This research report explores how slight modifications to the sampling protocol of Response Driven Sampling (RDS) could impact estimates. Two modifications are tested: 1) randomly selecting a contact from those provided by participants, rather than letting participants choose, and 2) weighting sampling preference according to a covariate related to the population of interest. Both modifications are found to produce estimates with smaller mean-square-error than normal RDS, but have similar sensitivity to seed participants. The document provides details on the simulation methodology used to test the modifications, including how social network structures and transition matrices were modeled.
Estimating design effect and calculating sample size for respondent driven sa...Dung Tri
This document discusses estimating sample sizes for respondent-driven sampling (RDS) studies of injection drug users. It finds that the common recommendation of doubling the sample size to account for a design effect of 2 underestimates the true design effect. By analyzing data from 43 RDS samples of injection drug users, the study finds the design effect is closer to 4. It presents a formula for calculating sample size in RDS studies among injection drug users using a design effect of 4. The results highlight the importance of accounting for the sampling design when analyzing RDS data and estimating sample sizes.
Designing studies with recurrent events | Model choices, pitfalls and group s...nQuery
In this free webinar, we will examine the important design considerations for analyzing recurring events and counts.
Watch the webinar at: https://www.statsols.com/en/webinar/designing-studies-with-recurrent-events
Designing studies with recurrent events (Model choices, pitfalls and group sequential design)
Practical Methods To Overcome Sample Size ChallengesnQuery
Watch the video at: https://www.statsols.com/webinars/practical-methods-to-overcome-sample-size-challenges
In this webinar hosted by Ronan Fitzpatrick - Head of Statistics and nQuery Lead Researcher at Statsols - we will examine some of the most common practical challenges you will experience while calculating sample size for your study. These challenges will be split into two categories:
1. Overcoming Sample Size Calculation Challenges
(Survival Analysis Example)
We will examine practical methods to overcome common sample size calculation issues by focusing in on one of the more complex areas for sample size determination; Survival analysis. We will cover difficulties and potential issues surrounding challenges such as:
Drop Out: How to deal with expected dropouts or censoring. We compare the simple loss-to-follow-up method and integrating a dropout process into the sample size model?
Planning Uncertainty: How best to deal with the inevitable uncertainty at the planning stage? We examine how best to apply a sensitivity analysis and Bayesian approaches to explore the uncertainty in your sample size calculations.
Choosing the Effect Size: Various approaches and interpretations exist for how to find the effect size value. We examine those contrasting interpretations and determine the best method and also how to deal with parameterization options.
2. Overcoming Study Design Challenges
(Vaccine Efficacy Example)
The Randomised Controlled Trial (RCT) is considered the gold standard in trial design in drug development. However, there are often practical impediments which mean that adjustments or pragmatic approaches are needed for some trials and studies.
We will examine practical methods how to overcome common study design challenges and how these affect your sample size calculations. In this webinar, we will use common issues in vaccine study design to examine difficulties surrounding issues such as:
Case-Control Analysis: We will examine how to deal with study constraints and how to deal with analyses done during an observational study.
Alternative Randomization Methods: How best to address randomization in your vaccine trial design when full randomization is difficult, expensive or impractical. We examine how sample size calculations are affected with cluster or Mendelian randomization.
Rare Events: How does an outcome being rare affect the types of study design and statistical methods chosen in your study.
This document discusses factors to consider when determining sample size for a study, including the target population, level of significance, power, expected effect size, and standard deviation. It states that sample size depends on finding an acceptable balance between being large enough to detect meaningful effects but not so large as to detect trivial effects. The sample must be big enough to detect scientifically significant effects that are also statistically significant when accounting for the level of significance, power, expected effect size, and variation within the population. Both under-powered and over-powered studies have drawbacks, so sample size estimation requires considering these various factors.
Power and sample size calculations for survival analysis webinar SlidesnQuery
This webinar presentation introduced sample size determination for survival analysis. It discussed how to estimate the appropriate sample size, key considerations for survival analysis including expected survival curves and handling dropouts. It demonstrated an example in nQuery software to calculate the sample size needed for a clinical trial to show a risk reduction in progression-free survival between treatment arms. The webinar concluded with plans to further enhance survival analysis capabilities in nQuery and addressed questions from participants.
