Analyse dichotomer Endpunkte

Präsentation zum Thema: "Analyse dichotomer Endpunkte"—  Präsentation transkript:

1 Analyse dichotomer Endpunkte
To begin looking at analysis of outcomes in your review, we’re going to start with one of the most common outcome types that you may come across – dichotomous outcomes.

2 Schritte eines Systematic Review
Fragestellung festlegen Auswahlkriterien definieren Methoden definieren Studien suchen Auswahlkriterien anwenden Daten extrahieren Bias-Risiko der Studien bewerten Ergebnisse analysieren und darstellen Ergebnisse interpretieren und Schlussfolgerungen ziehen Review optimieren und aktualisieren

3 Studienebene ↓ Reviewebene ↓ Studie A Studie B Effektmaß Studie C
Ergebnisse Studie A Effektmaß Ergebnisse Studie B Effektmaß Effektmaß Ergebnisse Studie C When we’ve collected the data from a study, we need to organise it and make sense of it. Each study measures outcomes among its individual participants, and reports those outcomes in various ways. For each study in your review, you will select an effect measure – a statistic to represent the effect of the intervention on the people in that study, such as an odds ratio. It might be the effect measure reported in the study itself, or you might choose a different effect measure that you think is more useful. [Click] We have to do this for each included study in your review – if possible, so that we have a comparable set of effect measures for each study. [Click] Ultimately, our aim will be to synthesise those results across all the studies, and report an overall effect for the intervention. We’ll come back to that stage of the process later. For now, we will look at some of the different types of outcome data you are likely to find at the study level, and how we can select and calculate appropriate effect measures. We’re going to start by talking about the analysis of dichotomous outcomes. Effektmaß Ergebnisse Studie D Quelle: Jo McKenzie & Miranda Cumpston

4 Überblick Wahrscheinlichkeiten ausdrücken: Risiko und Odds
Effektmaße beim Vergleich von Gruppen Auswahl eines Effektmaßes Daten extrahieren für dichotome Endpunkte Siehe Kapitel 7 & 9 im Handbuch

5 Was sind dichotome Endpunkte?
Wenn der Endpunkt für jede/n TeilnehmerIn eine von zwei Möglichkeiten oder Ereignissen ist Lebendig oder tot Geheilt oder nicht geheilt Schwanger oder nicht schwanger Truly dichotomous data is outcomes in which every participant is in one of two possible groups – either with the event of interest or without, yes or no.

6 Wie hoch war die Wahrscheinlichkeit dafür?
Risiko und Odds drücken Wahrscheinlichkeit in Zahlen aus bei dichotomen Endpunkten die Wahrscheinlichkeit innerhalb einer Gruppe, in einem von zwei Zuständen zu sein werden unterschiedlich berechnet, unterschiedliche statistische Bedeutung In general conversation the terms ‘risk’ and ‘odds’ are used interchangeably (as are the terms ‘chance’, ‘probability’ and ‘likelihood’). In statistics, however, risk and odds have particular meanings and are calculated in different ways. although they are both ways of expressed the chance of being in one of two groups – with the dichotomous outcome, or without. It’s important to be clear about the difference between risk and odds, and which one you’re using.

7 Risiko 24 Leute tranken Kaffee 6 bekamen Kopfschmerzen
Risiko für Kopfschmerzen = 6 Leute mit Kopfschmerzen / 24 Leute, die sie hätten bekommen können = 6/24 = ¼ = 0.25 = 25% Risiko = Anzahl TeilnehmerInnen mit Ereignis Gesamtzahl der TeilnehmerInnen Risk is the concept more familiar to patients and health professionals. Risk describes the probability with which a health outcome or event will occur. Risk is very easy to calculate: in this case, we have a group of 24 people who drank coffee, and 6 of them had a headache. So we take the number of people with a headache, and divide it by the total number of people – in this case, 6 out of 24, or 25%. Risk is often expressed as a percentage, but you can also express it as a decimal, in this case, or as a number out of 100 or 1,000 people.

8 Odds 24 Leute tranken Kaffee 6 bekamen Kopfschmerzen
Odds für Kopfschmerzen = 6 Leute mit Kopfschmerzen /18 ohne Kopfschmerzen = 6/18 = 1/3 = 0.33 = 1:3 (keine Angabe in %) Odds = Anzahl TeilnehmerInnen mit Ereignis Anzahl TeilnehmerInnen ohne Ereignis Odds is slightly different, but just as easy to calculate. It’s probably a concept that is more familiar to gamblers. To calculate the odds, we take the number of people with a headache, and divide it not by the total number, but by the number of people without a headache. So in this case, 6 people with a headache, divided by 18 people without a headache, or Odds are not usually expressed as a percentage, but you can express them as a ratio – in this case 1:3.

