A simple and robust alternative to Bland-Altman method of assessing clinical agreement

Clinical agreement between two quantitative measurements on a group of subjects is generally assessed with the help of the Bland-Altman (B-A) limits. These limits only describe the dispersion of disagreements in 95% cases and do not measure the degree of agreement. The interpretation regarding the presence or absence of agreement by this method is based on whether B-A limits are within the pre-specified externally determined clinical tolerance limits. Thus, clinical tolerance limits are necessary for this method. We argue in this communication that the direct use of clinical tolerance limits for assessing agreement without the B-A limits is more effective and has tremendous merits. This nonparametric approach is simple, is robust to the distribution pattern and outliers, has more flexibility, and exactly measures the degree of clinical agreement. This is explained with the help of two examples, including setups where clinical tolerance limits can be set up to follow varying trends if required in the clinical context – a feature not available in the B-A method

Receiver operating characteristic (ROC) curve for medical researchers

Sensitivity and specificity are two components that measure the inherent validity of a diagnostic test for dichotomous outcomes against a gold standard. Receiver operating characteristic (ROC) curve is the plot that depicts the trade-off between the sensitivity and (1-specificity) across a series of cut-off points when the diagnostic test is continuous or on ordinal scale (minimum 5 categories). This is an effective method for assessing the performance of a diagnostic test. The aim of this article is to provide basic conceptual framework and interpretation of ROC analysis to help medical researchers to use it effectively. ROC curve and its important components like area under the curve, sensitivity at specified specificity and vice versa, and partial area under the curve are discussed. Various other issues such as choice between parametric and non-parametric methods, biases that affect the performance of a diagnostic test, sample size for estimating the sensitivity, specificity, and area under ROC curve, and details of commonly used softwares in ROC analysis are also presented

裏表紙

Lambda-Mu-Sigma and Box-Cox Power Exponential are popular methods for constructing centile curves but are difficult to understand for medical professionals. As a result, the methods are used by experts only. Non-experts use software as a blackbox that can lead to wrong curves. This article explains these methods in a simple non-mathematical language so that medical professionals can use them correctly and confidently

Reversal in declining trend of adult mortality in many states of India, 1970-2001: Is it due to AIDS?

Objectives: To investigate the reversal in adult mortality trend from declining to rising in some segments of population in India, and to use an indirect demographic method to examine if this increase could be due to AIDS mortality. Also, to estimate the total excess deaths. Design: Cross-sectional data on age-specific death rate in 5-year age-intervals from 25 to 44 years for the years 1970 to 1998 for rural/urban and male/female segments for each of 16 major states of India obtained from the government reports, and their projections till the year 2001. Methods: In view of reversal of trend in some areas, we tried to fit a parabola to the observed rates in each segment. A statistically significant fit in some segments revealed the year with least mortality rate when the reversal started. Another fit was obtained by projection of the previously declining trend. Excess deaths were estimated by applying the excess death rate to the population of the segment where reversal in trend was significant. Results: Reversal in declining mortality trend was detected in 65 of a total 256 age-sex-area (urban/rural) segments that we examined. Fourteen of the 16 States revealed reversal in at least one segment. The year of reversal in most segments coincides fairly well with the anticipated year of start of substantial AIDS mortality. At the national level, a total of at least 214,390 deaths till 2001 were revealed as excess by this method. This number is quite low relative to the deaths otherwise attributed to AIDS in the country. Contrary to belief, increase in mortality due to AIDS was seen more commonly in rural areas than in urban areas, and more in females than in males. Conclusions: The indirect demographic method of estimating AIDS deaths in India yields an apparently low number of deaths, and does not confirm the belief that AIDS in India is spreading from urban to rural, and from male to female populations. Keywords: AIDS deaths, Mortality trend reversal, States of India, Age-specific death rates, Demographic metho

Trends in tobacco use in Nepal

This article does not have an abstract

A SIMPLE INDEX OF SMOKING

Background: Cigarette smoking is implicated in a large number of diseases and other adverse health conditions. Among the dimensions of smoking are number of cigarettes smoked per day, duration of smoking, passive smoking, smoking of filter cigarettes, age at start, and duration elapsed since quitting by ex-smokers. The practice so far is to study most of these separately. We develop a simple index that integrates these dimensions of smoking into a single metric, and suggest that this index be developed further. Method: The index is developed under a series of natural assumptions. Broadly, these are (i) the burden of smoking monotonically increases with the cigarette-years but it is more severe in the beginning, (ii) start of smoking early in life is more burdensome than a late start, and (iii) the burden gradually reverses as the duration elapsed since cessation by ex-smokers increases. Result: The index so arrived is: S = (3 – a/15)*1/2*sqrt[sumof(pi*ni*xi) – 0.5] - y for S greater than equal to 0, and sumof(pi*ni*xi) greater than equal to 0.5; otherwise zero (use a =30 for a\u3e30); where i = 1, 2, …, I, and I is the number of segments in life with different smoking pattern and a is the age at start of smoking, pi is the proportion of smoke inhaled in case of passive smoking (or adjustment for filter cigarettes or for other forms of smoking), xi is the number of cigarettes smoked for ni years, and y is the number of years elapsed since cessation by ex-smokers. Negative values of S are to be considered equal to zero. Examples are given that demonstrate the use of this index. Conclusion: Just as almost any other composite index, our index too could be good as a comprehensive measure of burden of smoking but not to study its individual dimensions. This measures the present burden in absolute sense and not the risk of smoking-related diseases. Like body-mass index, the smoking index may have good correlation with the risk of some diseases and poor for many others, depending upon the extent to which the risk of disease agrees to our postulations

Statistical fallacies in orthopedic research

Background: A large number of statistical fallacies occur in medical research literature. These are mostly inadvertent and occur due to lack of understanding of the statistical concepts and terminologies. Many researchers do not fully appreciate the consequence of such fallacies on the credibility of their report. Materials and Methods: This article provides a general review of the issues that could give rise to statistical fallacies with focus on orthopedic research. Some of this is based on real-life literature and some is based on the actual experiences of the author in dealing with medical research over the past three decades. The text is in teaching mode rather than research mode. Results: Statistical fallacies occur due to inadequate sample that is used for generalized conclusion; incomparable groups presented as comparable; mixing of two or more distinct groups that in fact require separate consideration; misuse of percentages, means and graphs; incomplete reporting that suppresses facts; ignoring reality and depending instead on oversimplification; forgetting baseline values that affect the outcome; misuse of computer packages and use of black-box approach; misuse of P -values that compromises conclusions; confusing correlation with cause-effect; and interpreting statistical significance as medical significance. Conclusion: Mere awareness of the situations where statistical fallacies can occur may be adequate for researchers to sit up and take note while trying to provide a credible report