Research integrity

Research integrity

In 2013, Universities UK, in collaboration with major funders of research, developed The Concordat to Support Research Integrity , which sets out key commitments to ensure a high standard in research. It highlights the principles and profes sional responsibilities of researchers and research institutions that are fundamental to the integrity of research wherever it is undertaken. These centre on: /uni25CF honesty in all aspects of research; /uni25CF accountability and transparency in the conduct of re search; /uni25CF professional courtesy and fairness in working with others; /uni25CF good stewardship of research. A study should not under any circumstances commence until appropriate approvals have been granted and compli ance with the principles of research integrity is ensured. A helpful international summary of country-specific approval requirements is available from NIH (https://clinregs nih.gov). Henry Berthold Mann , 1905–2000, and his student Donald Ransom Whitney ‘On a test of whether one of two random variables is stochastically larger than the other’ in 1947. Frank Wilcoxon , 1892–1965, born County Cork, Ireland, American chemist and statistician. Both audit and research commonly require statistical analysis. Many surgeons find the statistical analysis of a project the most di ffi cult part. It is also the most commonly criticised part of papers written by clinicians. There are many useful books about statistics (see Further reading ); if in any doubt, a statis - tician will be pleased to give assistance. Statisticians should be consulted before research or audit has been conducted rather than being presented with the data at the end; they often give helpful advice over stud y design and can be an important part of the project team. The following terms are frequently used when summaris - ing statistical data: /uni25CF Mean : the result of dividing the total by the number of observations (the average). - /uni25CF Median : the middle value with equal numbers of obser - vations above and below – used for numerical or ranked data. /uni25CF Mode : the value with the highest frequency observed – used for nominal data collection. /uni25CF Range : the largest to the smallest value. The most important decision for analysis is whether the distribution of the data is normal (i.e. parametric or non-para - metric). Normally , distributed data have a symmetrical bell- shaped curve, and the mean, median and mode all lie at the same value . The type of data collected determines which sta - tistical test should be used. 1 Numerical and normally distributed (e.g. blood pressure) – use an unpaired t -test to compare two groups or a paired t -test to assess whether a variable has changed between two time points. 2 Numerical but not normally distributed (e.g. tumour size) – use a Mann–Whitney U -test to compare two groups or a Wilcoxon signed rank test to assess whether a variable has increased/stayed the same/decreased between two time points. 3 Categorical (e.g. admitted or not admitted to an intensive - care unit) – a chi-squared test can be used to compare two groups. ( Note : the use of these and any other statistical tests may benefit from professional advice.) Confidence intervals are the best guide to the possible - range in which the true di ff erences are likely to lie. A confi - dence interval that includes zero usually implies a lack of sta - tistical significance. Scientists usually employ probability ( P -values) to describe statistical chance. A P -value <0.05 is commonly taken to imply a true di ff erence. It is important not to forget that P /uni00A0 = /uni00A0 0.05 - simply means that there is only a 1:20 chance that the di ff er - ences between the variables would have happened by chance when in fact there is no real di ff erence. If enough variables are .niaid. examined in any study , significant di ff erences will occur simply , 1915–2007, Ohio State University , OH, USA, published their seminal paper more sophisticated analysis to determine the significance of individual risk factors. Univariable or multivariable logistic regression analysis techniques may be appropriate. Statistics simply deal with the chance that observations between populations are di ff erent and should be treated with caution. Clinical results should show clear di ff erences. If sta tistics are required to demonstrate di ff erences betw een results, it is likely that they are unlikely to have major clinical signifi cance. Research integrity

