The Pitfalls of using Statistical Methodology in Dental Research and how you can avoid them ES/EN

Agosto / August 07, 2017

The Pitfalls of using Statistical Methodology in Dental Research and how you can avoid them ES/EN

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Evidence-based dentistry has long been defined as the only true way to answer a specific clinical question or solve a problem. Yet surprisingly it isn't without its flaws. Under normal circumstances, establishing 'best evidence' involves the collection and analysis of scientific data which is often represented in statistical form. Conclusions are then drawn from those statistics which make up the basis of reasoning.

Sounds pretty straightforward right? Well yes, until you consider the following… It's thought that 40-70% of all clinical papers written within the medical field (dentistry included) contain one or more statistical errors.

To back this up, in 2011 Kim et al carried out a series of tests on 418 clinical papers in the field of dental research written between 1995 and 2009. In 111 cases the use of statistics was deemed inappropriate due to insufficient information. What's more, of the remaining 307 cases, it was found that statistical data was misused in 51.5% of them.

To put it another way.... out of 418 clinical papers, 262 of them were deemed serious enough to have led to misleading conclusions!

"Statistical methodology in oral and dental research: pitfalls and recommendations. Hannigan A, Lynch CD."

So why do we get it so wrong?

The answer may lie in the initial testing itself.... In many cases, observational studies simply 'happen' rather than be designed. For this reason, any data used is often intended for another purpose. This can result in skewed findings.

Other issues occur when observational studies have gone on for many years. Consequently, subsequent assessors may use differing degrees of measurement. This again can result in flawed statistics. Finally, inappropriate use of controls from high-risk groups is often used to generalise results. For all of these reasons above, a justification of the sample size is highly important. While you might feel that sample justification is primarily an exercise in 'covering your back' check out these findings by Lucena et al...

In 2011, they researched 226 clinical papers on the study of microleakage in Operative Dentistry and of those, just 1% justified their sample size. Naturally, as design flaws cannot be fixed in the analysis stage of the research, in some cases this led to misconceptions.

That said, failure to justify sample sizes isn't the only pitfall when carrying out clinical testing. There are other reasons for statistical errors including:

      • Insufficient or ignored areas of information
      • The under use of confidence intervals
      • The effects of clustering

What's more, they occur in some of the most common statistical techniques such as:

      • Descriptive statistics
      • Parametric and nonparametric hypothesis tests
      • Survival analysis
      • Correlation and regression analysis

MimetikOss Sustituto oseo biomimetico


4 analysis pitfalls and how you can overcome them


Issues Counteractions

Failure to take into account so called 'irrelevant' information.

Graphs that lack a true zero.

Failure to make appropriate comparisons between the mean and median.


Consider that extreme outliers may extort the true value of the mean. Therefore comparisons should always be made between the mean and median before analysing results.

Only use the Standard Deviation (SD) as a measure of variability with the mean evaluation.

Choose the appropriate levels of numerical precision with which to represent results (e.g. when using – p-values).


Issues Counteractions

The heavy reliance on probability or p-value – Vähänikkilä et al reported that 81% of 928 articles reviewed in four high impact dental journals reported P-values.

Often the probability factor is rounded up or rounded down rather than giving a precise numeric evaluation.

Look to include confidence intervals. These are still heavily underused in dental research but can give a better indication that the true hypothesis lies between an indicated range. (See the study carried out by Lehmann et al). To back this up, Kim et al reported that only 20 of the 307 dental papers reviewed in journals contained confidence intervals. In other words, they add more 'weight' to your findings.



Issues Counteractions

Survival analysis testing is often carried out retrospectively. When this happens it's impossible to take into account censoring problems such as patients dropping out of the study - all of which can affect the end results.

Same subject observations may not behave independently. As a result, findings can become clustered. A recent review of 2218 leading dental articles identified clustering effects in 559 of them (25%). In addition, in a survey carried out by Fernandes-Taylor et al, they spoke to statistical reviewers who stated that clustering was the area of analysis that 'required the most attention'.


Survival analysis should always be carried out using cohorts of people from a particular starting point moving forwards in time. This way any censoring can be noted and included in the final results.

If the researcher wants to use the subject of surface/tooth/implant as the unit of analysis rather than the person, then clustering has to be recognised and taken into account.


Issues Counteractions

Misuse of the Pearson's Correlation Coefficient. While it's used to great effect to measure the strength of an association between two continuous variables, many clinical assessors have wrongly used it to correlate ordinal data (e.g. questions answered on a scale of 1-5).


For ordinal data testing the preferred method should be Spearman's Correlation Coefficient. In addition, don't be swayed by variable selection methods. Instead, biological and clinical know how should always win the day.


While evidence-based testing continues to be the number one factor in solving important dental issues, it can and does have its pitfalls. The key is understanding that they exist and more importantly, knowing what to do to avoid them.


Kim JS, Kim D-K, Hong SJ. Assessment of errors and misused statistics in dental research. International Dental Journal


Lucena C, Lopez JM, Abalos C, Robles V, Pulgar R. Statistical errors in microleakage studies in operative dentistry. A survey of the literature 2001–2009. European Journal of Oral Sciences 2011;119:504–10.

Vahanikkila ̈ H, Nieminen P, Miettunen J, Larmas M. Use of statistical methods in dental research: comparison of four dental journals during a 10-year period. Acta Odontologica Scandinavica 2011;67:206–11.

Tufte ER. The visual display of quantitative information. Cheshire, CT: Graphics Press; 1983.

Lehmann KM, Igiel C, Schmidtmann I, Scheller H. Four color-measuring devices compared with a

spectrophotometric reference system. Journal of Dentistry 2010;38S:e65–70.

Fernandes-Taylor S, Hyun JK, Reeder RN, Harris AHS. Common statistical and research design problems in manuscripts submitted to high impact medical journals. BMC Research Notes 2011;4:304.

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Long-term follow-up study of osseointegrated implants in the treatment of totally edentulous jaws.Adell R, Eriksson B, Lekholm U, Brånemark Pl, Jemt T. Int J Oral Maxillofac Implants 1990 Winter;5(4):347-59. PMID: 2094653

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A 15-year study of osseointegrated implants in the treatment of the edentulous jaw. Adell R, Lekholm U, Rockler B, Brånemark PI. Int J Oral Surg. 1981 Dec;10(6):387-416. PMID: 6809663

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Osseointegration and its experimental background., Brånemark PI., J Prosthet Dent. 1983 Sep;50(3):399-410. PMID: 6352924

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