  In his 2nd and 4th innings he was 0 Not Out.  In between he was 5 Not Out.  This coincided with his peak average every, 12 (orange triangles).  This allows us to note an important feature of statistics.  Let us pretend for a moment the average of 2.36 was “built-in” to Chris Martin from the beginning.  This means that it was inevitable that after many innings he would end up with that average.  But it is not inevitable that any one innings taken at random is equal to that mean.  Importantly, with only a few samples (ie the first few innings) the average at that point can be a long way from the “real” average.  This is a phenomenon caused by sampling from a larger population.  It is why we have to be very cautious with conclusions drawn from a small sample population.  For example, if General Practitioners throughout the country see on average 5 new leukemia cases a year, but we sample only three General Practitioners from Christchurch who saw 8, 9 and 14 then we would be quite wrong to conclude that Christchurch has a higher average leukemia rate than other regions.  We need a much larger sample from Christchurch to get a reasonable estimate of Christchurch’s average.  There are statistical techniques for deciding what proportion of General Practitioners should be sampled and what the uncertainty is in the average we arrive at.  Graphs also help… we can see with Chris that after only 10% of his innings he is within 1 of his average and stays that way throughout the rest of his career (orange triangles).

That’s it for today.  More on the legend of Chris Martin in the weeks ahead.

Tagged: batting, Chris Martin, cricinfo, Cricket, ESPN CricInfo, graphs, Statistics

Too little pee John Pickering Jun 26

This week’s post is really about the coloured stuff & why too little of it is dangerous.  Note, I say coloured stuff because it aint just yellow – check out this herald article if you don’t believe me (or just admire this beautiful photo).

 A rainbow of urine from a hospital lab. Credit:  laboratory scientist Heather West.

A rainbow of urine from a hospital lab.
Credit: laboratory scientist Heather West.

Story time

A long time ago, when Greeks wore togas, and not because they couldn’t afford shirts, a chap named Galen* noted that if you didn’t pee you’re in big trouble.  It took 1800 more years before the nephrologists and critical care physicians got together to try and decide just how much pee was too little.  This was at some exotic location in 2003 where these medics sat around for a few days talking and drinking (I’m guessing at the latter, but I have good reason to believe…) until they came up with the first consensus definition for Kidney Attack (then called Acute Renal Failure, now called Acute Kidney Injury)1.  It was a brilliant start and has revolutionised our understanding of just how prevalent Kidney Attack is.  It was, though, a consensus rather than strictly evidence based (that is not to say people didn’t have some evidence for their opinions, but the evidence was not based on systematic scientific discovery).  Since then various research has built up the evidence for or against the definitions they came up with (including some of mine which pointed out a mathematical error2 and the failings of a recommendation of what to do when you don’t have information about the patient before they enter hospital3).  One way they came up with to define Kidney Attack was to define it as too little pee.  Too little pee was defined as a urine flow rate of less than half a millilitre per kiliogram of body weight per hour over six hours (< 0.5ml/kg/h over 6h).  Our groups latest contribution to the literature shows that this is too liberal a definition.

The story of our research is that as part of a PhD program Dr Azrina Md Ralib (an anaesthesist from Malaysia) conduct an audit of pee of all patients entering Christchurch’s ICU for a year.  She did an absolutely fantastic job because this meant collecting information on how much every patient peed for every hour during the first 48 hours as well as lots of demographic data etc etc etc. Probably 60-80,000 data points in all!  She then began to analyse the data.  We decided to compare the urine output data against  meaningful clinical outcomes – namely death or need for emergency dialysis.  We discovered that if patients had a flow rate of between 0.3 to 0.5 ml/kg/h for six hours it made no difference to the rates of death or dialysis compared to those with a flow rate greater than 0.5.  Less than 0.3, though, was associated with greater mortality (see figure).  For the clinician this means they can relax a little if the urine output is at 0.4 ml/kg/h.  Importantly, they may not give as much fluid to patients. Given that in recent times a phenomenon called “fluid overload” has been associated with poor outcomes, this is good news.

The full paper can be read for free here.

Proportion of mortality or dialysis in each group. Error bars represent 95% confidence intervals.From Ralib et al Crit Care 2012.