In 2013, Universities UK, in collaboration with major funders of research, developed The Concordat to Support Research Integrity , which sets out key commitments to ensure a high standard in research. It highlights the principles and profes sional responsibilities of researchers and research institutions that are fundamental to the integrity of research wherever it is undertaken. These centre on: /uni25CF honesty in all aspects of research; /uni25CF accountability and transparency in the conduct of re search; /uni25CF professional courtesy and fairness in working with others; /uni25CF good stewardship of research. A study should not under any circumstances commence until appropriate approvals have been granted and compli ance with the principles of research integrity is ensured. A helpful international summary of country-specific approval requirements is available from NIH (https://clinregs nih.gov). Henry Berthold Mann , 1905–2000, and his student Donald Ransom Whitney ‘On a test of whether one of two random variables is stochastically larger than the other’ in 1947. Frank Wilcoxon , 1892–1965, born County Cork, Ireland, American chemist and statistician. Both audit and research commonly require statistical analysis. Many surgeons find the statistical analysis of a project the most di ffi cult part. It is also the most commonly criticised part of papers written by clinicians. There are many useful books about statistics (see Further reading ); if in any doubt, a statis - tician will be pleased to give assistance. Statisticians should be consulted before research or audit has been conducted rather than being presented with the data at the end; they often give helpful advice over stud y design and can be an important part of the project team. The following terms are frequently used when summaris - ing statistical data: /uni25CF Mean : the result of dividing the total by the number of observations (the average). - /uni25CF Median : the middle value with equal numbers of obser - vations above and below – used for numerical or ranked data. /uni25CF Mode : the value with the highest frequency observed – used for nominal data collection. /uni25CF Range : the largest to the smallest value. The most important decision for analysis is whether the distribution of the data is normal (i.e. parametric or non-para - metric). Normally , distributed data have a symmetrical bell- shaped curve, and the mean, median and mode all lie at the same value . The type of data collected determines which sta - tistical test should be used. 1 Numerical and normally distributed (e.g. blood pressure) – use an unpaired t -test to compare two groups or a paired t -test to assess whether a variable has changed between two time points. 2 Numerical but not normally distributed (e.g. tumour size) – use a Mann–Whitney U -test to compare two groups or a Wilcoxon signed rank test to assess whether a variable has increased/stayed the same/decreased between two time points. 3 Categorical (e.g. admitted or not admitted to an intensive - care unit) – a chi-squared test can be used to compare two groups. ( Note : the use of these and any other statistical tests may benefit from professional advice.) Confidence intervals are the best guide to the possible - range in which the true di ff erences are likely to lie. A confi - dence interval that includes zero usually implies a lack of sta - tistical significance. Scientists usually employ probability ( P -values) to describe statistical chance. A P -value <0.05 is commonly taken to imply a true di ff erence. It is important not to forget that P /uni00A0 = /uni00A0 0.05 - simply means that there is only a 1:20 chance that the di ff er - ences between the variables would have happened by chance when in fact there is no real di ff erence. If enough variables are .niaid. examined in any study , significant di ff erences will occur simply , 1915–2007, Ohio State University , OH, USA, published their seminal paper more sophisticated analysis to determine the significance of individual risk factors. Univariable or multivariable logistic regression analysis techniques may be appropriate. Statistics simply deal with the chance that observations between populations are di ff erent and should be treated with caution. Clinical results should show clear di ff erences. If sta tistics are required to demonstrate di ff erences betw een results, it is likely that they are unlikely to have major clinical signifi cance. Research integrity

In 2013, Universities UK, in collaboration with major funders of research, developed The Concordat to Support Research Integrity , which sets out key commitments to ensure a high standard in research. It highlights the principles and profes sional responsibilities of researchers and research institutions that are fundamental to the integrity of research wherever it is undertaken. These centre on: /uni25CF honesty in all aspects of research; /uni25CF accountability and transparency in the conduct of re search; /uni25CF professional courtesy and fairness in working with others; /uni25CF good stewardship of research. A study should not under any circumstances commence until appropriate approvals have been granted and compli ance with the principles of research integrity is ensured. A helpful international summary of country-specific approval requirements is available from NIH (https://clinregs nih.gov). Henry Berthold Mann , 1905–2000, and his student Donald Ransom Whitney ‘On a test of whether one of two random variables is stochastically larger than the other’ in 1947. Frank Wilcoxon , 1892–1965, born County Cork, Ireland, American chemist and statistician. Both audit and research commonly require statistical analysis. Many surgeons find the statistical analysis of a project the most di ffi cult part. It is also the most commonly criticised part of papers written by clinicians. There are many useful books about statistics (see Further reading ); if in any doubt, a statis - tician will be pleased to give assistance. Statisticians should be consulted before research or audit has been conducted rather than being presented with the data at the end; they often give helpful advice over stud y design and can be an important part of the project team. The following terms are frequently used when summaris - ing statistical data: /uni25CF Mean : the result of dividing the total by the number of observations (the average). - /uni25CF Median : the middle value with equal numbers of obser - vations above and below – used for numerical or ranked data. /uni25CF Mode : the value with the highest frequency observed – used for nominal data collection. /uni25CF Range : the largest to the smallest value. The most important decision for analysis is whether the distribution of the data is normal (i.e. parametric or non-para - metric). Normally , distributed data have a symmetrical bell- shaped curve, and the mean, median and mode all lie at the same value . The type of data collected determines which sta - tistical test should be used. 1 Numerical and normally distributed (e.g. blood pressure) – use an unpaired t -test to compare two groups or a paired t -test to assess whether a variable has changed between two time points. 2 Numerical but not normally distributed (e.g. tumour size) – use a Mann–Whitney U -test to compare two groups or a Wilcoxon signed rank test to assess whether a variable has increased/stayed the same/decreased between two time points. 3 Categorical (e.g. admitted or not admitted to an intensive - care unit) – a chi-squared test can be used to compare two groups. ( Note : the use of these and any other statistical tests may benefit from professional advice.) Confidence intervals are the best guide to the possible - range in which the true di ff erences are likely to lie. A confi - dence interval that includes zero usually implies a lack of sta - tistical significance. Scientists usually employ probability ( P -values) to describe statistical chance. A P -value <0.05 is commonly taken to imply a true di ff erence. It is important not to forget that P /uni00A0 = /uni00A0 0.05 - simply means that there is only a 1:20 chance that the di ff er - ences between the variables would have happened by chance when in fact there is no real di ff erence. If enough variables are .niaid. examined in any study , significant di ff erences will occur simply , 1915–2007, Ohio State University , OH, USA, published their seminal paper more sophisticated analysis to determine the significance of individual risk factors. Univariable or multivariable logistic regression analysis techniques may be appropriate. Statistics simply deal with the chance that observations between populations are di ff erent and should be treated with caution. Clinical results should show clear di ff erences. If sta tistics are required to demonstrate di ff erences betw een results, it is likely that they are unlikely to have major clinical signifi cance.