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Second, most research questions are addressed by many teams, and it is misleading to emphasize the statistically significant findings of any single team. What matters is the totality of the evidence. Diminishing bias through enhanced research standards and curtailing of prejudices may also help. However, this may require a change in scientific mentality that might be difficult to achieve. In some research designs, efforts may also be more successful with upfront registration of studies, e.g., randomized trials [35]. Registration would pose a challenge for hypothesis-generating research. Some kind of registration or networking of data collections or investigators within fields may be more feasible than registration of each and every hypothesis-generating experiment. Regardless, even if we do not see a great deal of progress with registration of studies in other fields, the principles of developing and adhering to a protocol could be more widely borrowed from randomized controlled trials.
Nevertheless, most new discoveries will continue to stem from hypothesis-generating research with low or very low pre-study odds. We should then acknowledge that statistical significance testing in the report of a single study gives only a partial picture, without knowing how much testing has been done outside the report and in the relevant field at large. Despite a large statistical literature for multiple testing corrections [37], usually it is impossible to decipher how much data dredging by the reporting authors or other research teams has preceded a reported research finding. Even if determining this were feasible, this would not inform us about the pre-study odds. Thus, it is unavoidable that one should make approximate assumptions on how many relationships are expected to be true among those probed across the relevant research fields and research designs. The wider field may yield some guidance for estimating this probability for the isolated research project. Experiences from biases detected in other neighboring fields would also be useful to draw upon. Even though these assumptions would be considerably subjective, they would still be very useful in interpreting research claims and putting them in context.
NSRR user Ciprian Crainiceanu and colleagues wrote an E-book titled Methods in Biostatistics with R that uses a subset of SHHS data for many coding examples. The book-specific dataset is available here for download.
In Chapter 2 the authors emphasize that a clinical trial must have a primary question. It should be carefully selected, as any subsidiary question must be, too. Otherwise, the investigators may lose the track. Chapter 3 deals with the study population, underlying the importance of unambiguous eligibility criteria, the latter having a direct influence on the recruitment of participants and, thus, on the final number involved in the project (which may end up with not enough data). Chapter 4 outlines the basic study design; it stresses the demand of a control group and the need of randomization for assigning participants to control and intervention groups. 2ff7e9595c
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