This document provides information on how to determine the required sample size for research studies. It discusses determining sample size for proportions and means. For proportions, it requires estimating the population size, rate in the population, maximum acceptable difference from the true rate, and desired confidence level. For means, it requires estimating the population standard deviation, maximum acceptable difference from the true mean, and desired confidence level. An example is provided for each that walks through calculating the required sample size.
2020 trends in biostatistics what you should know about study design - slid...nQuery
2020 Trends In Biostatistics - What you should know about study design.
In this free webinar you will learn about:
-Adaptive designs in confirmatory trials
-Using external data in study planning
-Innovative designs in early-stage trials
To watch the full webinar:
https://www.statsols.com/webinar/2020-trends-in-biostatistics-what-you-should-know-about-study-design
This document provides an outline for a presentation on determining sample size. It discusses key concepts like what sample size is, why determining an appropriate sample size is important, and factors that affect sample size calculations like available resources, required accuracy, and study design. The presentation aims to help audiences understand how to determine sample sizes and how to apply the concept in research and studies.
Estimating design effect and calculating sample size for respondent driven sa...Dung Tri
This document discusses estimating sample sizes for respondent-driven sampling (RDS) studies of injection drug users. It finds that the common recommendation of doubling the sample size to account for a design effect of 2 underestimates the true design effect. By analyzing data from 43 RDS samples of injection drug users, the study finds the design effect is closer to 4. It presents a formula for calculating sample size in RDS studies among injection drug users using a design effect of 4. The results highlight the importance of accounting for the sampling design when analyzing RDS data and estimating sample sizes.
Designing studies with recurrent events | Model choices, pitfalls and group s...nQuery
In this free webinar, we will examine the important design considerations for analyzing recurring events and counts.
Watch the webinar at: https://www.statsols.com/en/webinar/designing-studies-with-recurrent-events
Designing studies with recurrent events (Model choices, pitfalls and group sequential design)
Practical Methods To Overcome Sample Size ChallengesnQuery
Watch the video at: https://www.statsols.com/webinars/practical-methods-to-overcome-sample-size-challenges
In this webinar hosted by Ronan Fitzpatrick - Head of Statistics and nQuery Lead Researcher at Statsols - we will examine some of the most common practical challenges you will experience while calculating sample size for your study. These challenges will be split into two categories:
1. Overcoming Sample Size Calculation Challenges
(Survival Analysis Example)
We will examine practical methods to overcome common sample size calculation issues by focusing in on one of the more complex areas for sample size determination; Survival analysis. We will cover difficulties and potential issues surrounding challenges such as:
Drop Out: How to deal with expected dropouts or censoring. We compare the simple loss-to-follow-up method and integrating a dropout process into the sample size model?
Planning Uncertainty: How best to deal with the inevitable uncertainty at the planning stage? We examine how best to apply a sensitivity analysis and Bayesian approaches to explore the uncertainty in your sample size calculations.
Choosing the Effect Size: Various approaches and interpretations exist for how to find the effect size value. We examine those contrasting interpretations and determine the best method and also how to deal with parameterization options.
2. Overcoming Study Design Challenges
(Vaccine Efficacy Example)
The Randomised Controlled Trial (RCT) is considered the gold standard in trial design in drug development. However, there are often practical impediments which mean that adjustments or pragmatic approaches are needed for some trials and studies.
We will examine practical methods how to overcome common study design challenges and how these affect your sample size calculations. In this webinar, we will use common issues in vaccine study design to examine difficulties surrounding issues such as:
Case-Control Analysis: We will examine how to deal with study constraints and how to deal with analyses done during an observational study.
Alternative Randomization Methods: How best to address randomization in your vaccine trial design when full randomization is difficult, expensive or impractical. We examine how sample size calculations are affected with cluster or Mendelian randomization.
Rare Events: How does an outcome being rare affect the types of study design and statistical methods chosen in your study.
This document discusses factors to consider when determining sample size for a study, including the target population, level of significance, power, expected effect size, and standard deviation. It states that sample size depends on finding an acceptable balance between being large enough to detect meaningful effects but not so large as to detect trivial effects. The sample must be big enough to detect scientifically significant effects that are also statistically significant when accounting for the level of significance, power, expected effect size, and variation within the population. Both under-powered and over-powered studies have drawbacks, so sample size estimation requires considering these various factors.
