Risk factors and interventions with statistically significant tiny effects
George CM Siontis1 and John PA Ioannidis1,2,*
+ Author Affiliations
1Clinical Trials and Evidence-Based Medicine Unit and the Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece and 2Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, USA
↵*Corresponding author. Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA. E-mail: jioannid@stanford.edu
Accepted May 19, 2011.
Abstract
Background Large studies may identify postulated risk factors and interventions with very small effect sizes. We aimed to assess empirically a large number of statistically significant relative risks (RRs) of tiny magnitude and their interpretation by investigators.
Methods RRs in the range between 0.95 and 1.05 were identified in abstracts of articles of cohort studies; articles published in NEJM, JAMA or Lancet; and Cochrane reviews. For each eligible tiny effect and the respective study, we recorded information on study design, participants, risk factor/intervention, outcome, effect estimates, P-values and interpretation by study investigators. We also calculated the probability that each effect lies outside specific intervals around the null (RR interval 0.97–1.03, 0.95–1.05, 0.90–1.10).
Results We evaluated 51 eligible tiny effects (median sample size 112 786 for risk factors and 36 021 for interventions). Most (37/51) appeared in articles published in 2006–10. The effects pertained to nutrition (n = 19), genetic and other biomarkers (n = 8), correlates of health care (n = 8) and diverse other topics (n = 16) of clinical or public health importance and mostly referred to major clinical outcomes. A total of 15 of the 51 effects were >80% likely to lie outside the RR interval 0.97–1.03, but only 8 were >40% likely to lie outside the RR interval 0.95–1.05 and none was >1.7% likely to lie outside the RR interval 0.90–1.10. The authors discussed at least one concern for 23 effects (small magnitude n = 19, residual confounding n = 11, selection bias n = 1). No concerns were expressed for 28 effects.
Conclusions Statistically significant tiny effects for risk factors and interventions of clinical or public health importance become more common in the literature. Cautious interpretation is warranted, since most of these effects could be eliminated with even minimal biases and their importance is uncertain.
Category: Clinical trial
More abut Tamiflu

|
||
A new review of the influenza drug oseltamivir (Tamiflu) has raised questions about both the efficacy of the medication and the commitment of its maker to supply enough data for claims about the drug to be evaluated by independent experts.
It also raises questions about the entire process of systematic review. Researchers led by Tom Jefferson, MD, of the Cochrane Collaboration, pored over 15 published studies and nearly 30,000 pages of “clinical study reports.” But, they reported, the clinical study information – data previously shared only with regulators – was only a part of what internal evidence suggested was available. And many published studies had to be excluded because of missing or contradictory data, Jefferson and colleagues reported. Activate MedPage Today’s CME feature and receive free CME credit on medical stories like this one
Action Points
The drug’s maker, Switzerland-based Roche, had promised after a previous Cochrane review to make all of its data available for “legitimate analyses.” After a request for the data, Jefferson and colleagues reported, the company sent them 3,195 pages covering 10 treatment trials of the drug.
But, three of the reviewers noted in a parallel report in BMJ, the tables of contents suggested that the data were incomplete.
“What we’re seeing is largely Chapter One and Chapter Two of reports that usually have four or five chapters,” according to theBMJ article’s lead author, Peter Doshi, PhD, of Johns Hopkins University.
Roche did not immediately respond to a telephoned request for comment.
Requests for More Data
The researchers then asked the European Medicines Agency (EMA) for the data, under a Freedom of Information request, and obtained a further 25,453 pages, covering 19 trials.
But that data, too, was incomplete, they said, although the agency said it was all that was available.
The FDA is thought to have the complete reports, but has not yet responded to requests for them, the researchers reported.
Regulatory agencies such as the EMA and FDA routinely see the large clinical study reports, Jefferson and colleagues said in BMJ, but systematic reviewers and the general medical public do not.
“While regulators and systematic reviewers may assess the same clinical trials, the data they look at differs substantially,” they said.
The Cochrane group has been trying for several years to put together a clear-cut systematic review of the evidence on antivirals aimed at flu.
