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- Comprehensive meta analysis event rate inverse var trial#
- Comprehensive meta analysis event rate inverse var free#
Only sixteen of these estimated HRs and the remainder calculated ORs or RRs. For example, Isof the Cochrane Library contained 43 cancer meta-analyses based on published data that included an analysis of survival and were not conducted by the current authors. HRs can be estimated by carefully manipulating published or other summary data, but currently such methods are under-used in meta-analyses. Time-to-event outcomes are most appropriately analysed using hazard ratios (HRs), which take into account of the number and timing of events, and the time until last follow-up for each patient who has not experienced an event i.e. Furthermore, bias could arise if the time points are subjectively chosen by the systematic reviewer or selectively reported by the trialist at times of maximal or minimal difference between intervention groups. However, interpretation is difficult, particularly if individual trials do not contribute data at each time point.
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Alternatively, ORs or RRs can be calculated at specific points in time making estimates comparable and easier to interpret, at least at those time-points.
Comprehensive meta analysis event rate inverse var trial#
If the total number of events reported for each trial is used to calculate an OR or RR, this can involve combining trials reported at different stages of maturity, with variable follow up, resulting in an estimate that is both unreliable and difficult to interpret. Using such dichotomous measures in a meta-analysis of time-to-event outcomes can pose additional problems. Odds ratios (ORs) or relative risks (RRs) that measure only the number of events and take no account of when they occur are appropriate for measuring dichotomous outcomes, but less appropriate for analysing time-to-event outcomes.
Comprehensive meta analysis event rate inverse var free#
Other examples of outcomes where the timing of events may be vital in assessing the value of an intervention include: time free of seizures in epilepsy time to conception in fertility treatment time to resolution of symptoms of flu and time to fever in chickenpox. Therefore, although the same or similar number of deaths may be observed, it is hoped that a new intervention will decrease the rate at which they take place. For example, in cancer a cure may not be possible, but it is hoped that a new intervention will increase the duration of survival. Time-to-event outcomes take account of whether an event takes place and also the time at which the event occurs, such that both the event and the timing of the event are important.
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However, this practical guide should improve the quality of the analysis and subsequent interpretation of systematic reviews and meta-analyses that include time-to-event outcomes. The methods cannot circumvent the potential biases associated with relying on published data for systematic reviews and meta-analysis. The spreadsheet can be used to assist them in carrying out the calculations. When faced with particular circumstances, readers can refer to the relevant sections of the paper.
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ResultsĪ wider audience should be able to understand published time-to-event data in individual trial reports and use it more appropriately in meta-analysis. This paper aims to 'translate' the methods for estimating a HR and associated statistics from published time-to-event-analyses into less statistical and more practical guidance and provide a corresponding, easy-to-use calculations spreadsheet, to facilitate the computational aspects. Awareness and adoption of these methods is somewhat limited, perhaps because they are published in the statistical literature using statistical notation. In the absence of individual patient data (IPD), methods are available to obtain HRs and/or associated statistics by carefully manipulating published or other summary data. In systematic reviews and meta-analyses, time-to-event outcomes are most appropriately analysed using hazard ratios (HRs).