Proportion of mortality or dialysis in each group. Error bars represent 95% confidence intervals.From Ralib et al Crit Care 2013.

———————————————————

*Galen 131-201 CE.  He came up with one of the best quotes ever: “All who drink of this remedy recover in a short time, except those whom it does not help, who all die.”

1.     Bellomo R, Ronco C, Kellum JA, Mehta RL, Palevsky PM, Acute Dialysis Quality Initiative workgroup. Acute renal failure – definition, outcome measures, animal models, fluid therapy and information technology needs: the Second International Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group. Crit Care 2004;8(4):R204–12.

2.     Pickering JW, Endre ZH. GFR shot by RIFLE: errors in staging acute kidney injury. Lancet 2009;373(9672):1318–9.

3.     Pickering JW, Endre ZH. Back-calculating baseline creatinine with MDRD misclassifies acute kidney injury in the intensive care unit. Clin J Am Soc Nephro 2010;5(7):1165–73.

Tagged: Acute Kidney Injury, Acute Renal Failure, AKI, Fluids, Intensive Care, Kidney, Kidney Attack, Urine

Significantly p’d John Pickering Jun 20

I may be a pee scientist, but today is brought to you by the letter “P” not the product.  “P” is something all journalists, all lay readers of science articles, teachers, medical practitioners, and all scientists should know about.  Alas, in my experience many don’t and as a consequence “P” is abused. Hence this post.  Even more abused is the word “significant” often associated with P; more about that later.

P is short for probability.  Stop! – don’t stop reading just because statistics was a bit boring at school; understanding maybe the difference between saving lives and losing them.  If nothing so dramatic, it may save you from making a fool of yourself.

P is a probability.  It is normally reported as a fraction (eg 0.03) rather than a percentage (3%).  You will be familiar with it when tossing a coin.  You know there is a 50% or one half or 0.5 chance of obtaining a heads with any one toss.  If you work out all the possible combinations of two tosses then you will see that there are four possibilities, one of which is two heads in a row.  So the prior (to tossing) probability of two heads in a row is 1 out 4 or P=0.25. You will see P in press releases from research institutes, blog posts, abstracts, and research articles, this from today:

“..there was significant improvement in sexual desire among those on  testosterone (P=0.05)” [link]

So, P is easy, but interpreting P depends on the context.  This is hugely important.  What I am going to concentrate on is the typical medical study that is reported.  There is also a lesson for a classroom.

One kind of study reporting a P value is a trial where one group of patients are compared with another.  Usually one group of patients has received an intervention (eg a new drug) and the other receives regular treatment or a placebo (eg a sugar pill).  If the study is done properly a primary outcome should have been decided before hand.  The primary outcome must measure something – perhaps the number of deaths in a one year period, or the mean change in concentration of a particular protein in the blood.  The primary outcome is how these what is measured differs between the group getting the new intervention and the group not getting it.  Associated with it is a P value, eg:

“CoQ10 treated patients had significantly lower cardiovascular mortality (p=0.02)” [link]

To interpret the P we must first understand what the study was about and, in particularly, understand the “null hypothesis.”  The null hypothesis is simply the idea the study was trying to test (the hypothesis) expressed in a particular way.  In this case, the idea is that CoQ10 may reduce the risk of cardiovascular mortality.  Expressed as a null hypothesis we don’t assume that it could only decrease rates, but we allow for the possibility that it may increase as well (this does happen with some trials!).  So, we express the hypothesis in a neutral fashion.  Here that would be something like that the risk of cardiovascular death is the same in the population of patients who take CoQ10 and in the population which does not take CoQ10.  If we think about it for a minute, then if the proportion of patients who died of a cardiovascular event was exactly the same in the two groups then the risk ratio (the CoQ10 group proportion divided by the non CoQ10 group proportion) would be exactly 1.  The P value, then answers the question:

If the risk of cardiovascular death was the same in both groups (the null hypothesis) was true what is the probability (ie P) that the difference in the actual risk ratio measured from 1 is as large as was observed simply by chance?