Power and sample size calculations for survival analysis webinar SlidesnQuery
This webinar presentation introduced sample size determination for survival analysis. It discussed how to estimate the appropriate sample size, key considerations for survival analysis including expected survival curves and handling dropouts. It demonstrated an example in nQuery software to calculate the sample size needed for a clinical trial to show a risk reduction in progression-free survival between treatment arms. The webinar concluded with plans to further enhance survival analysis capabilities in nQuery and addressed questions from participants.
This document provides information on how to determine the required sample size for research studies. It discusses determining sample size for proportions and means. For proportions, it requires estimating the population size, rate in the population, maximum acceptable difference from the true rate, and desired confidence level. For means, it requires estimating the population standard deviation, maximum acceptable difference from the true mean, and desired confidence level. An example is provided for each that walks through calculating the required sample size.
2020 trends in biostatistics what you should know about study design - slid...nQuery
2020 Trends In Biostatistics - What you should know about study design.
In this free webinar you will learn about:
-Adaptive designs in confirmatory trials
-Using external data in study planning
-Innovative designs in early-stage trials
To watch the full webinar:
https://www.statsols.com/webinar/2020-trends-in-biostatistics-what-you-should-know-about-study-design
This document provides an outline for a presentation on determining sample size. It discusses key concepts like what sample size is, why determining an appropriate sample size is important, and factors that affect sample size calculations like available resources, required accuracy, and study design. The presentation aims to help audiences understand how to determine sample sizes and how to apply the concept in research and studies.
Sample size for survival analysis - a guide to planning successful clinical t...nQuery
Determining the appropriate number of events needed for survival analysis is a complex task as study planners try to predict what sample size will be needed after accounting for the complications of unequal follow-up, drop-out and treatment crossover.
The statistical, logistical and ethical considerations all complicate life for biostatisticians as issues to balance in planning a survival analysis. However, this complexity has created a need for new analyses and procedures to help the planning process for survival analysis trials.
The wider move from fixed to flexible designs has opened up opportunities for advanced methods such as adaptive design and Bayesian analysis to help deal with the unique complications of planning for survival data but these methods have their own complications that need to be explored too.
This document discusses important considerations for determining appropriate sample sizes, including ensuring samples are representative of the overall population and large enough to show real effects. It provides guidelines for minimum sample sizes for different population sizes to achieve 95% confidence with a 5% margin of error. Additional factors that influence sample size are the statistical tests to be used, effect size, and desired level of statistical power or confidence. Stratified random sampling is recommended to obtain a representative sample.
This document discusses key concepts related to sample and sample size in research. It defines a population as the entire group being studied, while a sample is a subset of the population. The size of the sample is represented by n and should be large enough to accurately represent the population within a desired level of confidence and margin of error. The document provides formulas for calculating sample size based on factors like population variance, desired confidence level, and acceptable margin of error. An optimal sample size allows for appropriate analysis while minimizing sampling error.
This document discusses determining the appropriate size of a sample from a population. It explains that a sample should be small enough to be practical but large enough to accurately represent the population within a desired level of precision and confidence level. The key factors in determining sample size are the homogeneity of the population, number of classes or groups in the population, nature of the study, type of sampling method, desired accuracy and confidence level, availability of resources, and other considerations like the nature and size of units, size of the population, and time constraints. The document concludes by mentioning an approach to determining sample size based on precision rate and confidence level.
Minimizing Risk In Phase II and III Sample Size CalculationnQuery
[ Watch Webinar: http://bit.ly/2thIgmi ]. In this free webinar, Head of Statistics at Statsols, Ronan Fitzpatrick, addresses the issues of reducing risk in Phase II/III sample size calculations. Topics covered will include:
Sample Size Determination For Different Trial Designs
Bayesian Sample Size Determination
Sample Size For Survival Analysis
& more
This document discusses sample size calculations for clinical trials. It explains that statistical methods can be used to determine the minimum number of patients needed to meet a trial's objectives with a given statistical power, while also considering practical and ethical factors. The document then provides more details on the statistical approaches, including discussing the general formula for sample size calculation and examples of calculating sample sizes for t-tests, survival analyses, and case-control studies. Key inputs for these calculations are described.
