You're torn between accuracy and efficiency in statistical workflows. How do you strike the right balance?

It is important to identify pieces of the project which may not require 100% accuracy. We should spend more time and effort on parts where accuracy is critical. For example, in stress testing models focus should be on getting 90% accuracy following appropriate process and governance, similarly in front office models we need more accuracy so need to spend more time in refining the model.

1) Prioritize tasks in sequence 2) Start with simple task to complete and on parallel learn the methodology, concepts of complex tasks. you can start complex task once you understand fundamentally fully. so that it will be more efficient to finish simple task as well as focusing on complex tasks as it will take some time to understand assumptions of particular methods etc. 3) Filter which software would be more appropriate for complex task, e.g. R is efficient for graphical presentation and open source. For some analysis, if you know PROCs of SAS already, then focus on using the same.

Identify Critical Steps: Focus on tasks like data validation and complex analyses that are essential for ensuring accuracy. Streamline Non-Critical Tasks: Look for opportunities to automate or simplify less critical steps to save time. Allocate Resources Wisely: Direct more resources (time, computational power) to tasks where accuracy is crucial. Balance Efficiency: Maintain efficiency by not over-investing in tasks that have minimal impact on the overall accuracy. Regular Review: Continuously assess and adjust priorities as needed, based on new insights or changing requirements.

A prioridade de tarefa na verdade é uma metodologia baste importante para optimiza??o de processos e permitir que a alta direc??o time decis?es atempadamente e coerente sobre temáticas cruciais do negócio ou de uma organiza??o prevenindo riscos e amea?as com alta severidade.

Simpler models are more often than not easier to interpret and provide better or atleast 95% of the accuracy in short period of time. The time saved can be used to solve pieces which require more complex analysis/models. Simpler models are also easy to explain to senior management /stakeholders and can help to formulate long term strategy. Simpler models avoid over fitting of the data as well.

Don't use complex methods when simple methods are suffice to interpret the results as per objectives. For example, check literature analysis of particular endpoints and then apply similar to that in your work. Select which statistical tool would be more efficient and easy to implement. Sometimes, I seen that MMRM modeling take more time due to so many visits included in the model using SAS. Try to find alternative approach which can be more faster in SAS. Search literature and apply appropriate tool for analyses.

Simplifying approach while maintaining result integrity is a smart way to balance accuracy and efficiency. Opting for simpler models when appropriate not only speeds up the process but also enhances the clarity and communication of your findings. This approach ensures that you're making informed, efficient decisions without compromising the quality of your analysis.

A forma de simplificar as metodologias ajuda na resolu??o de forma eficiente e eficaz de processos e facilita a compreens?o de todos envolvidos para resolu??o de problemas e alcance dos objectivos preconizados e de forma clara gerando altos rendimentos.

Prioritizing investments in robust data quality methods is necessary to maintain accuracy without sacrificing time. To ensure the integrity of your study and manage large datasets, you need to use automated data cleansing and effective software solutions. This approach lessens the need for rework and debugging, allowing you to focus on gleaning insightful information from high-quality data.

Most of the model development goes in getting the right development data. Data gathering/ data cleaning are the most critical steps. Thus, it is important to insure we invest enough time/ resources in data quality upfront. It could help significantly in reducing development time later on. Good quality data is needed to identify the right trends and factors for the models.

Data Quality is very critical when it's in clinical trials. Use data validation methods with software e.g. SAS to identify any data issues in different domain e.g. Medical history, Adverse events, Primary/Secondary efficacy endpoints in clinical trials. Flag to respective team when you found unusual data e.g. End date is after Start date, Duplicates of data. This will help us to maintain quality of data and eventually for final objective.

Concordo, pois a quantidade de dados primeiro permite a tomada decis?es de forma objectiva para resolu??o de problemas; Outro sim com dados é possível objectivar e avaliar qualidade de bens produtos e servi?os; Prevenir riscos, reduzir custos, gerir pessoas e processos.

When you build statistical model, you need to think all aspects of model goodness, fitting. In clinical trials, SAP should mentioned in details steps on model fitting. for example, unstructured variance covariance is basic structure that is being used in repeated measure analysis modeling. Before starting analyses, we should have all information on which variance-cov. structure to be used if UN will be failed. As analysis is ongoing process for different objectives e.g. IB, different conferences, regulatory submissions, and at last final analyses, same model would not be worked as data will change.

Use appropriate statistical tools as per regulatory requirements, tool's efficiency, knowledge of tools you have. Technology is helping to do complex analysis in less time but make sure to interpret the results appropriately. Go through the theory part before using any statistical tools. Simulation is very powerful example of technology to be used in many analysis as well. Select tool based on your requirement and make sure that the tool's output can be interpretable by you after understanding concepts.

A revis?o é fundamental porque facilita verificar e enxergar a possibilidade de erros que podem ser corrigidos, e melhorias que podem ser optimizadas para aumento da produtividade como rever processos, rever treinamento dos colaboradores, revisar procedimentos e instrumentos de trabalhos para uma actividade.