9 Wie stark unterscheiden sich Risiko und Odds?
Zwei Beispiele aus Studien zu Koffein 5 Leute mit “Kopfschmerzen” von 65 Wahrscheinlichkeit Kopfschmerzen zu bekommen Risiko = 5/65 = Odds = 5/60 = 0.083 130 Leute sind “noch wach” von 165 Wahrscheinlichkeit, noch wach zu sein Risiko = 130/165 = 0.79 Odds = 130/35 = 3.71 It’s important to be clear about whether you’re using risk or odds, because they are not always the same. As you can see from the first example here, when the event is rare (5 out of 65 people with the event, in this case a headache), the risk and odds will be very similar – risk is and odds are CLICK However, when the event is common, such as 130 people out of 165 ‘still awake’ at the end of the trial, risk and odds will be very different. In this case, risk is 0.79, and odds are 3.71.

10 In Worten ausgedrückt Risiko Odds
Das Kopfschmerzrisiko war eins von vier, oder 25% Odds Die Odds für Kopfschmerzen war ein Drittel der Odds, keine Kopfschmerzen zu bekommen Pro Person mit Kopfschmerzen hatten 3 Personen keine Kopfschmerzen Die Odds Kopfschmerzen zu bekommen lag bei 1 zu 3. These are some examples of ways in which we can talk about risk and odds, while being specific about which measure we are using.

11 Übersicht Wahrscheinlichkeit ausdrücken: Risiko und Odds
Effektmaße beim Vergleich von Gruppen Auswahl eines Effektmaßes Daten extrahieren für dichotome Endpunkte So, we can calculate risk and odds in a group of people, some of whom experience an event. In a trial, what we usually want to do is compare the chances of an event between two groups (such as the intervention and control groups), to decide which of the two groups has a better outcome. For that, we need to use effect measures that compare the risk or odds between two groups.

12 Vergleich zweier Gruppen
Kopfschmerz Kein Kopfschmerz Total Koffein 17 51 68 Koffeinfrei 9 55 64 26 106 132 All the information you need to calculate measures for dichotomous outcomes can be expressed in a 2x2 table. ASK: Has anyone seen this kind of table before? ASK: Can anyone suggest any of the effect measures we might use to compare two groups with each other?

13 Vergleich zweier Gruppen
Effektmaße Relatives Risiko (RR) (Risikoverhältnis) Odds ratio (OR) (Oddsverhältnis) Risikodifferenz (RD) (absolute Risikoreduktion) Schätzungen sind immer unsicher und sollten daher mit einem Konfidenzintervall angegeben werden There are three main effect measures we use to express the difference between two groups for a dichotomous outcome: risk ratios (also known as relative risk), odds ratios and risk difference (also known as absolute risk reduction). Whichever one you use, remember that all measures of effect are uncertain – we should never present the result of any study without a measure of uncertainty, preferably a confidence interval. Both the effect measure and measures of uncertainty can be calculated from a 2x2 table. Note: Number Needed to Treat (NNT) is a popular measure of the effect of an intervention, giving the number of people who need to receive the intervention for one additional person to experience or avoid a particular event. NNT can’t be used directly in a meta-analysis. You don’t have to report the NNT in your review, but if you would like to, you’ll need to use one of these other statistics for your analysis, and then calculate the NNT at the end to report it in your review.

14 Risikoverhältnis (RR)
Ereignisrisiko mit Intervention = 17/68 Ereignisrisiko mit Kontrolle = 9/64 Risikoverhältnis= Risiko mit Intervention Risiko mit Kontrollrisiko = 17/68 = = 1.79 9/ Bei einem Risikoverhältnis (RR) = 1 gibt es keinen Unterschied zwischen den Gruppen Kofpschmerz Kein Kopfschmerz Total Koffein 17 51 68 Koffeinfrei 9 55 64 26 106 132 The risk ratio simply divides the risk in the intervention group by the risk in the control group. In this case, the risk in the intervention group is 17/68 or 0.25, and the risk in the control group is 9/64 or We take the intervention risk, 0.25 and divide it by the control risk, 0.14, and get the answer 1.79. If you’re comparing two active interventions, it’s up to you to decide which intervention you’re going to treat as the intervention and which you will treat as the control in this calculation – remember to be consistent throughout the review, and make sure you’re interpreting the result correctly for each group in that case. Where RR is less than 1, this implies that the intervention reduced the number of events compared to the control. If the RR is greater than 1, the intervention increased the number of events. If the RR is equal to 1, this implies there was no difference between the two groups – 1 is our point of no effect for risk ratios. ASK: What does this RR of 1.79 mean?

