Rigorous statistical methodology represents a vital framework for upholding research integrity and maximizing benefits in medical science. However, the misuse of statistical tools contradicts ethical tenets and compromises validity. Problematic trends like p-hacking, hyping delicate results, and overemphasizing statistical significance relative to clinical meaning introduce prejudice and impede reproducibility. Case studies, including hormone replacement therapy trials, exhibit how unsound statistics propagate doubtful conclusions and potential injury. Resolving the "reproducibility crisis" necessitates proper statistical techniques such as sufficient power, preregistration, transparent data, and Bayesian approaches. Statistics and ethics are profoundly intertwined in accountable medical inquiry. By prioritizing statistical meticulousness, investigators can satisfy their ethical duty to generate reproducible discoveries that aid patients and society. Proper statistical application is indispensable for advancing medically and socially impactful research.

BACKGROUND: In biostatistics, evaluating fragility is crucial for understanding their vulnerability to miscategorization. One proposed measure of statistical fragility is the unit fragility index (UFI), which measures the susceptibility of the p-value to flip significance with minor changes in outcomes. Although the UFI provides valuable information, it relies on p-values, which are arbitrary measures of statistical significance. Alternative measures, such as the fragility quotient (FQ) and the percent fragility index, have been proposed to decrease the UFI's reliance on sample size. However, these approaches still rely on p-values and thus depend on an arbitrary cutoff of p < 0.05. Instead of quantifying fragility by relying on p-values, this study evaluated the effect of small changes on relative risk. METHODS: Random 2x2 contingency tables associated with an initial p-value of 0.001 to 0.05 were evaluated. Each table's UFI and relative risk index (RRI) were calculated. A derivative of the RRI, the percent RRI, was also calculated along with the FQ. The UFI, FQ, RRI, pRRI, initial p-value, and sample size were compared. RESULTS: A total of 15000 cases were tested. The correlation between the UFI and the p-value was the strongest (r = -0.807), and the correlation between the pRRI was the weakest (r = -0.395). The RRI had the strongest correlation with the sample size (r = 0.826), and the UFI had the weakest correlation (r = 0.3904). The coefficient of variation for the average RRI was the smallest at 28.3%, and for the FQ, it was the greatest at 57.0%. The correlation between the UFI, FQ, and p-value is significantly greater than the correlation between the RRI, pRRI, and p-value (for all comparisons, p < 0.001). CONCLUSION: The RRI and pRRI are significantly less correlated with the p-value than the UFI and FQ, indicating relative independence of the RRI and pRRI from p-values.

Hospitalized patients, upon admission, often have a degree of anorexia which gradually resolves as their medical condition improves. Thus, a quick way to assess the overall improvement of hospitalized patients is to look at their plate after breakfast when rounding. Patients with a clean plate after eating their full meal often are close to or ready to be discharged home.

Blockchain technology has great potential to revolutionize healthcare data management. The technology is sufficiently complex, however, making it essential that a large number of people with a broad range of skills will be required to implement the technology. Innovation clusters will be the primary means of producing blockchain breakthroughs in healthcare by bringing together computer scientists, medical experts, and business people in the pursuit of a common goal. Healthcare innovation clusters are most likely to be centered around medical universities, but will also include in close geographic proximity technology, business, and medical insurance organizations.

The classical definition of statistical significance is p <= 0.05, meaning a 1/20 chance the test statistic found is due to normal variation of the null hypothesis. This definition of statistical significance does not represent the likelihood that the alternative hypothesis is true. Hypothesis testing can be evaluated using a 2x2 table (shown below). Box "a" = true positives: p <= 0.05 and the alternative hypothesis is true. This is the study's power. A rule of thumb is that study power should be at least 80% (80% of the time the statistical test is positive when the alternative hypothesis is true). Therefore a = 0.80. Box "b" = false-positives: p <= 0.05 but the alternative hypothesis is false. By definition, when p = 0.05 the test statistic has a 5% probability of occurring by chance when the null hypothesis is true. Therefore, b = 0.05. Box "c" = false-negatives: p >= 0.05 but the alternative hypothesis is true. This occurs 20% of the time when the study's power is 80%. Therefore, c = 0.20. Box "d" = true-negatives: p >= 0.05 and the null hypothesis is true. This occurs 95% of the time when p <= 0.05. Therefore, d = 0.95. From this table we derive: Sensitivity = power = a/(a+c) = 80%. Specificity = (1-p) = d/(b+d) = 95%. Positive predictive value = power/(power + p-value) = a/(a+b) = 94%. Negative predictive value = d/(c+d) = 83%. The classical definition of statistical significance is (1-specificity) and does not take power into consideration. The proposed new definition of statistical significance is when the positive predictive value of a test statistic is 95% or greater. To arrive at this, the cut-off p-value representing statistical significance needs to be corrected for study power so that 0.05 > (p-value)/(p-value + power). To achieve a 95% predictive confidence,it can be derived that statistical significance is a p-value <= power / 19.

