EVIDENCE-BASED CHILD HEALTH: A COCHRANE REVIEW JOURNALEvid.-Based Child Health 2: 1089–1090 (2007)Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/ebch.166

Tips and Tricks

Tips and tricks for understanding and using SR results.No. 7: time-to-event dataElvira C. van Dalen,1* Jayne F. Tierney,2 Leontien C. M. Kremer1,3

1Department of Paediatric Oncology, Emma Children’s Hospital/Academic Medical Center, University of Amsterdam, The Netherlands2Meta-analysis Group, MRC Clinical Trials Unit, London, UK3Department of Paediatrics, Emma Children’s Hospital/Academic Medical Center, University of Amsterdam, The Netherlands

This seventh article for ‘Tips and tricks for understand-ing and using SR results’ in Evidence-Based ChildHealth is, like the previous ones, aimed at helpingreaders to understand the results of systematic reviewsand to use the results in clinical practice. This time,we focus on the concepts of time-to-event data andmethods for pooling such data. The information in thisarticle is based on earlier papers, the Cochrane Hand-book, and the collective experience of the authors inteaching evidence-based medicine (1–9).

Understanding SR results: time-to-eventdata

Sometimes, the outcome of interest in a study involvesthe assessment of both whether a particular eventoccurs, and also when it occurs. In the statisticalfield, such time-to-event data are sometimes known assurvival data, since time to death is often the event ofinterest. For example, in cancer it may not be possibleto prevent death and cure a patient, but a new treatmentmight increase the duration of survival. Therefore,although similar numbers of deaths may be observedin both treatment groups, it is possible that one of theevaluated treatments will decrease the rate at whichthey take place. Time-to-event outcomes may be basedon events other than death, such as time to recurrenceof disease or time to discharge from hospital (1,2).

Time-to-event outcomes consist of two variablesper individual included in the study: (a) an indicatorof whether an event has occurred or not; and (b) anindicator of the length of time until an event occursor, if no event occurs, to the end of the observationperiod (1).

A key characteristic of time-to-event data is cen-soring. The term ‘censoring’ is used to indicate thatthe period of observation stopped before the event ofinterest occurred. Usually, this happens when an eventhas not taken place by the date of last follow-up, or

*Correspondence to: Elvira C. van Dalen, Department of PaediatricOncology, Emma Children’s Hospital/Academic Medical Center, Uni-versity of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, TheNetherlands. E-mail: e.c.vandalen@amc.uva.nl

alternatively, this can be the result of incomplete infor-mation, for example when a patient is lost to follow-up(3).

Meta-analyses of time-to-event data

There are different methods to perform meta-analysesof time-to-event data.

Sometimes time-to-event data can be analyzed asdichotomous data. However, in order for this analysisto give reliable results, the status of all patients in astudy should be known at a fixed time-point (i.e. nopatients lost to follow-up), since the effect measuresfor dichotomous data take no account of censoring.For example, if all patients have been followed forat least 12 months, and the proportion of patientsin which the event occurred before 12 months isknown for both groups, then use of the RelativeRisk (RR), Odds Ratio (OR) or Risk Difference(RD) may be appropriate (see ‘Tips and tricks’ no1 and no 2 (5,6) for additional information on theseoutcome measures) (1). However, if a substantialnumber of patients have been censored, then thisshould be accounted for, otherwise the comparabilityand possibly the reliability of the individual studyresults may be affected. This in turn could impacton the quality of any meta-analyses of these studies(3). Thus, when censoring of patients has occurred,analyzing time-to-event outcomes as though they aredichotomous is not appropriate. Furthermore, biascould arise if the time-points are subjectively chosenby the reviewer or selectively reported by the trialist attimes of maximal or minimal difference between theintervention groups (2).

Moreover, it is not appropriate to analyse time-to-event outcomes using methods for continuous out-comes (e.g. using mean time-to-event) as the relevanttimes are only known for the subset of participantswho have experienced an event. Censored patientscannot be included, even though the period of timethat they are event-free could contribute information.This may very well introduce bias (1). Median sur-vival times are also not reasonable surrogate measuresfor meta-analyses of survival outcomes (7).

Copyright 2007 John Wiley & Sons, Ltd.

1090 E. C. van Dalen et al.

The most appropriate way of summarizing time-to-event data is to use survival analysis methods andexpress the treatment effect as a Hazard Ratio (HR).The HR takes into account the number of events, thetime to these events and also the data from censoredpatients (1,3). HRs and the associated statistics can beestimated by carefully manipulating published or othersummary time-to-event data (2,4). These statistics canthen be entered into the RevMan software that isused for Cochrane reviews by two possible methods:(a) the generic inverse variance option, providing aHR; and (b) the individual patient data analysis option,providing a modified version of the Peto method fordichotomous data. In the latter option, note that wherethe output states OR, it is actually a HR. The methodused depends on what data have been extracted fromthe primary studies, or obtained from re-analysis ofindividual patient data, but the results should be thesame (1).

