In this section we're going to review the information that should be included in the report of a trial, according to the consort guidelines. So, the consort guidelines start with the title of the report,and the title of the report should be succinct and this is not part of consort, but it's a good idea to make it succinct because you are usually limited on the number of words you can have in the title. And if the title's too long, and people start to ignore part of the title, and the consort statement says that, k design terms should be included in the title, such as trial, and randomized, and the title should also include the treatments that are being evaluated. And the disease or population that's being studied. And this is helpful so that consumers can scan titles when searching for relevant literature. And here I've included an example of a title that I think is a well-done title. This is the title of a primary results paper for the must trial that you've heard Janet and me talk about earlier in the course. And the title was, Randomized Comparison of Systemic Anti-Inflammatory Therapy ,versus Fluocinolone Acetonide Implant for Intermediate, Posterior, and Panuveitis: The Multicenter Uveitis Steroid Treatment Trial. And you'll notice that it does have key design terms, both randomized and trial. It tells the treatments that we're evaluating, systemic therapy versus,the implant and it also talks about the population that we're studying and people with intermediate posterior or panuveitis. And as I mentioned there is a separate consort statement about writing abstracts, but there's also a bit about writing abstracts in the main consort statement. And abstracts are extremely important because they are the key to the future of the paper. For the National Library of Medicine. The abstract is the primary source for indexing terms. And it's also what consumers usually read first, and sometimes it's the only thing that consumers read. So abstracts, according to CONSORT, should be structured, no free form abstracts. And they need to have a design statement, a method statement, results, and conclusions. And the specific structure will be dictated to some extent by the journal that you submit to, but most of them follow this basic structure. And again, with abstracts like the titles, it is important to be syched because you'll usually only get 200 to 300 words for the abstract. And those words have to be used to basically summarize the entire study. And now we're going to move on to the body of the paper. CONSORT has guidelines on how to write the introduction. The introduction should talk about the background in rationale, and describe why the trial was being done. And it also needs to establish equipoise. You'll remember from our ethics lecture that we talked about the Declaration of Helsinki which says that ethical research should not put people at unneccessary risk and if we cannot establish equipoise than we are putting people at unneccessary risk if one of the treatments has obviously already shown to be better than the other. And in the 2010 consort guidelines, they added that ideally, the introduction should include a systematic review of the literature that's been published up to the point where the trial began. And in the introduction section, we also need to state what our objectives are for the trial and our hypotheses. Now we'll move on to the method section of the paper and this is the dry part that's usually written in small print and some people skip this part completely. But hopefully after you've taken this class you'll read this part, much more carefully because the focus of our class has really been on the methods of conducting clinical trials. So the consort statement doesn't have a guideline about ,a statement in the methods on IRB review and approvals at the different clinics, but most journals do require, that you have a statement in their on where the protocol was reviewed and that it was approved by local IRB's., You also need to have a description of the trial design, including the allocation ratio. The eligibility criteria should be explicitly defined. And you need to mention if there were any issues in executing the criteria. You need to describe the intervention in enough details so that it can be replicated and how much detail is needed, will depend on intervention itself. If it's the standard intervention, you may need only a little bit of detail. But if it's a new surgery, for example, you might need quite a lot of detail in this section. You need to describe the hierarchy of outcomes. What are the primary outcomes and the secondary outcomes. And you need to describe how each of these outcomes was assessed and how they were defined. You need to have information on how the sample size was determined and all assumptions that went into determining that sample size. So that a reader can look over your assumptions and see if they think they look reasonable. And that includes a description of what detectable difference you used in your sample size, so that the reader can see if that was a clinically important difference or if it's too large to be important. You'll also need to describe if there were interim analyses and if the sample size calculation was adjusted for interim analyses. And you need to talk about any important changes that happened during a trial. It is not uncommon, at all, for changes to happen during a trial because of information that is gained during the trial or information outside of the trial and we talked about this some in our data monitoring lecture. And you just need to talk about what those changes were and how you think they affected the trial. The writer needs to describe the randomization. How it was generated? Who generated it? And whether there was blocking and stratification? The writer also needs to discuss allocation concealment, and how the randomization was implemented. So what we need to know here, is whether it was. Possible to protect future randomizations. So here, we're trying to establish that there was not selection bias. And this doesn't have to be a detailed description. It can be as simple as saying, that after confirming eligibility, the clinic staff obtained treatment assignment centrally using a data system. As long as we're clear that the clinic staff did not have access. To the treatment assignment list. Before the randomization was received. The writer needs to discuss masking, whether or not there was masking, and if so who was masked, and how masking was achieved. Did you use over encapsulation or matching placebos? Or where there sham surgeries? The writer has to describe the statistical methods, the methods for the primary and secondary outcomes. So that the reader can decide whether or not they did the analysis according to the design of the study. They also need to discuss any subgroup analyses that were done. And how they were done, so that the reader can see if the analysis