In 2006, the group concluded that the evidence showed that oseltamivir reduced the complications of the flu. But that conclusion was challenged on the basis that a key piece of data was flawed.
An updated review in 2009 – throwing out the flawed study — concluded there wasn’t enough evidence to show that the drug had any effect on complications.
For this analysis, the Cochrane reviewers had originally intended to perform a systematic review on both of the approved neuraminidase inhibitors – oseltamivir and zanamivir (Relenza), using the clinical study reports to supplement published trials.
In the end, they decided that for oseltamivir, they needed more detail in order to perform the review in its entirety. But, they reported, some conclusions could be drawn from published data on the 15 trials and from 16,000 pages of clinical study reports that were available before their deadline.
They also decided to postpone analysis of zanamivir (for which they had 10 trials) because the drug’s maker, GlaxoSmithKline, offered individual patient data which they wanted time to analyze.
The oseltamivir analysis showed:
Data Discrepancies Found
But discrepancies between the published trial data and the clinical study reports “led us to lose confidence in the journal reports,” Doshi and colleagues wrote in BMJ.
For example, they noted that one journal report clearly said there were no drug-related serious adverse events, but the clinical study report listed three that were possibly related to oseltamivir.
As well, the sheer scope of the clinical study reports meant that much was left out of journal reports. One 2010 study, on safety and pharmacokinetics of oseltamivir at standard and high dosages, took up seven journal pages and 8,545 pages of the clinical study report.
But the researchers were also shaken, they said, by the “fragility” of some of their assumptions.
For instance, they found that the clinical study reports showed that in many trials, the placebo contained two chemicals not found in the oseltamivir capsules.
“We could find no explanation for why these ingredients were only in the placebo,” they wrote in BMJ, “and Roche did not answer our request for more information on the placebo content.”
Jefferson and colleagues also reported they found disparities in the numbers of influenza-infected people reported to be present in the treatment versus control groups of oseltamivir trials.
One possible explanation, they noted, is that oseltamivir affects antibody production – even though the manufacturer says it does not.
Gaps in Knowledge Remain
That question is profoundly important, Doshi told MedPage Today, because it may offer clues to how the drug works – one of the gaps in knowledge about oseltamivir.
“You can’t make good therapeutic decisions if you don’t know how the drugs works,” he said – information that he and his colleagues suspect may be buried in the mass of missing data.
It’s also important, he said, because public health agencies have been making decisions to stockpile oseltamivir without a clear understanding of the facts.
Essentially, he said, those decisions have been based on the flawed study – a Roche-supported meta-analysis – that was thrown out of the 2009 Cochrane review.
“They’re taking the drug manufacturer’s word at face value,” he said.
The results seem unlikely to resolve conflicts over the medical value of the drug, which is a major cash cow for Roche, adding some $3.4 billion to the company’s bottom line in 2009 alone, according to Deborah Cohen, investigations editor of BMJ.
In an accompanying article, Cohen said that “clinicians can be forgiven for being confused about what the evidence on oseltamivir says.”
She noted that the European Centre for Disease Prevention and Control, the CDC, and the World Health Organization “differ in their conclusions about what the drug does.”
As well, those conclusions are often contradicted by claims on the drug labels – themselves allowed by regulators, Cohen argued.
The Cochrane reviewers reported grant support from the U.K. National Institute for Health Research and Jefferson and Doshi reported they had no recent financial links with industry.
Cohen is employed by BMJ.
|
||
Primary source: Cochrane Database of Systematic Reviews Source reference: Jefferson T, et al “Neuraminidase inhibitors for preventing and treating influenza in healthy adults and children” Cochrane Database of Systematic Reviews 2011; 12. Art. No.: CD008965. Additional source: BMJ Cohen D “Flu drugs: search for evidence goes on” BMJ 2012; 344: e458. Additional source: BMJ |
Evidence-Based Medicine in the EMR Era

Evidence-Based Medicine in the EMR Era

SOURCE INFORMATION
REFERENCES
-
1Lowe HJ, Ferris TA, Hernandez PM, Weber SC. STRIDE — an integrated standards-based translational research informatics platform. AMIA Annu Symp Proc 2009;14:391-395
-
2Prokosch HU, Ganslandt T. Perspectives for medical informatics: reusing the electronic medical record for clinical research. Methods Inf Med 2009;48:38-44
Web of Science | Medline -
3Gunn PW, Hansen ML, Kaelber DC. Underdiagnosis of pediatric hypertension — an example of a new era of clinical research enabled by electronic medical records. AMIA Annu Symp Proc 2007;11:966-966
-
4Halevy A, Norvig P, Pereira F. The Unreasonable Effectiveness of Data. IEEE Intelligent Systems, March/April 2009:8-12.