5 essential steps for sample size determination in clinical trials slidesharenQuery
In this free webinar hosted by nQuery Researcher & Statistician Eimear Keyes, we map out the 5 essential steps for sample size determination in clinical trials. At each step, Eimear will highlight the important function it plays and how to avoid the errors that will negatively impact your sample size determination and therefore your study.
Watch the Video: https://www.statsols.com/webinar/the-5-essential-steps-for-sample-size-determination
Non-inferiority and Equivalence Study design considerations and sample sizenQuery
About the webinar
This webinar examines the role of non-inferiority and equivalence in study design
In this free webinar, you will learn about:
-Regulatory information on this type of study design
-Considerations for study design and your sample size
-Practical worked examples of
--Non-inferiority Testing
--Equivalence Testing
Duration - 60 minutes
Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols
Watch the video at: https://www.statsols.com/webinars
This document provides an overview of key statistical concepts for medical research including:
- Common measures like mean, standard deviation, confidence intervals, and p-values.
- Study designs such as randomized controlled trials.
- Tests for comparing groups like t-tests, ANOVA, and chi-square tests.
- Measures of disease frequency and test accuracy like sensitivity and specificity.
- The importance of understanding statistics for medical research and exams.
- Examples of choosing the appropriate statistical tests based on the study design and variables.
In 3 sentences or less, it orients the reader to fundamental epidemiological and biostatistical concepts for medical research and exam preparation.
This document summarizes some key statistical considerations for clinical trials comparing a similar biotherapeutic product (SBP) to a reference biotherapeutic product (RBP). It discusses that equivalence or non-inferiority trials are generally acceptable designs, with equivalence preferred. It notes that determining the appropriate design, equivalence/non-inferiority margins, sample size calculations, and ensuring assay sensitivity and the constancy assumption are all important statistical principles that must be carefully considered in order to draw valid conclusions about the clinical similarity of an SBP to an RBP.
This document discusses sample size estimation and informed consent for research studies. It explains the importance of calculating an appropriate sample size to avoid interpreting studies with inadequate samples or wasting resources. The key factors for determining sample size are outlined, including the type of study, primary outcome, acceptable margin of error, and desired power. The document also covers sampling methods, types of errors, effect sizes, and adjusting sample sizes. It emphasizes the need for informed consent and outlines the necessary elements and information to include in participant information sheets to properly consent participants.
This document discusses factors to consider when determining sample size for research, including the size of the population, available resources, desired accuracy or precision, homogeneity of the population, nature of the study, sampling method, and nature of respondents. It provides formulas for calculating sample size based on population standard deviation, difference between population and sample means, standard error of the mean, and standard error of proportion. An example shows how to calculate a sample size of 60 when the population standard deviation is 6, desired precision is 99%, and difference between population and sample means is 2.
This document provides information and examples on calculating sample size for clinical studies. It discusses key factors that affect sample size calculation, including minimum important difference, standard deviation, power, type I and II errors, study design, dropout rate, and compliance. It provides step-by-step worked examples of calculating sample size for various hypothetical clinical studies. The document emphasizes that sample size calculation is important to ensure studies are adequately powered and conclusions are valid.
Webinar slides- alternatives to the p-value and power nQuery
What are the alternatives to the p-value & power? What is the next step for sample size determination? We will explore these issues in this free webinar presented by nQuery
Sample size calculations are an important step in planning epidemiological studies. An adequate sample size is needed to ensure reliable results, while samples that are too large or small can lead to wasted resources or inaccurate findings. Different study designs require different sample size calculation methods. Factors considered include the desired precision or confidence level, population parameters, and variability. Several formulas and online calculators exist to determine appropriate sample sizes for estimating means, proportions, and comparing groups in studies like clinical trials, surveys, case-control studies, and experiments. Larger effects, more samples, less variability, and higher significance levels can increase a test's statistical power.
1. The document discusses sample size calculation for various study designs including cross-sectional studies, case-control studies, and clinical trials. It provides the key formulas and parameters involved in sample size calculation including confidence level, precision, power, and Z-values.
