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Sampling Distribution of the Mean
Published in Marcello Pagano, Kimberlee Gauvreau, Heather Mattie, Principles of Biostatistics, 2022
Marcello Pagano, Kimberlee Gauvreau, Heather Mattie
In the previous chapter we examine theoretical probability distributions, such as the binomial distribution and the normal distribution. In all cases the relevant population parameters are assumed to be known; this allows us to describe the distributions completely and calculate the probabilities associated with various outcomes. In most practical applications, however, we are not given the values of these parameters. Instead, we must attempt to describe or estimate a population parameter – such as the mean of a normally distributed random variable – using the information contained in a sample of observations selected from the population. The process of drawing conclusions about an entire population based on the information contained in a sample is known as statistical inference.
Quantitative Methods for Analyzing Experimental Studies in Patient Ergonomics Research
Published in Richard J. Holden, Rupa S. Valdez, The Patient Factor, 2021
Kapil Chalil Madathil, Joel S. Greenstein
Statistical inference techniques enable us to draw conclusions about a population from a sample. A sample is a subset of participants from the population about which we are interested in developing conclusions. Analyzing the data collected from patient ergonomics studies, like other experimental studies, consists of determining descriptive statistics (such as the mean, median, and standard deviation) to summarize the data collected for the sample, followed by the determination of inferential statistics (such as confidence intervals and effect sizes) to generalize the findings from the sample to the entire population.
I am going to make a prediction model. What do I need to know?
Published in Thomas A. Gerds, Michael W. Kattan, Medical Risk Prediction, 2021
Thomas A. Gerds, Michael W. Kattan
A statistical inference problem starts by defining the target population and the parameter of interest. Typical parameters are regression coefficients that describe the association of treatment or other exposure with the outcome. Statistical estimates of the parameters are based on a representative sample of the population and a regression model. Goodness-of-fit tests are used to check a model's validity. Confidence intervals are used to express the uncertainty about the estimate of the parameter of interest.
Significance test for linear regression: how to test without P-values?
Published in Journal of Applied Statistics, 2021
Paravee Maneejuk, Woraphon Yamaka
To further illustrate, we consider an experiment to make comparisons directly among p-value, Bayes factor and plausibility approaches under the linear regression context. We start with the following data generating process, p-value, we use the conventional statistical inference, in which the p-value is equal to or lower than thresholds namely 0.10, 0.05, and 0.01, to make a decision about the null hypothesis. Likewise, the plausibility-based belief function is interpreted in the same way as the p-value. On the other hand, in the case of the minimum Bayes factor approach, the interpretation is different from the first two methods as we make the decision upon the MBF following the Held and Ott [9] labeled intervals as presented in Table 2. Our interest is to see whether these methods will reveal any non-significant outcome when the null is false and reveal the significant outcome when the null is true. The results of the method comparison are provided in the following Figures and Tables.
The use of PROMIS patient-reported outcomes (PROs) to inform light chain (AL) amyloid disease severity at diagnosis
Published in Amyloid, 2020
Anita D’Souza, Brooke E. Magnus, Judith Myers, Angela Dispenzieri, Kathryn E. Flynn
Our study is limited by the small sample size and thus limited power for statistical inference. Within our known groups of disease severity, Fatigue showed a large score difference (of >5 points on the T-score metric) between groups but did not meet our statistical threshold for significance and this may well be owing to our small sample size. We did not include other measures such as KCCQ or FACIT to compare the convergent/discriminant validity of PROMIS in AL amyloidosis, but this is an area for future research. Despite the small sample size in our study, we are encouraged that we were able to make substantive additions to the understanding of HRQoL measurement in AL disease, identify important PRO domains that need to be studied, and the next steps needed to advance this work. Our next steps include qualitative research to further understand the mental health domains in AL amyloidosis, assessing changes in PROs over time, and expanding the work to larger groups of patients. Ultimately, the goal of our work is to identify PROs that may be most useful in determining change in status and could serve as a clinical outcomes assessment tool in AL clinical trials.
The impact of pneumococcal conjugate vaccines on serotype 19A nasopharyngeal carriage
Published in Expert Review of Vaccines, 2019
Myint Tin Tin Htar, Heather L. Sings, Maria Syrochkina, Bulent Taysi, Betsy Hilton, Heinz-Josef Schmitt, Bradford D. Gessner, Luis Jodar
The current review has several limitations. Most of the studies in this review are ecological in nature. As such, results of these studies can be variable, and differences in carriage specimen ascertainment, methods for identifying pneumococcus and pneumococcal serotypes, seasonality and population can all play a role. Moreover, PCV use is not the only time-dependent variable in a particular setting that can alter serotype 19A carriage; for example, antibiotic use patterns or the emergence of a more antibiotic resistant clone, could change serotype 19A epidemiology. In addition, many studies had small sample numbers, which limits statistical inference. Lastly, vaccines can impact carriage at several levels including by reducing carriage acquisition, density or duration, or by reducing transmission. Most studies did not distinguish between these components and assessed only the summary outcome of cross-sectional carriage prevalence, although a minority of studies also assessed carriage acquisition. Cross-reactivity by serotype 19F could possibly affect one of these components but not others, for example, reducing density but not acquisition.