Interpretation of the HR

Hazard is similar in notion to risk, but is subtlydifferent in that it measures instantaneous risk and maychange continuously. A HR is interpreted in a similarway to a risk ratio, as it describes how many timesmore (or less) likely a participant is to experience theevent of interest if they receive the experimental ratherthan the control intervention, but over time rather thanat a fixed time point (1).

When the estimated effects are about the same forboth interventions, the HR will be close to 1. Ifthe experimental intervention is superior to that ofthe control intervention, the HR will be less than 1,whereas if the control group is superior to that ofpatients in the experimental group the HR will begreater than 1. For example, a HR of 0.35 suggeststhat the relative risk of an event with the experimentalintervention is 0.35 (35%) of that for the controlintervention. Another interpretation is that it representsa 65% reduction in the relative risk of the event withthe experimental intervention. If required, the HR canalso be translated into an absolute effect at a clinicallysignificant point in time (2). Keep in mind that theestimate of the HR has a 95% Confidence Interval(CI) around it (calculated by software packages likethe RevMan software used in Cochrane reviews).

Using SR results: time-to-event data

In the review of Okebe et al. (8), included in this issueof EBCH, survival data were analyzed as ORs insteadof HRs. For example, in the analysis of event-free sur-vival for the evaluation of ‘prespecified duration ver-sus duration determined by clinical response (remis-sion induction)’, the OR calculated by the reviewerswas 2.10 with a 95% CI of 0.93–4.74 (p = 0.07, i.e.

no significant evidence of effect is identified). How-ever, in the publication of Brecher et al. (9), which wasthe only study included in this meta-analysis, event-free survival was evaluated as a time-to-event outcome(using the logrank method) and a significant differencein favour of the intervention treatment (duration deter-mined by clinical response) was identified (p = 0.027;HR and 95% CI not stated).

This example showed that results of meta-analysescan be misleading if inappropriate effect measureshave been used.

Bottom line

Sometimes, the outcome of interest in a study involvesthe assessment of both whether a particular eventoccurs, and also when it occurs. These are calledtime-to-event outcomes and a key characteristic iscensoring, indicating that the period of observationwas stopped before the event of interest occurred. Themost appropriate way of summarizing time-to-eventdata is to express the treatment effect as a HR. TheHR describes how many times more (or less) likely aparticipant is to experience the event of interest overtime if they receive the experimental rather than thecontrol intervention.

References

1. Higgins JPT, Green S, eds. Cochrane Handbook for SystematicReviews of Interventions 4.2.6 (updated September 2006).http://www.cochrane.org/resources/handbook/hbook.htm.

2. Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practicalmethods for incorporating summary time-to-event data into meta-analysis. Trials. 2007; (Epub ahead of print).

3. Vale CL, Tierney JF, Stewart LA. Effects of adjusting for censoringon meta-analyses of time-to-event outcomes. Int J Epidemiol. 2002;31: 107–11.

4. Parmar MK, Torri V, Stewart L. Extracting summary statistics toperform meta-analyses of the published literature for survivalendpoints. Stat Med. 1998; 17: 2815–34.

5. Kremer LC, Moyer V. Tips and tricks for understanding and usingSR results. Evid-Based Child Health. 2006; 1: 356–8.

6. Kremer LC, Barrowman N. Tips and tricks for understanding andusing SR results – no. 2: odds and odds ratio. Evid-Based ChildHealth. 2006; 1: 732–3.

7. Michiels S, Piedbois P, Burdett S, Syz N, Stewart L, Pignon JP.Meta-analysis when only the median survival times are known: acomparison with individual patient data results. Int J Technol AssessHealth Care. 2005; 21: 119–25.

8. Okebe JU, Lasserson TJ, Meremikwu MM, Richards S. Therapeu-tic interventions for Burkitt’s lymphoma in children. CochraneDatabase of Systematic Reviews 2006, Issue 4. Art. No.: CD005198.DOI: 10.1002/14651858.CD005198.pub2.

9. Brecher ML, Schwenn MR, Coppes MJ, Bowman WP, Link MP,Berard CW, et al. Fractionated cyclophosphamide and back toback high dose methotrexate and cytosine arabinoside improvesoutcome in patients with stage III high grade small non-cleaved celllymphomas (SNCCL): a randomized trial of the Pediatric OncologyGroup. Med Pediatr Oncol. 1997; 29: 526–33.

Copyright 2007 John Wiley & Sons, Ltd. Evid.-Based Child Health 2: 1089–1090 (2007)DOI: 10.1002/ebch.166