The Experts below are selected from a list of 87 Experts worldwide ranked by ideXlab platform

Sandy Lovie - One of the best experts on this subject based on the ideXlab platform.

Tom Coulthard - One of the best experts on this subject based on the ideXlab platform.

  • The Leaf Plot: a novel way of presenting the value of tests.
    The British journal of general practice : the journal of the Royal College of General Practitioners, 2019
    Co-Authors: Malcolm G. Coulthard, Tom Coulthard
    Abstract:

    The power and accuracy of clinical tests is usually reported either in terms of their sensitivity and specificity, their predictive values, or their likelihood ratios, but these concepts can be difficult for many GPs to apply to real-life clinical situations.1 ### Sensitivity and specificity These are independent of the prevalence of the condition (or its equivalent in an individual patient, your estimate of their pre-test probability of having the condition), and so cannot answer the clinician’s question of ‘How much does a positive or a negative result for this test or sign influence the probability of my provisional diagnosis?’ Correctly interpreting these values is difficult, and requires us to grasp non-intuitive concepts with ‘both sides of our brains’.2 ### Positive (PPV) and negative (NPV) predictive values These seem to make more sense, but are misleading because they can only be applied to populations with the same prevalence of the condition as was present in the study that generated them. For example, studies in special educational facilities show that finding a child with a single-palmar-crease gives a PPV of them having Down’s syndrome of about 75%, but if you notice this pattern in the setting of a normal infant having a 6-week check it then would only have a PPV of about 10%. ### Positive and negative likelihood ratios These seem more helpful because they determine how a test result will alter the pre-test odds, but it is not straightforward to quantify their impact for an individual patient. The clinician has to estimate that person’s pre-test odds of having the diagnosis (= probability/1 – probability), and then multiply that by the appropriate likelihood ratio to find their new odds. Because these methods are difficult to apply accurately in real practice, they may cause doctors to make vast errors when estimating the significance of screening results.3 Very few GPs use them in any formal way, instead relying …

Avijit Hazra - One of the best experts on this subject based on the ideXlab platform.

  • biostatistics series module 1 basics of biostatistics
    Indian Journal of Dermatology, 2016
    Co-Authors: Avijit Hazra, Nithya J Gogtay
    Abstract:

    Although application of statistical methods to biomedical research began only some 150 years ago, statistics is now an integral part of medical research. A knowledge of statistics is also becoming mandatory to understand most medical literature. Data constitute the raw material for statistical work. They are records of measurement or observations or simply counts. A variable refers to a particular character on which a set of data are recorded. Data are thus the values of a variable. It is important to understand the different types of data and their mutual interconversion. Biostatistics begins with descriptive statistics that implies summarizing a collection of data from a sample or population. Categorical data are described in terms of percentages or proportions. With numerical data, individual observations within a sample or population tend to cluster about a central location, with more extreme observations being less frequent. The extent to which observations cluster is summarized by measures of central tendency while the spread can be described by measures of dispersion. The confidence interval (CI) is an increasingly important measure of precision. When we observe samples, there is no way of assessing true population parameters. We can, however, obtain a standard error and use it to define a range in which the true population value is likely to lie with a certain acceptable level of uncertainty. This range is the CI while its two terminal values are the confidence limits. Conventionally, the 95% CI is used. Patterns in data sets or data distributions are important, albeit not so obvious, component of descriptive statistics. The most common distribution is the normal distribution which is depicted as the well-known symmetrical bell-shaped Gaussian curve. Familiarity with other distributions such as the binomial and Poisson distributions is also helpful. Various graphs and Plots have been devised to summarize data and trends visually. Some Plots, such as the box-and-whiskers Plot and the stem-and-Leaf Plot are used less often but provide useful summaries in select situations.