-
5Stout R. In the best families. New York: Viking Press, 1950:71.

The Prostrate Placebo
a tennis ball, I am going to go looking for a therapy that will shrink it, not fool me into thinking I can write
“It is also an expectorant, and controls irritation of mucous tissues. It has proved useful in irritative cough, chronic bronchial coughs, whooping-cough, laryngitis, acute and chronic, acute catarrh, asthma, tubercular laryngitis, and in the cough of phthisis pulmonalis. Upon the digestive organs it acts kindly, improving the appetite, digestion, and assimilation. However, its most pronounced effects appear to be those exerted upon the urino-genital tracts of both male and female, and upon all the organs concerned in reproduction. It is said to enlarge wasted organs, as the breasts, ovaries, and testicles, while the paradoxical claim is also made that it reduces hypertrophy of the prostate. Possibly this may be explained by claiming that it tends toward the production of a normal condition, reducing parts when unhealthily enlarged, and increasing them when atrophied.”
the take-home message for clinicians, for physicians, for all health professionals is that their words, behaviors, attitudes are very important, and move a lot of molecules in the patient’s brain. So, what they say, what they do in routine clinical practice is very, very important, because the brain of the patient changes sometimes… there is a reduction in anxiety; but we know that there is a real change…in the patient’s brain which is due to… the ‘ritual of the therapeutic act.’

All Trial Data Must Be Disclosed: Rogawski Explains


Rogawski: When I participated in a translational working group of the International League Against Epilepsy on how to encourage the development of more effective epilepsy therapies, I realized that negative clinical data was critically important in assessing the predictiveness of animal models. Then, sometime later when I was writing a review article I asked a company for the clinical trial results on a product they had abandoned. I let them know that I hoped they would publish their trial results as even negative studies provide important scientific information and the patients who participated in the trials expect that the information derived from their participation will benefit mankind. The terse answer was that the company “does not intend to publish the results of the epilepsy trial.
Rogawski: The results data in ClinicalTrials.gov is simply presented in tables. There’s no presentation of detailed analysis or an interpretation as in a journal publication. And there’s minimal review. ClinicalTrials.gov could become the primary repository for the results of clinical trials of drugs and devices. And some people may feel that publication in a peer-reviewed article is no longer needed.
We’re arguing that you still need to write up the results.
Rogawski: There’s a whole separate tab on every entry for results. If no study results are available, it will indicate that. For terminated products, you may never see the results published. There’s a loophole in the law that allows them (sponsors) to be exempt from posting the basic results when the product hasn’t been approved.
Rogawski: There are a couple of ways a study can be exempt. One way is to study the drug for another indication. Delayed submission is permitted when sponsor is seeking a new use. The other way is if a drug or device is never approved. In principle, although it’s not stated this way, if the sponsor never submits the particular product for FDA approval, then they don’t have to post the basic results. The current law only applies to agents approved by the FDA. Let’s say the sponsor submits an NDA and the agency gives a complete response letter saying they have to do X, Y and Z, and the sponsor never does that. They decide it would be too expensive to do further clinical trials and they decide to abandon the product. Then they also don’t have to put data on ClinicalTrials.gov.
Rogawski: We’re hoping HHSs will use its authority under FDAAA 801 to require sponsors to report results with any trial registered with ClinicalTrials.gov, even for any product that is abandoned… There’s potentially useful information in any clinical trial and if it’s not available, it diminishes public knowledge. It may even place patients in later trials at risk. There could be some data showing this particular product causes green spots. It would be important to know this the next some time somebody considers developing a drug that acts by a similar molecular mechanism.