2. Several examples are provided to demonstrate how to calculate the minimum required sample size given information about the study design, variables, expected outcomes, confidence level, and power.
3. Key factors that determine sample size are confidence level, precision or minimum detectable difference, power, and values of outcomes in pilot or previous studies. The appropriate formulas are selected based on the study design and scale of measurement of variables.
The document discusses key principles of sampling size for estimating values from samples. It states that the information provided by a sample increases with sample size, and samples that are larger are more representative of the overall population. However, larger samples also require more time and resources. The document then provides guidelines for determining appropriate sample sizes for estimating averages, variances, and fitting lines to data based on desired confidence levels and estimates of standard deviations.
Extending A Trial’s Design Case Studies Of Dealing With Study Design IssuesnQuery
This document discusses several case studies of dealing with complex study design issues in clinical trials, including non-proportional hazards, cluster randomization, and three-armed trials. The agenda outlines topics on non-proportional hazards modeling and sample size considerations, cluster randomized and stepped-wedge designs, and methods for analyzing data from three-armed trials that include experimental, reference, and placebo groups. Worked examples are provided to illustrate sample size calculations and statistical approaches for each of these complex trial design scenarios.
This document discusses key concepts related to determining sample size for surveys:
- Confidence interval and confidence level describe the level of certainty or precision in a sample - a 95% confidence level means the true population value would fall within the confidence interval 95% of the time.
- Sample size, population size, and response distribution (how answers are split) all impact the required sample size to achieve a given confidence level and interval. Higher confidence or lower intervals require larger samples.
- For a population of 20,000, with a 50-50 response split, and 95% confidence level, the required sample size is 377 people.
Sample size for survival analysis - a guide to planning successful clinical t...nQuery
Determining the appropriate number of events needed for survival analysis is a complex task as study planners try to predict what sample size will be needed after accounting for the complications of unequal follow-up, drop-out and treatment crossover.
The statistical, logistical and ethical considerations all complicate life for biostatisticians as issues to balance in planning a survival analysis. However, this complexity has created a need for new analyses and procedures to help the planning process for survival analysis trials.
The wider move from fixed to flexible designs has opened up opportunities for advanced methods such as adaptive design and Bayesian analysis to help deal with the unique complications of planning for survival data but these methods have their own complications that need to be explored too.
This document discusses important considerations for determining appropriate sample sizes, including ensuring samples are representative of the overall population and large enough to show real effects. It provides guidelines for minimum sample sizes for different population sizes to achieve 95% confidence with a 5% margin of error. Additional factors that influence sample size are the statistical tests to be used, effect size, and desired level of statistical power or confidence. Stratified random sampling is recommended to obtain a representative sample.
This document discusses key concepts related to sample and sample size in research. It defines a population as the entire group being studied, while a sample is a subset of the population. The size of the sample is represented by n and should be large enough to accurately represent the population within a desired level of confidence and margin of error. The document provides formulas for calculating sample size based on factors like population variance, desired confidence level, and acceptable margin of error. An optimal sample size allows for appropriate analysis while minimizing sampling error.
This document discusses determining the appropriate size of a sample from a population. It explains that a sample should be small enough to be practical but large enough to accurately represent the population within a desired level of precision and confidence level. The key factors in determining sample size are the homogeneity of the population, number of classes or groups in the population, nature of the study, type of sampling method, desired accuracy and confidence level, availability of resources, and other considerations like the nature and size of units, size of the population, and time constraints. The document concludes by mentioning an approach to determining sample size based on precision rate and confidence level.
Minimizing Risk In Phase II and III Sample Size CalculationnQuery
[ Watch Webinar: http://bit.ly/2thIgmi ]. In this free webinar, Head of Statistics at Statsols, Ronan Fitzpatrick, addresses the issues of reducing risk in Phase II/III sample size calculations. Topics covered will include:
Sample Size Determination For Different Trial Designs
Bayesian Sample Size Determination
Sample Size For Survival Analysis
& more
This document discusses sample size calculations for clinical trials. It explains that statistical methods can be used to determine the minimum number of patients needed to meet a trial's objectives with a given statistical power, while also considering practical and ethical factors. The document then provides more details on the statistical approaches, including discussing the general formula for sample size calculation and examples of calculating sample sizes for t-tests, survival analyses, and case-control studies. Key inputs for these calculations are described.