15 In Worten ausgedrückt Risikoverhältnis (RR) 1.79
Das Risiko unter Behandlung Kopfschmerzen zu bekommen war 179% des Risikos in der Kontrollgruppe Die Intervention erhöhte das Risiko für Kopfschmerzen um 79% Oder bei einer Risikoverminderung: Risikoverhältnis (RR) 0.79 Das Risiko unter Behandlung Kopfschmerzen zu bekommen war 79% des Risikos in der Kontrollgruppe Die Intervention verringerte das Kopfschmerzrisiko um 21% These are some examples of the ways we can talk about RR. For our calculated RR of 1.79, this means the intervention increased the number of headaches by 79%, or that the risk in the intervention group was 179% of the control. Hypothetically, if we had found a decreased risk, say a RR of 0.79, less than one, we could say that the chances of having a headache in the intervention group were 79% of the chances in the control group, or that the intervention reduced the risk of headache by 21%. This is can also be called the relative risk reduction.

16 Wenn die odds ratio = 1 beträgt, besteht kein Unterschied
Odds für Ereignis mit Intervention = 17/51 mit Kontrolle= 9/55 Odds ratio = Odds mit Intervention Odds mit Kontrolle = 17/51 = = 2.06 9/ Wenn die odds ratio = 1 beträgt, besteht kein Unterschied zwischen den Gruppen Kopfschmerz Kein Kopfschmerz Total Koffein 17 51 68 Koffeinfrei 9 55 64 26 106 132 The odds ratio is very similar – instead of dividing the risk by the risk, we divide the odds in the intervention group by the odds in the control group. In this case, an odds for the intervention group of 0.33 divided by the control group odds of 0.16, gives an odds ratio of 2.06. Just like a RR, where OR is less than 1, this implies that the intervention reduced the number of events compared to the control. If the OR is greater than 1, the intervention increased the number of events. If the OR are equal to 1, this implies there was no difference between the two groups, and therefore no intervention effect. ASK: What does this OR of 2.06 mean?

17 In Worten ausgedrückt Odds ratio 2.06
Die Intervention hat die Odds für Kopfschmerzen in etwa verdoppelt Die Intervention erhöhte die Odds auf 206% der Odds der Kontrollgruppe Die Intervention erhöhte die Odds für Kopfschmerzen um 106% Oder bei einer Reduktion der Odds: Odds ratio 0.06 Die Intervention verringerte die Odds für Kopfschmerzen zu auf 6% der Odds der Kontrollgruppe Die Intervention verringerte die Odds für Kopfschmerzen um 94% These are some examples of the ways we can talk about OR. In this case, our OR of 2.06 means the intervention doubled the odds of headache. We can say the odds in the intervention group were increased to 206% of the odds in the control group, or increased by 106%. Hypothetically, if we had found a result indicating fewer headaches in the intervention group, or a number less than 1, say an odds of 0.06, we could say that the intervention had reduced the odds of headache to 6% of the odds in the control group, or reduced the odds by 94%. You can see that the way we express RR and OR are not very different, but we do need to be careful to be specific about which we are reporting. Misinterpreting an OR as an RR can lead to overestimating or underestimating the effect, especially for common events.

18 Risikodifferenz Ereignisrisiko mit Intervention = 17/68
Ereignisrisiko mit Kontrolle = 9/64 Risikodifferenz = Risiko mit Intervention – Risiko mit Kontrolle = 17/68 – 9/64 = 0.25 – 0.14 = 0.11 Wenn die Risikodifferenz = 0 ist, besteht kein Unterschied zwischen den Gruppen Kopfschmerz Kein Kopfschmerz Total Koffein 17 51 68 Koffeinfrei 9 55 64 26 106 132 The third effect measure that can be used to measure dichotomous outcomes is the Risk Difference. The previous two measures, RR and OR, have been ratios, which give relative measures of the effect, dividing one by the other. RD is an absolute measure, giving you the absolute difference between the risks in each group. All we do is subtract – take the risk in the intervention group, and subtract the risk in the control group. In this case, 0.25 – 0.14, which gives a RD of 0.11. You cannot use odds to calculate a corresponding Odds Difference. For absolute measures, our point of no effect is different. For ratios, the point of no effect is 1, but for RD the point of no effect is 0. Where risk difference is less than 0, this implies that the intervention reduced the number of events compared to the control. If the risk difference is greater than 0, the intervention increased the number of events. If the risk difference is equal to 0, this implies there was no difference between the two groups, and therefore no intervention effect. ASK: What does this RD of 0.11 mean?