Rogawski: HHS may decide to balance with the interests of pharmaceutical companies with the public interest. They may decide it’s commercially damaging to require companies to publish the results of trials with abandoned products. We think it’s a fallacious argument. We understand that many sponsors consider data proprietary… They put all this money into it and may want to continue work on the product later on or try for a different indication, and they may perceive that negative trial data would impair that. And they may consider it a waste of time and money to write stuff up when it isn’t going to go anywhere…We think it’s incorrect, but I guess companies may think that way.
Rogawski: I’ve noticed that it’s often the case that sponsors do not publish results when they’re abandoning the product. In my own area, which is the development of anti-epileptic drugs, we have a problem. We use animal screening models to identify drugs, but we don’t know how valid the models are because we don’t know much about the cases where a drug was effective in the models, but not in clinical trials because very little information about failed drugs is available publicly.
Investigating over-the-counter oral analgesics
There is good evidence supporting the efficacy of standard doses of aspirin, paracetamol, ibuprofen, naproxen, and diclofenac, all of which are available as over-the-counter (OTC) medicines in some part of the world. There is no good evidence for most branded combination products, though it is likely that additional analgesic effect is produced by codeine. Combinations of ibuprofen and paracetamol appear to be particularly effective.
Background
Systematic review and methods
Results
Table 1: Details of available data
Drug |
Details of available data |
References of included studies |
Anadin Extra | We found no trials comparing Anadin Extra (or a generic combination analgesic containing paracetamol, aspirin and caffeine in similar doses) to placebo | N/A |
Askit | We found no trials comparing Askit (or a generic combination analgesic containing aspirin, caffeine and aloxiprin in similar doses) to placebo | N/A |
Aspirine | We found two trials: Forbes et al. 1990 and Rubin et al. 1984 comparing a generic combination of aspirin and caffeine (ASA 650mg/caffeine 65mg in Forbes et al. and ASA 800mg/caffeine 65mg in Rubin et al.) against placebo. Both trials were relatively small and used different pain types: Forbes et al. (n=141) in dental and Rubin et al. (n=230) in episiotomy. The results reflect this with Forbes et al. reporting the % of patients achieving 50% pain relief on the active treatment as 27% and on the placebo as 1%; while Rubin et al. report 86% on the active treatment and 48% on the placebo | Forbes JA. Pharmacotherapy. 1990; 10(6):387-93 Rubin A. J Int Med Res. 1984; 12(6):338-45 |
Aspro Clear | We found seven trials in a Cochrane review of single dose oral aspirin for acute pain (Edwards et al. 2000 – currently undergoing in-house update) comparing aspirin in any formulation (ASA 1000mg) against placebo | Edwards JE. Cochrane Database Syst Rev. 2000;(2):CD002067 |
Codis | We found no trials comparing Codis (or a generic combination analgesic containing aspirin and codeine in similar doses) to placebo | N/A |