Objectives: Statistical significance does not equal clinical significance. This study looked at how frequently statistically significant results in the nuclear medicine literature are clinically relevant. Methods: A medline search was performed with results limited to clinical trials or randomized controlled trials, published in one of the major nuclear medicine journals. Articles analyzed were limited to those reporting continuous variables where a mean (X) and standard deviation (SD) were reported and determined to be statistically significant (p < 0.05). A total of 32 test results were evaluated. Clinical relevance was determined in a two-step fashion. First, the crossover point between group 1 (normal) and group 2 (abnormal) was determined. This is the the point at which a variable is just as likely to fall in the normal distrubution as the abnormal distribution. Jacobson's test for clinically significant change was used: crossover point = (SD1 * X2 + SD2 * X1) / (SD1 + SD2). It was then determined how many SD's from the mean this crossover point fell. For example, 13.9 +/- 4.5 compared to 9.2 +/- 2.1 was reported as statistically significant (p < 0.05). The crossover point is 10.7, which equals 0.71 std from the mean: 13.9 - (0.71*4.5) = 9.2 + (0.71*2.1).   Results: The average crossover point was 0.66 SD's from the mean. The crossover point was within 1 SD from the mean in 26/32 cases, and in these cases averaged 0.45 SD. Thus, for 4 out of 5 statistically significant results, when applied to an individual patient, the cut-off between normal and abnormal was 0.45 SD from the mean. This results in a third of normal patients falling into an abnormal category. Conclusions: Statistically significant results frequently are not clinically significant. Statistical significance alone does not ensure clinical relevance.    

Thomas F Heston, MD, FAAFP1 1Department of Medical Education and Clinical Sciences, Elson S Floyd College of Medicine, Washington State University, Spokane, Washington USA Citation: Heston TF. Sauna bathing and the cardiovascular system. Int J Sci Res (Ahmedabad). 2017 Nov; 6(11) 569-570.

Healthcare complexity and costs can be decreased through the application of blockchain technology to medical records and insurance companies. Estonia has taken a leadership role in blockchain based services both in the commercial sector and in government. The Estonian government’s innovation strategy was to create GovTech partnerships to implement blockchain based technologies throughout the country, and become a global leader in the technology. Starting in 2011, just 3 years after Satoshi Nakamoto published the first description of distributed ledgers and blockchain technology, the Estonian Government started partnering with the private technology startup company Guardtime to use blockchains to secure public and internal records. Then in 2016, Estonia once again reinforced its global leadership in blockchain technology when it announced it would use blockchain technology to secure the health records of over a million citizens. Estonia’s systematic method of applying blockchain technologies through GovTech partnerships demnostrates how innovation is a process. Estonia also identified early the value of the blockchain as a disruptive platform innovation. The application of blockchain technology to healthcare is a radical innovation given that nearly all previous applications have been in the financial and legal sectors.

Healthcare complexity and costs can be decreased through the application of blockchain technology to medical records and insurance companies. Estonia has taken a leadership role in blockchain based services both in the commercial sector and in government. The Estonian government’s innovation strategy was to create GovTech partnerships to implement blockchain based technologies throughout the country, and become a global leader in the technology. Starting in 2011, just 3 years after Satoshi Nakamoto published the first description of distributed ledgers and blockchain technology, the Estonian Government started partnering with the private technology startup company Guardtime to use blockchains to secure public and internal records. Then in 2016, Estonia once again reinforced its global leadership in blockchain technology when it announced it would use blockchain technology to secure the health records of over a million citizens. Estonia’s systematic method of applying blockchain technologies through GovTech partnerships demnostrates how innovation is a process. Estonia also identified early the value of the blockchain as a disruptive platform innovation. The application of blockchain technology to healthcare is a radical innovation given that nearly all previous applications have been in the financial and legal sectors.

Blockchain technology is a system of creating an immutable, secure, distributed database of transactions. Blockchains were initially created to provide a distributed ledger of financial transactions that did not rely upon a central bank, credit company, or other financial institution. The technological breakthrough, however, has been extended to transactions involving legal matters, medical records, insurance billing, and smart contracts. One primary way that blockchain technology is important to healthcare professionals in that it can revolutionize medical database interoperability. This greater interoperability can help improve access to medical records, imaging archives, prescription databases. Given that a patient’s medical history is a primary cornerstone of good medicine, blockchain technology has the potential to dramatically improve medical care.

Blockchain technology enables the creation of immutable, publicly available data. Initial applications have been primarily in the fields of finance (Bitcoin) and law (smart contracts), yet it can also help advance science by reducing human bias.  The blockchain can ensure that hypotheses are not altered; that methods of data collection are transparent; that results are publicly available for independent analyses; and that conclusions are less biased. Our current system of medical research suffers from too much bias. Blockchain technologies, by creating immutable data, will lead to an increased confidence in evidence based medicine.