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Im currently working on a meta-analysis of randomized controlled trials (RCTs).

These RCTs have reported their outcomes at different time points.

I decided to split the data of these studies into sub-categories, depending on the time point at which they reported their data.

These sub-categories are: 1 months, 3 months, 6 months, 12 months, 24 months, and 48 months.

After that, I ran an analysis on the whole data-set to generate a pooled effect estimate.

My problem is: The sub-categories of 12 months and beyond contain only a single study(see the picture provided below).

Do I have to cancel these sub-categories from the analysis since they do not represent pooled data? or is it correct to keep them since the primary intent is to analyze the whole data-set instead of an individual sub-category? enter image description here

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  • $\begingroup$ What is your purpose? Clearly there isn't enough data to do meaningful meta-analysis for the longer time periods. You could still report the estimate from the single study, or include time in a hierarchical model so that the 12 & 24 month estimates 'borrow' some information from the other time periods via partial pooling. $\endgroup$
    – mkt
    Jun 23 at 17:40
  • $\begingroup$ The main purpose of doing this is to have a pooled estimate that is conclusive of all data reported by the included studies. Im not interested in the subgroups individually but rather the pooled estimate of all the subgroups. However, Im not sure if this is methodologically correct. $\endgroup$ Jun 23 at 18:32
  • $\begingroup$ What do you mean by a hierarchal model? Im sorry, Im not really an expert. Do you mean pooling the studies without subgrouping? $\endgroup$ Jun 23 at 18:35
  • $\begingroup$ Not exactly, no. Look up the mixed-model, multilevel-analysis and hierarchical-bayesian tags to understand these types of models better. $\endgroup$
    – mkt
    Jun 23 at 18:48
  • $\begingroup$ This one is a reasonable starting point: stats.stackexchange.com/questions/21760/… $\endgroup$
    – mkt
    Jun 23 at 18:50

1 Answer 1

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To properly answer this question, we have to back to the basics of the meta-analysis methods employed. The analyses assume that the units (e.g. randomized patients) are independent from each other. In this case, they are not as the same patients were evaluated at different time points (e.g. 1, 3, 6 months, etc.). Therefore each patient/ study should be included in the analysis only once. That can be the longest follow-up reported for each study, it can be the longest follow-up across studies, etc. Clinical expectations also plays an important role here as the interventional effect may not be expected to last once the intervention is no longer given.

The above is one option but there are others. For example, you could decide to present each subgroup (e.g. time period) separately without pooling across the subgroups. You can also present bother (e.g. pooled effect at longest time period reported per trial + subgroups based on time periods without pooling across time periods) in separate forest plots.

Another option is to use a hierarchical model, or simulate it by first pooling studies with multiple time periods into a single study. For this not to have the same issues as before (e.g. unit of analysis error), you will have to divide the number of participants across the number of times the study is being included in the analysis (e.g. 30 patients over three time periods = 10 patients per time period). That will prevent over-estimating the precision of the trial after pooling. Of course, this method doesn't come without assumptions. For example you assume that length of following is independent from the effect estimates and the differences based on time periods is due to random chance.

In practice, we usually use the longest follow-up per trial and perform a subgroup analysis separately for hypothesis generation.

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    $\begingroup$ This is the answer I was looking for. I would really like to express my gratitude for your generous time and effort. Thank you. $\endgroup$ Jun 28 at 7:46
  • $\begingroup$ Okay suppose that I have done a forest plot of the subgroups without pooling the subgroups together. Does the issue of having one study being repeated in different time points (and even being alone in some time points) remains an issue? $\endgroup$ Jun 28 at 7:59
  • $\begingroup$ Sorry, I forgot to answer your question on a single studies in a forest plot. No it is not a problem at all as the study will get 100% of the weight. In a purely statistical fashion there is no meta-analysis, as this requires data from 2 or more studies to be meta-analyzed/ pooled, but from a practical perspective there are no issues with presenting only one study in a forest plot or subgroup analysis. That is a common scenario and what happened in my 1st Cochrane review years ago. $\endgroup$
    – abousetta
    Jun 28 at 8:03
  • $\begingroup$ As long as the study participants are only included once in an analysis then you are not double-counting. So the analysis you presented is correct with the exception of the overall pooled analysis even though for example some studies studies provided evidence for multiple time points are you are not pooling across time points. $\endgroup$
    – abousetta
    Jun 28 at 8:05
  • $\begingroup$ I'm glad my response was helpful and please feel free to reach out if you have any other questions. $\endgroup$
    – abousetta
    Jun 28 at 8:06

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