Cuprofen Plus | We found two trials: Cater et al. 1985 and Norman et al. 1985 comparing a generic combination of ibuprofen and caffeine (IBU 400mg/COD 30mg) against placebo. Both trials reported pain following episiotomy with similar results | Cater M. Clin Ther. 1985; 7(4):442-7 Norman SL. Clin Ther. 1985; 7(5):549-54 |
Disprin | We found no trials comparing Dispirin (or a generic formulation of aspirin in a similar dose) to placebo | N/A |
Disprin Extra | We found no trials comparing Dispirin Extra (or a generic combination of aspirin and paracetamol in similar doses) to placebo | N/A |
Feminax Ultra | We found five trials in an up-to-date Cochrane review of single dose oral naproxen for acute pain (Derry et al. 2009) comparing naproxen or naproxen sodium (NAPROX 500mg or NAPROX SODIUM 550mg) against placebo | Derry C. Cochrane Database Syst Rev. 2009 Jan 21;(1);CD004234 |
Mersyndol | We found one trial: Margarone et al. 1995 comparing Mersyndol against placebo. The trial reported pain following dental surgery | Margarone JE. Clin Pharmacol Ther. 1995 Oct; 58(4):453-8 |
Nurofen | We found 61 trials in an up-to-date Cochrane review of single dose oral ibuprofen for acute pain (Derry et al. 2009) comparing a generic formulation of ibuprofen (IBU 400mg) against placebo | Derry C. Cochrane Database Syst Rev. 2009 Jul 8;(3):CD001548 |
Panadeine 15 | We found no trials comparing Panadeine 15 (or a generic combination of paracetamol and codeine in similar doses) to placebo | N/A |
Panadol | We found 28 trials in an up-to-date Cochrane review of single dose oral paracetamol for acute pain (Toms et al. 2008) comparing a generic formulation of paracetamol (PARA 1000mg) against placebo | Toms L. Cochrane Database Syst Rev. 2008 Oct 8;(4):CD004602 |
Panadol Extra | We found one trial: Winter et al. 1983 comparing a generic combination of paracetamol and caffeine (PARA 1000mg/CAF 130mg) against placebo. The trial reported pain following dental surgery | Winter L Jr. Current Therapeutic Research. 1983 Jan; 33(1):115-122 |
Paracodol | We found no trials comparing Paracodol (or a generic combination of paracetamol and codeine in similar doses) to placebo | N/A |
Paramol | We found no trials comparing Paramol (or a generic combination of paracetamol and dihydrocodeine tartrate in similar doses) to placebo | N/A |
Pentalgin H | We found no trials comparing Pentalgin H (or a generic combination of naproxen, codeine, caffeine, dipyrone and phenobarbitol in similar doses) to placebo | N/A |
Saridon | We found one trial: Kiersch et al. 2002 comparing Saridon against placebo. The trial reported pain following dental surgery | Kiersch TA. Curr Med Res Opin. 2002; 18(1):18-25 |
Sedalgin-neo | We found no trials comparing Sedalgin-neo (or a generic combination of paracetamol, caffeine, codeine, dipyrone and phenobarbitol in similar doses) to placebo | N/A |
Solpadeine Max | We found no trials comparing Solpadeine Max (or a generic combination of paracetamol and codeine in similar doses) to placebo | N/A |
