Estimating disease prevalence in two-phase studies

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Before a clinical trial begins, researchers review prior information about the drug to develop research questions and objectives. Then, they decide:. Whether there will be a control group and other ways to limit research bias. Clinical trials follow a typical series from early, small-scale, Phase 1 studies to late-stage, large scale, Phase 3 studies.

Purpose: Safety and dosage. During Phase 1 studies, researchers test a new drug in normal volunteers healthy people. However, if a new drug is intended for use in cancer patients, researchers conduct Phase 1 studies in patients with that type of cancer. Phase 1 studies are closely monitored and gather information about how a drug interacts with the human body.

Researchers adjust dosing schemes based on animal data to find out how much of a drug the body can tolerate and what its acute side effects are. As a Phase 1 trial continues, researchers answer research questions related to how it works in the body, the side effects associated with increased dosage, and early information about how effective it is to determine how best to administer the drug to limit risks and maximize possible benefits. This is important to the design of Phase 2 studies. Purpose: Efficacy and side effects. In Phase 2 studies, researchers administer the drug to a group of patients with the disease or condition for which the drug is being developed.

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Typically involving a few hundred patients, these studies aren't large enough to show whether the drug will be beneficial. Instead, Phase 2 studies provide researchers with additional safety data. Researchers use these data to refine research questions, develop research methods, and design new Phase 3 research protocols. Study Participants: to 3, volunteers who have the disease or condition. Purpose: Efficacy and monitoring of adverse reactions. Researchers design Phase 3 studies to demonstrate whether or not a product offers a treatment benefit to a specific population.

Sometimes known as pivotal studies, these studies involve to 3, participants. Phase 3 studies provide most of the safety data. In previous studies, it is possible that less common side effects might have gone undetected. Because these studies are larger and longer in duration, the results are more likely to show long-term or rare side effects. Purpose: Safety and efficacy.

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Topics in the Estimation of Historical Control in Early Phase Oncology Trials - Lynn Navale

Drug developers are free to ask for help from FDA at any point in the drug development process, including:. Pre-IND application, to review FDA guidance documents and get answers to questions that may help enhance their research. As long as clinical trials are thoughtfully designed, reflect what developers know about a product, safeguard participants, and otherwise meet Federal standards, FDA allows wide latitude in clinical trial design. It is interesting to note from Fig. Fitted mean sizes across the other prevalence classes were similar; There is an apparent effect of prevalence in phase 3 trials Fig.

The fitted mean sample sizes were Although the wide variation in sample sizes and the relatively small numbers of trials for some prevalence classes leads to wide confidence intervals, similar conclusions can be drawn to those given above based on Fig. The R-squared statistic, an indication of the proportion of variability of fitted log sample size by the prevalence, phase, interaction between prevalence and phase and the other covariates was 0.

This is small despite the large number of regressors in the model, suggesting that there appears to be a lot of unexplained variability. We performed sensitivity analyses with parallel 2-arm trials only and single group assignment 1-arm trials only to investigate the effect of prevalence and phase of study adjusted by covariates on sample size. For the analysis of parallel 2-arm trials only, we also included the types of arm experimental, active comparator, placebo comparator, sham comparator, no intervention or others as one of the covariates that may be associated with sample size.

The possible combinations of 2-arm trials are: experimental vs. The fitted sample size for trials where the experimental arm vs. Fitted mean sample sizes for trials across the other types of 2-arm were very similar; Overall, we observed similar trend where sample size is affected by prevalence where as the prevalence increases, mean sample size increases with a more noticeable difference in phase 3 trials see Fig. We found that a majority of trials were conducted in one country only regardless of the disease prevalence.

This is slightly surprising given the opportunity in multi-nation trials to recruit more patients. Further investigation may be necessary to understand why multi-nation trials were not conducted more frequently. We also found that the actual sample size for completed trials was generally smaller than the anticipated trial size for ongoing trials. This could be indicative of an ambition to complete large trials in rare disease populations that are difficult to achieve in practice. Sample sizes for trials in rare diseases were statistically significantly related to gender, age, whether or not the trial had a DMC, whether or not the intervention was FDA regulated, intervention model, trial regions with at least one participating centre, number of countries participating in the trial, year that enrolment to the protocol began and number of treatment arms.

Trials enrolling males only were on average smaller than those that enrolled either females only or both sexes.

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Trials enrolling females only had slightly larger size than those that enrolled both sexes but this was not statistically significantly different. We expected that trials enrolling males only and females only to have smaller size because when the eligibility criteria is restrictive, the population is more homogeneous and less variable in effectiveness, thus smaller sample size may be sufficient. Of note is that most of these trials were in diseases that affect one sex only; all of the male-only trials were X-linked disorders whereas almost all of the female-only trials affected females only.

A few of these trials were in disorders for pregnant women only. Further research is necessary to investigate and identify other factors that could explain this difference.

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Similarly, we expected trials enrolling various age groups to have larger sample sizes than those that recruited children only, adults only or elderly only because by expanding the sampling pool more patients could be recruited. However, on average trials recruiting multiple age groups were slightly smaller than adults-only and elderly-only trials. Unsurprisingly, trials with factorial design had larger sample size than single group and crossover trials since in a factorial design a few combinations of interventions are tested at the same time.

The levels of evidence from these trials may not be as high quality as the gold standard RCT. This in turn presents a challenge of developing new methodology for trials in small populations. In response to this challenge, three collaborative research projects Asterix, IDeAl and InSPiRe are working on methods for clinical trials in the small population setting [ 13 ]. The main analysis and sensitive analyses with parallel 2-arm trials only and single group 1-arm trials only showed that generally, the mean sample size was affected by prevalence where mean sample size increases as prevalence increases.

Estimating disease prevalence in two-phase studies.

The increase was noticeably larger in phase 3 trials compare to phase 2. However, due to small number of trials in some classes, it is difficult to make comparisons. The generalisability of the results obtained in this study rely on the extent to which trials included in the database are representative.

For example, there was a multi-centre interventional trial on tuberculosis with locations in the US, United Kingdom and Peru. Another possible limitation with our study is that we considered a condition to be rare if information on prevalence was listed in Orphadata.

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This database is updated on a regular basis and some conditions may have been missed out or with no prevalence information. Prevalence of some diseases changes over time and because the prevalence information in Orphadata is updated regularly, old prevalence data are not retained.

The most efficient two-phase design

This presents a weakness to the study as trials studying rare diseases prior to were assumed to have updated prevalence. As explained in the methods section, we have used point prevalence to classify diseases into prevalence classes where this is available. In some cases, some other measure of prevalence has been used. In this project diseases are classified into groups according to their prevalence value and because of categorising continuous variable we have lost some information.

Our results depend to some extent on the choice of types of prevalence used but as the results presented are based on means from a number of studies, it is likely that conclusions are relatively robust. However, there may have been inconsistency in data entry by investigators with the definition given by US FDA. This is likely to introduce systematic bias. Theses inconsistencies are difficult to rectify as the registry does not require investigators to give details on the design and sample size calculation where detailed examinations could be performed to check if the objective of the design correspond to the US FDA definition.

The number of patients eligible for trials may also depend on whether the rare condition is acute or life threatening, so that only new cases can be recruited, or chronic, when it may be possible to sample from a larger population depending on the prevalence rather than the incidence rate.