were performed appropriately. And whether or not they were specified ahead of time or post talk. We talked about subgroup analyses and it's fine to do both analyses that are preplanned and analyses that are post talked. We just need to be clear on which are which. And also if you have any adjusted analysises you need to state why those specific recoveries were chosen for adjusted analysises. And that's the end of the methods section. And I mentioned that's a section that a lot of people skim over but it's an important. Section, because the reviewers of your article are going to be reading it very critically to decide whether or not your article will be accepted for publication. So one of the important contributions of consort guidelines, has been to promote the use of flow charts to describe how patients entered, exited, and were treated during the trial. And here I have an example of a CONSORT flowchart from the CONSORT website, and you can see at the top that CONSORT recommends that you describe the people who were assesessed for eligibility and why those people were not eligible. And then the people who were randomized, how many were allocated to each treatment group. And then within those groups, how many actually received the allocated intervention? Because you almost always had at least one person who for some reason was allocated to one treatment group or the other, and did not receive their allocated treatment. And then you described how people, went through the follow up process. Who was lost to follow up, and who discontinued the intervention for various reasons? And here I have an example consort diagram for a trial that was done in the center for treatment of asthma. And at the top we have data on 1,309 potential participants that were assessed for eligibility. And this part of the CONSORT dot flow chart is not always included in the paper, because you can't always know exactly how many people were assessed for eligibility, and it can be problematic to collect data on these people because they weren't always consenting. And then you have different definitions of screening at all of the different clinics. So this can be difficult to collect data on but in this case, we provided a definition for screening, which, for this trial, screening including only people who were actually approached about this Specific study and were evaluated for eligibility. Of the 1,300 who were evaluated, 522 were excluded because they did not meet one or more of the eligibility criteria. So in this trial, we had an enrollment phase before the randomization. And this was done because one of the eligibility criteria was that we were only including patients who were stable on corticosteroids for a month. So after they were enrolled, if they weren't already stable on corticosteroids they had to Wait for a month to become stable before they were eligible for randomization. And at the bottom we have 500 participants who were randomized and here on the next slide we see how they were allocated. 169 were allocated to fluticasone. 165 were allocated to the combination therapy. Fluticasome and salmeterol and 166 were allocated to to montelukast. And underneath we have the number who actually received their allocated intervention and you'll notice almost everyone received it but there were. Three people who did not receive their allocated intervention. Underneath we describe follow-up. During follow-up there were 13 participants, 16 participants and 20 participants in the different groups who discontinued intervention, and we have the reasons for discontinuing Underneath, but this was an intention to treat analysis, so you'll recall that even people who never took their intervention or who discontinued early were included in the analysis. And at the bottom of the slide, you'll see the number of people who were included in analysis. So there were six people who had no follow-up data and they were not included in this analysis. But everyone else, regardless of whether or not they took their assigned treatment, were included in the analysis. And on this slide, I have another example. It's very similar, but I just wanted to point out that sometimes things happen in a trial that you absolutely cannot control. But you can use the CONSORT diagram to describe them. So in this case, we had a site that was in Louisiana, and during Hurricane Katrina, the site was devastated and we lost all of the data, and we stopped collecting data at that site because the staff and the participants just had other things that they needed to worry about at the time. So we described that in the flow chart, we've described how the randomization was originally 412, but we lost ten participants due to Hurricane Katrina. There were five in each group. So the group that we actually continued with was without those ten participants. And also in the text of the results section, We need to discuss when the trial was actually conducted, the dates. Because this helps for us to establish context. We also have baseline data that describes the demographic and the clinical characteristics of the population, and that gives us an idea of the generalized ability and also. It allows us to compare groups and see whether there are any imbalances. In all results tables we need to know the number that were analyzed. And this sometimes differs slightly from table to table. But we need to know this number so we can see whether the analysis was done by the original treatment assignment and so we can see who was excluded. An estimates of the outcomes, we need to know the treatment effect estimates. So not just the estimates in each treatment group separately. But an estimate of the difference or the relative effect. And we need an estimate of uncertainty. So standard error, standard deviations or confidence variables. If there were ancillary analyses, we need to know the results. In the sub groups if there are unadjusted and adjusted analysis we need both of those. And again, we need to be clear about which ones were pre-specified and which ones were exoloratory and the results section at least for the primary paper Should have details on the harms, the adverse events. How these were assessed, and the rates in each group. The reporting harms can be difficult. I have on this slide a table from an article in annuls, that talks about the problems in reporting harms. Things that you commonly see in papers. So sometimes you see generic statements that say the drug was generally well tolerated or poorly tolerated with no supportive data. And sometimes we see combined data on harms, instead of data in each of the study arms. Sometimes we see data on adverse events grouped together. Not specified by the severity or they type of adverse event and sometimes we see adverse events reported only if they reach a certain frequency and. I have to admit I do this sometimes because when you gather data on adverse events we frequently use