Solpadeine Plus | We found one trial: Cooper et al. 1986 comparing a generic combination of paracetamol, codeine and caffeine (PARA 1000mg/COD 16mg/CAF 30mg) against placebo. The trial reported pain following dental surgery | Cooper SA. Anesth Prog. 1986 May-Jun; 33(3):139-42 |
Voltarol | We found four trials in an up-to-date Cochrane review of single dose oral diclofenac for acute pain (Derry et al. 2009) comparing all generic formulations of diclofenac (DICLO 25mg) against placebo | Derry P. Cochrane Database Syst Rev. 2009 Apr 15;(2):CD004768 |
Table 2 summarises data available for each of the analgesics along with its calculated relative benefit (RB) and number-needed-to-treat-to-benefit (NNT). Para = paracetamol, Asa – aspirin, Caf = caffeine, Cod = codeine, Naprox = naproxen, Diclo = diclofenac, Ibu = ibuprofen
Drug |
Constituents |
Number of Trials |
Number of Patients |
Percent with Active |
Percent with Control |
RB
(95% CI)
|
NNT
(95% CI)
|
Anadin Extra | Para400 + Asa600 + Caf90 |
0 |
|||||
Askit | Asa530 + Caf110 + Aloxiprin140 |
0 |
|||||
Aspirine | Asa650 + Caf65 |
2 |
371 |
65 |
28 |
2.3 (1.8 – 3.0) |
2.7 (2.2 – 3.7) |
Forbes 1990 |
141 |
17 |
0 |
39.8 (2.4 – 648) |
3.9 (2.7 – 6.7) |
||
Rubin 1984 |
230 |
86 |
48 |
1.8 (1.5 – 2.2) |
2.6 (2.0 – 3.7) |
||
Aspro Clear | Asa1000 |
7 |
679 |
43 |
16 |
2.6 (2.0 – 3.5) |
3.7 (3.0 – 5.0) |
Codis | Asa1000 + Cod base 16 |
0 |
|||||
Cuprofen Plus | Ibu400 + Cod base 20 |
2 |
167 |
55 |
31 |
1.8 (1.2 – 2.6) |
4.1 (2.6 – 10.3) |
Disprin | Asa900 |
0 |
|||||
Disprin Extra | Asa600 + Para400 |
0 |
|||||
Margarone 1995 |
76 |
21 |
8 |
2.7 (0.8 – 9.3) |
7.6 |
||
Feminax Ultra | Naprox500 |
9 |
784 |
52 |
15 |
3.4 (2.7 – 4.4) |
2.7 (2.3 – 3.2) |
Winter 1983 |
81 |
48 |
22 |
2.2 (1.1 – 4.2) |
3.9 (2.2 – 18.0) |
||
Mersyndol | Para1000 + Cod base15 + Doxylamine succinate10 |
1 |
76 |
21 |
8 |
2.7 (0.8 – 9.3) |
Not Calculated |
Nurofen | Ibu400 |
61 |
6475 |
54 |
14 |
4.0 (3.6 – 4.4) |
2.5 (2.4 – 2.6) |
Panadeine 15 | Para1000 + Cod base23 |
0 |
|||||
Panadol | Para1000 |
28 |
3232 |
46 |
18 |
2.5 (2.2 – 2.9) |
3.6 (3.2 – 4.0) |
Cooper 1986 |
61 |
29 |
4 |
6.2 (0.9 – 45.0) |
4.2 (2.5 – 14.2) |
||
Panadol Extra | Para1000 + Caf130 |
1 |
81 |
48 |
22 |
2.2 (1.1 – 4.2) |
3.9 (2.2 – 18.0) |
Paracodol | Para1000 + Cod base13 |
0 |
|||||
Paramol | Para1000 + Dihydrocodeine tartarate15 |
0 |
|||||
Pentalgin H | Naprox100 + Cod base8 + Caf50 + Dipyrone300 + Phenobarbitol15 |
0 |
|||||
Saridon | Para500 + Caf100 + Propifenazone300 |
1 |
301 |
23 |
2 |
9.2 (1.3 – 64.5) |
4.9 (3.6 – 7.4) |
Sedalgin-neo | Para600 + Caf100 + Cod base20 + Dipyrone300 + Phenobarbitol30 |
0 |
|||||
Norman 1985 |
74 |
53 |
29 |
1.8 (1.0 – 3.3) |
4.2 (2.2 – 48.5) |
||
Cater 1985 |
93 |
57 |
32 |
1.8 (1.1 – 2.9) |
4.1 (2.3 – 19.8) |
||
Solpadeine Max | Para1000 + Cod base20 |
0 |
|||||
Solpadeine Plus | Para1000 + Caf60 + Cod base13 |
1 |
61 |
29 |
4 |
6.2 (0.9 – 45.0) |
4.2 (2.5 – 14.2) |
Voltarol | Diclo25 |
4 |
502 |
53 |
15 |
3.6 (2.6 – 5.0) |
2.6 (2.2 – 3.3) |
None | Ibu100 + Para 250 |
2 |
175 |
73 |
10 |
7.6 (4.2 -14) |
1.6 (1.3 – 1.9) |
None | Ibu200 + Para 500 |
2 |
280 |
74 |
10 |
7.7 (2.2 – 14 |
1.6 (1.4 – 1.8) |
None | Ibu400 + Para 1000 |
2 |
320 |
75 |
10 |
7.9 (4.3 – 14) |
1.5 (1.4 – 1.7) |
Table 3 shows a sub-analysis of only those trials involving dental pain.