  • Biostatistics without the mathematics. Part 1-Descriptive statistics
    2013
    Co-Authors: Avijit Hazra
    Abstract:

    Biostatistics is now an integral part of medical research. Knowledge of statistics is also becoming mandatory to understand most medical literature. The word data denotes the values of variables. It is important to understand the types of data and their mutual interconversion. The raw data for statistical analyses come from experiments or observations and can be numerical or categorical. Numerical variables may be continuous or discrete. Categorical data are described in terms of frequencies, proportions, or percentages. The applications of statistics in medical sciences can be categorized as descriptive statistics, inferential statistics and statistical modeling. Descriptive statistics implies summarizing a collection of data from a population. The observations within a sample tend to cluster around a central location, with more extreme observations being less frequent. The extent to which the observations cluster is summarized by measures of central tendency, while the spread is described by measures of dispersion. The measurement of central tendency include mean, median and mode, while the measurement of dispersion include range, standard deviation, mean deviation and others. The population mean, median, standard deviation, etc., are known as the parameters, while the sample mean, median, standard deviation, etc., are known as the statistics. We can hardly know the true values of parameters. However, we can obtain a reasonable point estimate of a parameter and define an interval in which the true population value is likely to lie with a certain level of confidence. This range is known as the confidence interval (CI). A CI of a parameter that has X% confidence is defined as an interval so that the parameter will lie within this interval with probability X. Conventionally, a 95% CI is used for most analyses. Understanding patterns in data sets and the distribution of the corresponding population are important components of descriptive statistics. The most common distribution is the normal distribution, which is depicted as the well-known symmetrical bell-shaped Gaussian curve. Familiarity with other distributions such as the binomial and Poisson distributions is also helpful. Various graphs and Plots have been devised to summarize data and trends visually. Some Plots, such as the box-and-whiskers Plot and the stemand-Leaf Plot are less familiar but provide useful summaries in select situations.

Malcolm G. Coulthard - One of the best experts on this subject based on the ideXlab platform.

  • The Leaf Plot: a novel way of presenting the value of tests.
    The British journal of general practice : the journal of the Royal College of General Practitioners, 2019
    Co-Authors: Malcolm G. Coulthard, Tom Coulthard
    Abstract:

    The power and accuracy of clinical tests is usually reported either in terms of their sensitivity and specificity, their predictive values, or their likelihood ratios, but these concepts can be difficult for many GPs to apply to real-life clinical situations.1 ### Sensitivity and specificity These are independent of the prevalence of the condition (or its equivalent in an individual patient, your estimate of their pre-test probability of having the condition), and so cannot answer the clinician’s question of ‘How much does a positive or a negative result for this test or sign influence the probability of my provisional diagnosis?’ Correctly interpreting these values is difficult, and requires us to grasp non-intuitive concepts with ‘both sides of our brains’.2 ### Positive (PPV) and negative (NPV) predictive values These seem to make more sense, but are misleading because they can only be applied to populations with the same prevalence of the condition as was present in the study that generated them. For example, studies in special educational facilities show that finding a child with a single-palmar-crease gives a PPV of them having Down’s syndrome of about 75%, but if you notice this pattern in the setting of a normal infant having a 6-week check it then would only have a PPV of about 10%. ### Positive and negative likelihood ratios These seem more helpful because they determine how a test result will alter the pre-test odds, but it is not straightforward to quantify their impact for an individual patient. The clinician has to estimate that person’s pre-test odds of having the diagnosis (= probability/1 – probability), and then multiply that by the appropriate likelihood ratio to find their new odds. Because these methods are difficult to apply accurately in real practice, they may cause doctors to make vast errors when estimating the significance of screening results.3 Very few GPs use them in any formal way, instead relying …

Cheng Chen - One of the best experts on this subject based on the ideXlab platform.

  • Recurrence risk model for esophageal cancer after radical surgery
    Chinese journal of cancer research = Chung-kuo yen cheng yen chiu, 2013
    Co-Authors: Hua Tao, Dan Song, Cheng Chen
    Abstract:

    Objective: The aim of the present study was to construct a risk assessment model which was tested by disease-free survival (DFS) of esophageal cancer after radical surgery. Methods: A total of 164 consecutive esophageal cancer patients who had undergone radical surgery between January 2005 and December 2006 were retrospectively analyzed. The cutpoint of value at risk (VaR) was inferred by stem-and-Leaf Plot, as well as by independent-samples t-test for recurrence-free time, further confirmed by crosstab chi-square test, univariate analysis and Cox regression analysis for DFS. Results: The cutpoint of VaR was 0.3 on the basis of our model. The rate of recurrence was 30.3% (30/99) and 52.3% (34/65) in VaR