5 essential steps for sample size determination in clinical trials slidesharenQuery
In this free webinar hosted by nQuery Researcher & Statistician Eimear Keyes, we map out the 5 essential steps for sample size determination in clinical trials. At each step, Eimear will highlight the important function it plays and how to avoid the errors that will negatively impact your sample size determination and therefore your study.
Watch the Video: https://www.statsols.com/webinar/the-5-essential-steps-for-sample-size-determination
Non-inferiority and Equivalence Study design considerations and sample sizenQuery
About the webinar
This webinar examines the role of non-inferiority and equivalence in study design
In this free webinar, you will learn about:
-Regulatory information on this type of study design
-Considerations for study design and your sample size
-Practical worked examples of
--Non-inferiority Testing
--Equivalence Testing
Duration - 60 minutes
Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols
Watch the video at: https://www.statsols.com/webinars
This document provides an overview of key statistical concepts for medical research including:
- Common measures like mean, standard deviation, confidence intervals, and p-values.
- Study designs such as randomized controlled trials.
- Tests for comparing groups like t-tests, ANOVA, and chi-square tests.
- Measures of disease frequency and test accuracy like sensitivity and specificity.
- The importance of understanding statistics for medical research and exams.
- Examples of choosing the appropriate statistical tests based on the study design and variables.
In 3 sentences or less, it orients the reader to fundamental epidemiological and biostatistical concepts for medical research and exam preparation.
This document summarizes some key statistical considerations for clinical trials comparing a similar biotherapeutic product (SBP) to a reference biotherapeutic product (RBP). It discusses that equivalence or non-inferiority trials are generally acceptable designs, with equivalence preferred. It notes that determining the appropriate design, equivalence/non-inferiority margins, sample size calculations, and ensuring assay sensitivity and the constancy assumption are all important statistical principles that must be carefully considered in order to draw valid conclusions about the clinical similarity of an SBP to an RBP.
This document discusses sample size estimation and informed consent for research studies. It explains the importance of calculating an appropriate sample size to avoid interpreting studies with inadequate samples or wasting resources. The key factors for determining sample size are outlined, including the type of study, primary outcome, acceptable margin of error, and desired power. The document also covers sampling methods, types of errors, effect sizes, and adjusting sample sizes. It emphasizes the need for informed consent and outlines the necessary elements and information to include in participant information sheets to properly consent participants.
This document discusses factors to consider when determining sample size for research, including the size of the population, available resources, desired accuracy or precision, homogeneity of the population, nature of the study, sampling method, and nature of respondents. It provides formulas for calculating sample size based on population standard deviation, difference between population and sample means, standard error of the mean, and standard error of proportion. An example shows how to calculate a sample size of 60 when the population standard deviation is 6, desired precision is 99%, and difference between population and sample means is 2.
This document provides information and examples on calculating sample size for clinical studies. It discusses key factors that affect sample size calculation, including minimum important difference, standard deviation, power, type I and II errors, study design, dropout rate, and compliance. It provides step-by-step worked examples of calculating sample size for various hypothetical clinical studies. The document emphasizes that sample size calculation is important to ensure studies are adequately powered and conclusions are valid.
Webinar slides- alternatives to the p-value and power nQuery
What are the alternatives to the p-value & power? What is the next step for sample size determination? We will explore these issues in this free webinar presented by nQuery
Sample size calculations are an important step in planning epidemiological studies. An adequate sample size is needed to ensure reliable results, while samples that are too large or small can lead to wasted resources or inaccurate findings. Different study designs require different sample size calculation methods. Factors considered include the desired precision or confidence level, population parameters, and variability. Several formulas and online calculators exist to determine appropriate sample sizes for estimating means, proportions, and comparing groups in studies like clinical trials, surveys, case-control studies, and experiments. Larger effects, more samples, less variability, and higher significance levels can increase a test's statistical power.
1. The document discusses sample size calculation for various study designs including cross-sectional studies, case-control studies, and clinical trials. It provides the key formulas and parameters involved in sample size calculation including confidence level, precision, power, and Z-values.
2. Several examples are provided to demonstrate how to calculate the minimum required sample size given information about the study design, variables, expected outcomes, confidence level, and power.
