Welcome to the pfhrp2/3 Planner

How to use this tool

This tool is designed to help researchers conducting Plasmodium falciparum hrp2/3 gene deletion studies. It can be used in two ways:

1. In the design phase (before data have been collected) to help guide the appropriate number of health facilities and a sample size per health facility.
2. In the analysis phase (once data are available) to estimate prevalence of deletions and determine if they are above a set threshold.

The ideal plan would be to perform both steps, i.e., using this app before a study has started to choose target sample sizes and then returning to the app once data are available. However, it is valid to analyse data even if sample sizes were chosen using a different method (see FAQs ).

For those wanting more background information on the method, or who want to perform more advanced analyses, please take a look at the DRpower R package that underpins this app.

Our framework



Now go ahead and start exploring!


Acknowledgments

This tool was developed by Shazia Ruybal-Pesántez and Bob Verity, Malaria Modelling Group, Imperial College London, in collaboration with the Global Malaria Programme, World Health Organisation (WHO).


How to reference

Ruybal-Pesántez S and Verity R (2023). DRpower and pfhrp2/3 planner app: Study design and analysis for pfhrp2/3 deletion prevalence studies. R package version 1.0.2 and R Shiny app version 1.0.1.


Data privacy disclaimer

This web application does not store any data within the application itself. Data is temporarily stored on our the Shiny server during your active session and/or when you save your results for export into the downloadable report. Please note that any refresh or reload of the page will result in the loss of data, as it is not stored beyond the duration of your session.



Most recent update 12 June 2024.


How many samples? How many health facilities?

The table below gives the number of confirmed malaria positive samples required per health facility in order for study power to be 80% or higher. You can use these numbers as a general guide when scoping out a study plan, before moving to more tailored sample sizes in the Design step.

Sample sizes required to achieve a target power of 80%


Refine your health facility sizes

Sample size tables assume you will collect the same number of samples in every health facility, but this may not be possible in practice. Here, you can enter your final target sample size in each health facility and then estimate power directly. Generally, surveys will focus on health facilities but the 'cluster' could be different in specific situations.

When choosing sample sizes, remember this is the number of confirmed malaria positive individuals. Check with local teams to see how many cases can realistically be recruited within the study period based on local incidence trends. You can also use this table to account for drop-out, which can occur for many reasons from participants withdrawing consent to failure of lab samples. Local staff and technicians may be able to advise on sensible values for assumed drop-out.

1. Enter sample sizes specific to your study

2. Estimate power

If you update any of the values in Step 1, make sure you remember to recalculate power

Check and save your parameters and results

Click the button below to display a summary of the information you entered in the previous tab. If everything looks as expected, click on the download button to download your report PDF.



Estimate prevalence and compare against the 5% threshold

Here, you can enter your observed counts of pfhrp2/3 deletions in each health facility and use the DRpower model to estimate the prevalence of deletions along with a 95% credible interval (CrI). You can also compare prevalence against the WHO recommended 5% threshold to work out the probability of being above this threshold. Generally, surveys will focus on health facilities but the 'cluster' could be different in specific situations.

If your intention is to make a binary decision as to whether prevalence is above or below the threshold (i.e., a hypothesis test) then it is worth being clear about your analysis plan before you see the result. For example, we recommend accepting that prevalence is above the 5% threshold if the probability of this outcome is 0.95 or higher (the power calculations in the Design tab assume this value). You should not change your criteria for accepting/rejecting a hypothesis once you have seen the result.

Step 1. Analyse prevalence of deletions compared with the 5% threshold

Select the final number of health facilities in your study and enter the raw number of observed pfhrp2/3 deletion counts, and the number of confirmed malaria cases per cluster.


Step 2. Analyse intra-cluster correlation (ICC)

Although the prevalence of pfhrp2/3 deletions is usually the main focus of our analysis, the ICC is an extremely valuable supplementary analysis. Reporting this value not only contextualises the prevalence estimates, but it also provides valuable information to assist with the design of future studies.

The raw data for this analysis are taken from Step 1, and there are no additional parameters needed.

If you update any of the values in Step 1, make sure you remember to recalculate ICC

Check and save your parameters and results

Click the button below to display a summary of the information you entered in the previous tab. If everything looks as expected, click on the download button to download your report PDF.


DRpower pfhrp2/3 planner app version 1.0.1. Developed by Shazia Ruybal-Pesántez and Bob Verity.