The “by chance” is because when the patients were selected for the trial there is a chance that they don’t fairly represent the true population of every patient in the world (with whatever condition is being studied) either in their basic characteristics or their reaction to the treatment. Because not every patient in the population can be studied, a sample must be taken.  We hope that it is “random” and representative, but it is not always.  For teachers, you may like to do the lesson at the bottom of the page to explain this to children.  Back to our example, some numbers may help.

If we have 1000 patients receiving Drug X, and 2000 receiving a placebo.  If, say, 100 patients in the Drug X group die in 1 year, then the risk of dying in 1 year we say is 100/1000 or 0.1 (or 10%).  If in the placebo group, 500 patients die in 1 year, then the risk is 500/2000 or 0.25 (25%).  The risk ratio is 0.1/0.25 = 0.4.  The difference between this and 1 is 0.6.  What is the probability that we arrived at 0.6 simply by chance?  I did the calculation and got a number of p<0.0001.  This means there is less than a 1 in 10,000 chance that this difference was arrived at by chance.  Another way of thinking of this is that if we did the study 10,000 times, and the null hypothesis were true, we’d expect to see the result we saw about one time.  What is crucial to realise is that the P value depends on the number of subjects in each group.  If instead of 1000 and 2000 we had 10 and 20, and instead of 100 and 500 deaths we had 1 and 5, then the risks and risk ratio would be the same, but the P value is 0.63 which is very high (a 63% chance of observing the difference we observed).  Another way of thinking about this is what is the probability that we will state there is a difference of at least the size we see, when there is really no difference at all. If studies are reported without P values then at best take them with a grain of salt.  Better, ignore them totally.

It is also important to realise that within any one study that if they measure lots of things and compare them between two groups then simply because of random sampling (by chance) some of the P values will be low.  This leads me to my next point…

The myth of significance

You will often see the word “significant” used with respect to studies, for example:

“Researchers found there was a significant increase in brain activity while talking on a hands-free device compared with the control condition.” [Link]

This is a wrong interpretation:  “The increase in brain activity while talking on a hands-free device is important.” or  “The increase in brain activity while talking on a hands-free device is meaningful.”

“Significant” does not equal “Meaningful” in this context.  All it means is that the P value of the null hypothesis is less than 0.05.   If I had it my way I’d ban the word significant.  It is simply a lazy habit of researchers to use this short hand for p<0.05.  It has come about simply because someone somewhere started to do it (and call it “significance testing”) and the sheep have followed.  As I say to my students, “Simply state the P value, that has meaning.”*

sig

_____________________________________________________________

For the teachers

Materials needed:

  • Coins
  • Paper
  • The ability to count and divide

Ask the children what the chances of getting a “Heads” are.  Have a discussion and try and get them to think that there are two possible outcomes each equally probable.

Get each child to toss their coin 4 times and get them to write down whether they got a head or tail each time.

Collate the number of heads in a table like.

#heads             #children getting this number of heads

0                      ?

1                      ?

2                      ?

3                      ?

4                      ?

If your classroom size is 24 or larger then you may well have someone with 4 heads or 0 (4 tails).

Ask the children if they think this is amazing or accidental?

Then, get the children to continue tossing their coins until they get either 4 heads or 4 tails in a row.  Perhaps make it a competition to see how fast they can get there.  They need to continue to write down each head and tail.

You may then get them to add all their heads and all their tails.  By now the proportions (get them to divide the number of heads by the number of tails).  If you like, go one step further and collate all the data.  The probability of a head should be approaching 0.5.

Discuss the idea that getting 4 heads or 4 tails in a row was simply due to chance (randomness).

For more advanced classes, you may talk about statistics in medicine and in the media.  You may want to use some specific examples about one off trials that appeared to show a difference, but when repeated later it was found to be accidental.

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*For the pedantic.  In a controlled trial the numbers in the trial are selected on the basis of pre-specifying a (hopefully) meaningful difference in the outcome between the case and control arms and a probability of Type I (alpha) and Type II (beta)  errors.  The alpha is often 0.05.  In this specific situation if the P<0.05 then it may be reasonable to talk about a significant difference because the alpha was pre-specified and used to calculate the number of participants in the study.

Tagged: health, medicine, P, RCT, Science, significance, Statistics, trials

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