both standard reporting so we have a list of events that we report and then we ask them to tell us if any events that they are experiencing aren't on the list. So you end up with a lot of text that you have to parse through. And you have one event by one person. And if you listened to all of those, you wouldn't be able to fit it into the length requirements for the paper. So sometimes you do have to put some sort of frequency cutoff for single adverse events that are reported. And then there are occasions where you see adverse events In the paper, only if they reach a certain threshold, and all of them should be reported and sometimes you see adverse events reported only counts, instead of the timing of the event. So, it's possible that the adverse event is occuring in both groups, but in one group it's occuring earlier than the other group, so we need to know the relative timing of the events. And there are more items on this list, and if you're interested, you can read the paper. And learn more about the problems with reporting harms. Okay, I'm going to talk a little bit about tables and figures, and a lot of what I'm going to say here is not included in consort, but it's guidelines that we use in the center. Because we produce a lot of tables and figures and we've come up with a system that works for us. So tables and figures need to convey the essence of the results without having to read all of the results text. And the legends are necissary to explain what is happening in the figures And then tables, but they have to be succint. And each of the tables and figures needs to provide both numerator and denominator data, so you shouldn't provide just percentages, because we don't know what the denominator is. If you're going to provide a percentage, we need both the numerator and the denominator. And even when we're talking about time-to-event data, it's good to know how many people are in the risk set. And for easy reading, we usually make the treatment comparisons in the columns and we decimal align all the tables. Table one is typically the baseline by characteristics by treatment group. At various points in time, it's been the fashion to either include or not include P-values in Table one. Currently, the fashion is not to report them. Because by definition, if there is a baseline imbalance, it's by chance. Because we randomized the treatment. But the p-values do provide you with some measure of potential confounding, but regardless of whether or not the p-value is small for a specific imbalance, if it's an important predictor of the outcome it might still be a strong confounder. So we need to carefully look over the table, not just the p-values, but the actual values in the table and pick out. Any potential imbalances that could be important and might need to be adjusted for in the analysis. In Table one, we usually express variability using standard deviations. And again, for easier reading, it's good to decimal align the digits. And report things at an appropriate precision level. If you're measuring things on a whole number level, like one, two, three, then you don't need to go out three decimal places to talk about the means. And here's an example baseline characteristics table, and this is an example from the consort group. And in this this table, you'll that they have the treatment groups in the columns, and they have the number of people allocated to each group, N equals 141 and N equals 142. They describe in the rows how the variable is expressed, the mean age plus and minus the standard deviation. And you'll notice they use reasonable precision. They don't go out to many decimal places. We only have one decimal place showing here. They have smokers showing in n percent. So we have both numerator, and denominator data. And this is another example from consort of a results table. So in this result's table, they showed both the primary and the secondary outcome. And this was. A treatment for rheumatoid arthritis. And they have the number and the perecent of people meeting the primary outcome at 12 weeks. And then they have the treatment effect, which is the difference in the percents. They have the treatment effect with the confidence interval, and that's important to include a measure of the variability. And the results table, we do include p-values. And on this slide we have an example of an Instance Curve, or an Inverse Kaplan Meier Curve. And you'll notice, at the bottom, that we have the number at risk at each of the time points. And you can include this number at risk or the number of people included at each of the time points, also, if you have a continuous variable. And this is a useful number because it lets us know, especially out in the far right of the table. If there's only a few people included in that group, then whether or not we should believe any differences we see far out to the right of the table. You'll notice underneath the figure, we have the number at risk in each of the three treatment groups at each of the time points, and this is an important number to include because it lets us know, at the far right of the table in particular, whether or not we have a lot of people contributing to the estimates at that level. And finally, the consort guidelines also talk about what should be included in the discussion of the paper. So first, we need an interpretation of the results. We can give a conclusion of the study hypothesis and reiterate the key results, not. All the results. We don't want to recapitulate the entire results section. And then we need to talk about the limitations, and be honest, what went wrong in the trial. Something always goes wrong. We need to talk about what it was, what are potential sources of bias, and where do we think we might have imprecision. We should discuss generalizability,and the interpretation. So how do we place this trial in context to what's already been done? The 2010 consort guideline say that this is best achieved, by including a formal systematic review, that includes the new results along with what was known before the trial began. Authors should also discuss whether or not they believe that there is an appropriate balance of benefit and harms. The concert guidelines also state that in the paper we should have some information about where the trial is registered and where we can access the protocol from the trial. And this could be on a website that the journal provides or it could be on. The study website, if there is a public portion that's open to everyone. We need to know who funded the trial and what were the role of the funders. Did they have a say in the design, the conduct, or the analysis of the trial. Most journals have specific guidelines on how the conflicts of interest should be reported, and how the role of the funder should be reported. In section C, we're going to talk about how to evaluate what's written to see whether or not the trial was conducted appropriately.