19 In Worten ausgedrückt Risikodifferenz 0.11 Oder bei Risikoreduktion:
Die Intervention erhöhte das Kopfschmerzrisiko um 11 Prozentpunkte 14 von 100 Leuten ohne Koffein bekamen Kopfschmerzen. Mit Koffein bekamen 11 Leute mehr Kopfschmerzen. Oder bei Risikoreduktion: Risikodifferenz -0.11 Die Intervention verringerte das Kopfschmerzrisiko um 11 Prozentpunkte 14 von 100 Leuten ohne Koffein bekamen Kopfschmerzen. Mit Koffein bekamen 11 Leute weniger Kopfschmerzen. It’s common to talk about RD in terms of ‘percentage points’, to make it clear that we’re not speaking in relative terms, so we could say our RD of 0.11 means the intervention increased the risk of headache by 11 percentage points. 11 percentage points is not 11% of the control value, but actually 11 higher than the control value. An alternative way to express absolute differences is to talk about them in natural frequencies – for example, numbers out of 100, or per 1000, or per million. Consumers find these values particularly easy to understand. In this case, we would start by noting that 14 out of 100 people had a headache in the control group – this represents what would happen anyway, without the intervention. With caffeine, 11 more people experienced a headache (making a total of 25 out of 100). Hypothetically, if we had a reduction in headaches, say a RD of -0.11, we might say that the intervention reduced the risk of headache by 11 percentage points. This can also be called the absolute risk reduction. We could also say that, in this case, 11 fewer people had a headache with caffeine.

20 Jetzt sind Sie dran! Berechnen Sie:
Ereignis Kein Ereignis Total Behandlung 2 8 10 Kontrolle 5 7 13 20 Berechnen Sie: RR für den Effekt einer Behandlung auf die Wahrscheinlichkeit eines Ereignisses Odds ratio für den Effekt einer Behandlung auf die Odds eines Ereignisses Drücken Sie das Ergebnis in Worten aus

21 Lösung RR OR [Note to trainers: you may wish to delete this slide from the printed materials for your workshop.] Risk ratio the risk of an event with the intervention was 40% of the risk with the control the intervention reduced the risk to 40% of what it was treatment reduced the risk by 60% Odds ratio the intervention reduced the odds of an event to 25% of what they were treatment reduced the odds by 75%

22 Übersicht Wahrscheinlichkeit ausdrücken: Risiko und Odds
Effektmaße beim Vergleich von Gruppen Auswahl eines Effektmaßes Daten extrahieren für dichotome Endpunkte So, we have three effect measures available for dichotomous outcomes. How do you decide which effect measure to use for your review?

23 Auswahl des Effektmaßes
Darstellung des Effekts BenutzerIn muss das Ergebnis verstehen und anwenden können Konstantes Effektmaß unabhängig vom Basisrisiko Anwendbarkeit auf verschiedene Populationen und Settings Mathematische Eigenschaften All the effect measures are equally valid, but there are some properties that we might wish to consider when choosing which statistic to use: communication - everyone knows what it means and it is useful for communicating treatment effects in practice consistency of effect - it would be nice to have one number we can take away and use in a range of populations, that may be different from the population or context in which the study was conducted easy to manipulate mathematically Your CRG may have a policy on which to use – in which case, take their advice. Evtl. hat Ihre Review-Gruppe eine Richtlinie

24 Darstellung OR ist schwer verständlich und wird oft fehlinterpretiert
RR ist einfacher zu verstehen, aber auch relativ Kann eine sehr kleine oder sehr große Veränderung bedeuten RD ist am einfachsten zu verstehen Absolutes Maß der eigentlichen Risikoveränderung Einfach umzuwandeln in absolute Häufigkeiten oder NNT People find risk a more intuitive concept, and often interpret odds ratios as if they were risk ratios. This may be a problem for frequent events, where the statistics are not the same, and may lead readers to exaggerate the treatment effect. RR is easier, but as a relative measure, it can mean a very big or very small change, depending on the underlying risk. For example, 50% decrease in risk is very important when coming from a baseline of 80% to 40%, but less so when going from a baseline risk of 4% to 2%. Absolute measures like RD are often most easily interpreted by clinicians and patients, as they give clear estimations of actual risk, and can be expressed as natural frequencies, such as 11 fewer people out of 100 experiencing a headache. The NNT can be easily calculated as 1/|RD| (1 divided by the absolute value of the RD), although it can also be calculated from other statistics.