Drug |
Constituents |
Number of Trials |
Number of Patients |
Percent with Active |
Percent with Control |
RB
(95% CI)
|
NNT
(95% CI)
|
Anadin Extra | Para400 + Asa600 + Caf90 |
0 |
|||||
Askit | Asa530 + Caf110 + Aloxiprin140 |
0 |
|||||
Aspirine | Asa650 + Caf65 |
1 |
141 |
17 |
0 |
39.8 (2.4 – 648) |
3.9 (2.7 – 6.7) |
Aspro Clear | Asa1000 |
3 |
345 |
32 |
11 |
2.9 (1.8 – 4.8) |
4.7 (3.4 – 7.6) |
Codis | Asa1000 + Cod base 16 |
0 |
|||||
Cuprofen Plus | Ibu400 + Cod base 20 |
0 |
|||||
Disprin | Asa900 |
0 |
|||||
Disprin Extra | Asa600 + Para400 |
0 |
|||||
Feminax Ultra | Naprox500 |
5 |
402 |
62 |
7 |
8.9 (5.3 – 14.9) |
1.8 (1.6 – 2.1) |
Mersyndol | Para1000 + Cod base15 + Doxylamine succinate10 |
1 |
76 |
21 |
8 |
2.7 (0.8 – 9.3) |
Not Calculated |
Nurofen | Ibu400 |
49 |
5428 |
55 |
12 |
4.7 (4.2 – 5.2) |
2.3 (2.2 – 2.4) |
Panadeine 15 | Para1000 + Cod base23 |
0 |
|||||
Panadol | Para1000 |
18 |
2171 |
40 |
9 |
4.4 (3.5 – 5.5) |
3.3 (3.0 – 3.7) |
Panadol Extra | Para1000 + Caf130 |
1 |
81 |
48 |
22 |
2.2 (1.1 – 4.2) |
3.9 (2.2 – 18.0) |
Paracodol | Para1000 + Cod base13 |
0 |
|||||
Paramol | Para1000 + Dihydrocodeine tartarate15 |
0 |
|||||
Pentalgin H | Naprox100 + Cod base8 + Caf50 + Dipyrone300 + Phenobarbitol15 |
0 |
|||||
Saridon | Para500 + Caf100 + Propifenazone300 |
1 |
301 |
23 |
2 |
9.2 (1.3 – 64.5) |
4.9 (3.6 – 7.4) |
Sedalgin-neo | Para600 + Caf100 + Cod base20 + Dipyrone300 + Phenobarbitol30 |
0 |
|||||
Solpadeine Max | Para1000 + Cod base20 |
0 |
|||||
Solpadeine Plus | Para1000 + Caf60 + Cod base13 |
1 |
61 |
29 |
4 |
6.2 (0.9 – 45.0) |
4.2 (2.5 – 14.2) |
Voltarol | Diclo25 |
3 |
398 |
51 |
11 |
4.6 (3.1 – 7.1) |
2.5 (2.1 – 3.2) |
None | Ibu100 + Para 250 |
2 |
175 |
73 |
10 |
7.6 (4.2 -14) |
1.6 (1.3 – 1.9) |
None | Ibu200 + Para 500 |
2 |
280 |
74 |
10 |
7.7 (2.2 – 14 |
1.6 (1.4 – 1.8) |
None | Ibu400 + Para 1000 |
2 |
320 |
75 |
10 |
7.9 (4.3 – 14) |
1.5 (1.4 – 1.7) |
Figure 1: NNTs for all available data
Figure 2: NNTs for dental studies only
Comment
Dearth of evidence
What can we make of the evidence we have
References
- Laska EM, Sunshine A, Mueller F, Elvers WB, Siegel C, Rubin A. Caffeine as an analgesic adjuvant. JAMA 1984 251:1711-8.
- Barden J, Derry S, McQuay HJ, Moore RA. Bias from industry trial funding? A framework, a suggested approach, and a negative result. Pain 2006 121:207-18.
- Smith LA, Moore RA, McQuay HJ, Gavaghan D. Using evidence from different sources: an example using paracetamol 1000 mg plus codeine 60 mg. BMC Med Res Methodol. 2001 Jan 10;1:1.
El señor de las curvas que tanto nos suenan
Fuente: Rafa Bravo

- Que al final del periodo de observación no todos los pacientes habrán presentado el evento objeto de estudio.
- Los pacientes se incorporan durante todo el periodo de observación, por lo que los últimos en hacerlo serán observados durante un periodo de tiempo menor que los que entraron al principio y por lo tanto la probabilidad de que les ocurra el suceso es menor.
- Que algunos pacientes se hayan perdido por causas diversas, no habiendo sido posible determinar su estado.
- Al final del estudio habrá pacientes que no presentan el suceso.