3. Key factors that determine sample size are confidence level, precision or minimum detectable difference, power, and values of outcomes in pilot or previous studies. The appropriate formulas are selected based on the study design and scale of measurement of variables.
The document discusses key principles of sampling size for estimating values from samples. It states that the information provided by a sample increases with sample size, and samples that are larger are more representative of the overall population. However, larger samples also require more time and resources. The document then provides guidelines for determining appropriate sample sizes for estimating averages, variances, and fitting lines to data based on desired confidence levels and estimates of standard deviations.
Extending A Trial’s Design Case Studies Of Dealing With Study Design IssuesnQuery
This document discusses several case studies of dealing with complex study design issues in clinical trials, including non-proportional hazards, cluster randomization, and three-armed trials. The agenda outlines topics on non-proportional hazards modeling and sample size considerations, cluster randomized and stepped-wedge designs, and methods for analyzing data from three-armed trials that include experimental, reference, and placebo groups. Worked examples are provided to illustrate sample size calculations and statistical approaches for each of these complex trial design scenarios.
This document discusses key concepts related to determining sample size for surveys:
- Confidence interval and confidence level describe the level of certainty or precision in a sample - a 95% confidence level means the true population value would fall within the confidence interval 95% of the time.
- Sample size, population size, and response distribution (how answers are split) all impact the required sample size to achieve a given confidence level and interval. Higher confidence or lower intervals require larger samples.
- For a population of 20,000, with a 50-50 response split, and 95% confidence level, the required sample size is 377 people.
Innovative Sample Size Methods For Clinical Trials nQuery
"Innovative Sample Size Methods for Clinical Trials" is hosted to coincide with the Spring 2018 update to nQuery - The leading Sample Size Software.
Hosted by Ronan Fitzpatrick - Head of Statistics and nQuery Lead Researcher at Statsols - you'll learn about the benefits of a range of procedures and how you can implement them into your work:
1) Dose-escalation with the Bayesian Continual Reassessment Method
CRM is a growing alternative to the 3+3 method for Phase I trials finding the Maximum Tolerated Dose (MTD).
See how researchers can overcome 3+3 drawbacks to easily find the required sample size for this beneficial alternative for finding the MTD.
2) Bayesian Assurance with Survival Example
This Bayesian alternative to power has experienced a rapid rise in interest and application from researchers.
See how Assurance is being used by researchers to discover the true “probability of success” of a trial.
3) Mendelian Randomization
Mendelian randomization (MR) is a method that allows testing of a causal effect from observational data in the presence of confounding factors.
However, in order to design efficient Mendelian randomization studies, it is essential to calculate the appropriate sample sizes required. We demonstrate what to do to achieve this.
4) Negative Binomial Distribution
Negative binomial model has been increasingly used to model the count data. One of the challenges of applying negative binomial model in clinical trial design is the sample size estimation.
We demonstrate how best to determine the appropriate sample size in the presence of challenges such as unequal follow-up or dispersion.
Biostatistics_Unit_II_Research Methodology & Biostatistics_M. Pharm (Pharmace...RAHUL PAL
This document provides an overview of biostatistics topics including parametric and non-parametric statistical tests, sample size calculation, and factors influencing sample size. It discusses commonly used parametric tests like the t-test, ANOVA, correlation coefficient, and regression analysis. Non-parametric tests like the Wilcoxon rank-sum test are also covered. The importance of considering sample size, factors that can impact it, and how dropouts are handled are summarized as well.
This document discusses different methods of randomization used in clinical trials. It defines randomization as assigning participants equally to treatment groups to provide unbiased estimates. The key methods discussed are:
1. Simple randomization, which uses chance like coin flips but can lead to imbalance in small trials.
2. Permuted block randomization, which uses blocks of predetermined assignments to balance groups over time.
3. Stratified randomization, which balances covariates between groups by assigning participants to blocks based on covariate levels before random assignment.
4. Covariant adaptive randomization, which sequentially assigns participants by taking into account covariates and previous assignments to balance groups.
The document discusses research methods. It distinguishes between double sampling and multiphase sampling, noting that double sampling involves taking two samples to make acceptance or rejection decisions, while multiphase sampling collects some data from the full sample and more detailed data from a subsample. The document also defines replicated or interpenetrating sampling as involving multiple samples from different batches or processes to account for variation.
