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Heterogeneity in metaanalyses
When performing a metaanalysis, the investigator often experiences heterogeneity problems; the effects planned to be pooled in a metaanalysis are more heterogeneous than could be expected just from sampling variation. One solution to the problem is to try to estimate an average effect instead of a common one. Technically, this means fitting a randomeffects model…

Adjustment problems
Confounding refers to a situation where the effect of a studied risk factor on a specific outcome is mixed with the effect of a factor not accounted for in the analysis. This other factor, known as a confounder, can distort (bias) the risk factor’s estimated effect and mislead the investigator. The problem can arise if…

Avoidable mistakes – significance
Most reports are probably written with the intention of being clear, coherent, and stringent. However, methodological misunderstandings cause many publications to be vague, ambiguous, and confusing. Misinterpretation of statistical significance plays a major role in this problem. In modern medicine, research is usually based on a dataset. Statistical methods produce pvalues, which are used to…

Spin
Spin is usually defined as reporting practices that distort the interpretation of results and mislead readers to view them in a more favourable light. A research report usually starts with a clear research question and ends with a conclusion empirically supported by the studied data. Speculations and hypotheses can be presented, but unsubstantiated claims must…

Significance and dogma
Statistical inference is essential for evaluating empirical support for medical research findings. Still, much medical research is methodologically poor. The problem is not that medical research fails to employ statistical methods; on the contrary, research reports usually include large numbers of pvalues. The poor quality is more related to the misconception that statistical methodology is…

Study protocols
Many medical publications present the results of more or less random statistical tests of data available in datasets collected for other reasons. The statistical test results are then interpreted according to the investigators’ prejudices, suitable hypotheses are formulated, and reports are written pretending that the studies from beginning to end were performed to test the…

Predictive inference
A special form of statistical inference is known as predictive inference. Instead of learning about unknown parameters, sampled data are used to predict new observations. For example, data on the treatment outcomes of existing patients can be used to develop a statistical model that facilitates clinical decisionmaking for new patients. Predictive inference, often discussed with…

Uncertainty and study design
The uncertainty described by a confidence interval is related to sampling. The sampling uncertainty of a study is known as the study’s statistical precision. The magnitude of the uncertainty depends partly on the sample size. Other forms of uncertainty (nonsampling uncertainty) are related to validity rather than precision, and these also need to be considered…