Validation in epidemiological studies

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Validation in epidemiological studies

04:49, 19 September 2012 (CDT)

ABSTRACT

Provide objective evidence that a program fulfill the requirements whilst running on huge datasets with mixed quality might become a challenge. E.g. the quality of data may lead to inopportune efforts for double programming; unforeseen exceptions in the data may cause this. Beforehand sample drawing may help reducing run time whilst programming and may also help to limit exceptions. To address all issues with certain relevance we have to define a proper sample size. An alternative approach is to define artificial test data. Thus the correct result is defined. Double programming (of the analysis datasets) would be obsolete. However, some of the data issues may not be addressed regardless of their relevance. But keep in mind that a proper Data Definition Table is the first step to ensure that is done what was intended in your analysis plan.

INTRODUCTION

At pressent there is a lot of experience how to handle the need of validating statistical programming in clinical trials and submissions, abstracts based on clinical trial data, signal detection based on clinical trial data etc. The well controlled quality of data, the explanation of the data obtained from an annotated case report form(s), study protocol(s) together with a defined analysis described in protocols and analysis plans lead to a clear understandig what has to be programmed and hopefully to a Data Definition Table defining all the derivations to be done. There are several possible ways to verify the correctness and validity of programming and there is also the obligation to do so in clinical trial reporting.

Analysing claims databases is differend because

  • The data is like it is delivered, no queries are possible. Data has to be used like it is delivered.
  • The data is like it is used/collected in real live, to serve the needs of real live but not for needs of analyses.
  • You may have to work with "links by meening" in stead of links established by design of the database. For example you may link an observed claim from a pharmacy with one observed claim from a practioneer
  • The amount of data you have to deal with at the begin of your program/analysis depends on the organisation/purpose the data is collected for but not on any kind of estimate of statistical power.
    • it may happen that at the end there are too few usable observations left
    • depending on the database and table you are working with the number of observations may be beyond the millions. This will have an impact on run time and need of computational power.

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REFERENCES

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