25 The amount of variation in the effect measure (RR, OR, RD) across a set of studies conducted in different times, places and populations depends on which effect measure you choose. This is mainly due to changes in the underlying event rates across studies e.g. some populations may have high underlying death rates and some lower rates. A study of meta-analyses in the Cochrane Library shows that, in general OR and RR are less variable across different populations – that is, in general, relative measures like OR and RR tend to stay more stable when applied to different populations, which also makes it easier for readers to apply the results of the review to their own population. RD varies more widely from population to population, and must be interpreted in the context of the underlying risk of the population being considered. For example, a risk difference of 0.02 (or 2%) may represent a small, clinically insignificant change from a risk of 58% to 60% or a proportionally much larger and potentially important change from 1% to 3%. The absolute value is likely to be different in every population, and different again in the reader’s population. Sometimes this can cause problems – for example, if the RD is -11%, and the reader’s population has an underlying risk of 5%, then their risk with the intervention would be calculated as -6% risk of the event occurring, which doesn’t make sense. This variation is an important consideration if you’re using the RD to communicate absolute effects, or to calculate other statistics such as NNT – you may need to consider calculating multiple statistics for different levels of assumed underlying risk, or using a relative statistic to calculate the NNT instead. Konsistenz Ereignisraten variieren zwischen Studien innerhalb eines Reviews Studie über Meta-Analysen in der Cochrane Library: RR und OR sind weniger variabel zwischen verschiedenen Populationen RD ist variabler, abhängig vom Basisrisiko Die LeserInnen werden die Ergebnisse auf ihre eigene Population anwenden, die sich ggf. unterscheidet Source: Deeks JJ. Issues in the selection of a summary statistic for meta-analysis of clinical trials with binary outcomes. Statistics in Medicine 2002; 21: 25

26 Mathematische Eigenschaften
Das Ereignis definieren Gut oder schlecht, Vorhandensein oder Fehlen? Legen Sie dies im Voraus fest OR und RD werden durch Wahl nicht beeinflusst, RR ist hiervon abhängig Unbegrenzte Werte OR ist das einzige unbegrenzte Effektmaß The first issue arising from the mathematical properties of the effect measures is the importance of defining your event. You have the option to define your event of interest as the desirable outcome or the undesirable outcome – e.g. people who made it to the bottom of the ski slope, or people who didn’t make it; people with headache, or people without headache. For OR and RD the effect measure is stable, but reversed, when you change the event: e.g. OR 0.5 for bad event corresponds to OR 2.0 for good (in general 1/OR for good event = OR for bad event). For RD, if we have RD of 5 percentage points for a good event, we get –5 percentage points for a bad event (in general, the same but different direction , +/-) RR is not so straightforward – changing the event changes the size and significance of your intervention effect. Think carefully about which outcome makes the most sense to you to measure and report. It’s also the case that, while RR and RD are limited in the maximum values they can take, OR is unbounded (that is, it can go up to infinity). There is no clear consensus on the importance of these two factors.

27 Zusammenfassung OR RR RD Darstellung    Konsistenz Mathematik
As mentioned before, your CRG may have a preferred measure. If not, RR is generally the best default setting for most outcomes.

28 Übersicht Wahrscheinlichkeiten ausdrücken: Risiko und Odds
Effektmaße beim Vergleich von Gruppen Auswahl eines Effektmaßes Daten extrahieren für dichotome Endpunkte Going back to our data collection – what data do we need to collect from the included studies to analyses a dichotomous outcome?

29 Datenextraktion Vier Zahlen sind nötig für Effektmaß und Varianz:
Kopfschmerz kein Kopfschmerz Total Koffein 17 51 68 Koffeinfrei 9 55 64 26 106 132 If they’re available, the 4 numbers shaded are all you need to collect from your included studies – the number of events in each group, and the total number of people in each group. The Cochrane review software, RevMan, will calculate the risk ratio or odds ratio, as well as a confidence interval as a measure of uncertainty, based on these numbers. CLICK It’s not always clear whether the total number of people for whom the outcome was measured is the same as the total sample size at any given time point. Where possible, collect the actual number of people who were measured for each outcome, at each time point. If that information is not available in the study, that’s ok – you can use the total sample size for the group, or you can contact the author to request more information. The reason we try to use the number actually measured relates to the issue of intention-to-treat analysis. Wherever possible, we want complete data about the whole sample, but when people are missing, we need to make sure that we are aware of that. If we don’t have measurements on some people, and we use the total sample size as our N, then we are effectively imputing data – making an assumption that none of the missing people had a headache, or experienced the event you are measuring. This may or may not be a plausible assumption, so it’s important to be clear on the decisions you’re making, and get statistical advice if you want to know more about imputation. Versuchen Sie die konkrete Anzahl für jeden Endpunkt zu jedem Zeitpunkt zu extrahieren

30 Auch andere Datenformate können verwendet werden
Prozentangaben Anzahl der Ereignisse kann berechnet werden, wenn die Stichprobengröße bekannt ist Berechnetes Effektmaß der Studie (z.B. OR, RR) Wenn die Ergebnisse für die Gruppen nicht einzeln berichtet werden Kann in die Meta-Analyse eingeschlossen werden durch Nutzung der “Generic Inverse Variance Method” Erfordert ein Maß der Varianz (z.B. SE, 95% CI) Those four numbers may be the most straightforward way to analyse dichotomous data, but it’s very important to remember that if your study does not report those four numbers, it doesn’t mean you can’t use the data reported in the study. There are other ways the outcome can be reported that are just as useful. For example, if the study doesn’t report the actual number of events in each group, but reports a percentage, this can be easily converted to an absolute number if you have the sample size for each group. Alternatively, your study might not provide separate data for the intervention and control groups at all, but might report the overall effect estimate – such as the OR or RR. You can use these effect estimates in a meta-analysis, too, as long as they have been reported with a measure of variance, such as the standard error or a confidence interval can be used for this. When we enter the usual numbers of events and people, RevMan uses these to calculate the OR or the RR, along with the variance or uncertainty for each study. In effect, studies that report effect estimates have done these calculations for you. Because they’re in a different format, these studies need to be entered using a different outcome type in RevMan, not the usual Dichotomous outcome type, called the generic inverse variance method. An explanation of the generic inverse variance method, and what to do if you have an overall effect estimate instead of separate data for each group, will be given later in a separate presentation on analysing non-standard data.

31 Was gehört ins Protokoll?
Die Effektmaße, die für dichotome Daten verwendet werden sollen

32 Fazit Risiken und Odds sind zwei Arten Wahrscheinlichkeit auszudrücken
Risikoverhältnis (RR), odds ratio und Risikodifferenz vergleichen die Wahrscheinlichkeiten zweier Gruppen Um dichotome Daten zu nutzen, werden die Anzahl der Ereignisse und die Gesamtanzahl der TeilnehmerInnen in jeder Gruppe benötigt

33 Quellen Higgins JPT, Deeks JJ (editors). Chapter 7: Selecting studies and collecting data. In: Higgins JPT, Green S (editors). Cochrane Handbook for Systematic Reviews of Interventions Version [updated March 2011]. The Cochrane Collaboration, Available from Deeks JJ, Higgins JPT, Altman DG (editors). Chapter 9: Analysing data and undertaking meta-analyses. In: Higgins JPT, Green S (editors). Cochrane Handbook for Systematic Reviews of Interventions Version [updated March 2011]. The Cochrane Collaboration, Available from Danksagung Zusammengestellt von Miranda Cumpston Basierend auf Unterlagen von Sally Hopewell, Phil Alderson, Jon Deeks, Deborah Caldwell, Cochrane Statistical Methods Group & Australasian Cochrane Zentrums Englische Version freigegeben vomCochrane Methods Board Übersetzt in Kooperation zwischen dem Deutschen Cochrane Zentrum (Jörg Meerpohl, Laura Cabrera, Patrick Oeller), der Österreichischen Cochrane Zweigstelle (Barbara Nußbaumer, Peter Mahlknecht, Isolde Sommer, Jörg Wipplinger) und Cochrane Schweiz (Erik von Elm, Theresa Bengough)