Here are short notes on median and standard deviation:
[a. Median]
The median is the middle value in a data set arranged in numerical order. To find the median, the
values must be listed in numerical order from smallest to largest, and the median is the middle
value. If there is an even number of values, the median is the average of the two middle values.
The median is not affected by extremely large or small values (outliers) and provides a measure
of central tendency that is more representative for skewed data sets compared to the mean.
[b. Standard Deviation]
The standard deviation is a measure of how spread out the values are in a data set
This document discusses experimental research methodology, specifically focusing on variance, sources of error, and control techniques. It defines variance as a measure of dispersion among scores and explains how research design aims to maximize systematic variance from the experimental manipulation while controlling extraneous and minimizing error variance. Extraneous variance comes from irrelevant variables and can be controlled through randomization, elimination, matching, or statistical control. Error variance results from uncontrollable individual differences among subjects and errors of measurement. The goal is for research design to provide valid answers to research questions in an accurate and cost-effective manner.
How Randomized Controlled Trials are Used in Meta-Analysis Pubrica
Randomized Controlled Trials (RCTs) are a commonly used research design in medical and scientific studies to assess the effectiveness of interventions or treatments. Meta-analysis, on the other hand, is a statistical technique used to combine and analyze the results of multiple studies on a particular topic to draw more robust conclusions.
Continue reading @ https://pubrica.com/academy/meta-analysis/how-randomized-controlled-trials-are-used-in-meta-analysis/
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5. researchers ask participants to write down all their contacts and then randomly pick one, then every
connection regardless of closeness will have equal chance to be selected. Therefore, for mod.1 the transition
matrix will become:
(x, ) 1, if W(x, ) 0W˜ y = y >
(x, ) 0, if W(x, ) 0W˜ y = y =
.(x, ) K y = W (x, y)˜
(x,y)∑
5000
y = 1
W˜
Population Design for mod.2
Mod.2 requires a knowing covariate that is correlated with Infection function , where (x) h (x)f x
represent any individual in the population. In this paper, two subgroups are deliberately design with
subgroup A having a distinguishably higher infection rate than subgroup B. In this context, mod.2 means
that researchers will increase the likelihood of recruit someone from group A.
A population size of 5000 was then created, with a portion of subgroup A and the rest being
subgroup B. Every individual in the network is connected to each other with some probability and for
simplicity the connection will be represented as 1 and no connection will be represented as 0.
(x, ) 1, if x, y are connectedW y =
(x, ) 0, if x, y are not connectedW y =
It is important to realize that infected people are usually 4 5 times more than others to be
connected , and people in the same subgroup are more likely to be connected as well. In order to reflect
different connectivity among four types of members in population (Infected A, Non Infected A, Infected B,
NonInfected B), we need to control 10 parameter of the probability of connection between these four
types.
(W(x, ) 1) , if x nfect A, y ealthy A or x ealthy A, y nfect A p y = = pAa ⊆ I ⊆ H ⊆ H ⊆ I
Same for the rest 9 cases.
The 10 parameter should be tuned so that the heatmap of the network will have strongest heat in 5,
10 and some heat in 1, 8, 4, and not so much heat in the rest map in the left graph below. The right graph is
the actual results from the chosen parameter in this experiment. After having the proper chosen parameter, I
only modify the population connectivity by multiplying all 10 parameters by a same quantity, which means
relative ratio of each parameter stays the same.
7. 3. Result
Both of the modification is compared to the normal RDS (control) in two aspects: comparison
between meansquareerror of the estimate and its sensitivity to seed. Meansquareerror is apˆ
measurement to understand the fluctuation from to the true p, and thus the smaller the meansquareerrorpˆ
is, the better .Seed refers to the first participant in the RDS sample. In most practise, the seed is notpˆ
chosen randomly, and therefore the less sensitive is to the seed, the more stable the result will be.pˆ
The results shows that both mod.1 and mod.2 have smaller meansquareerror than control
but neither have less sensitivity to seed.
Results for modification 1
● Meansquareerror
Graph 1 and Graph 2 display that mod.1 meansquareerror is smaller than that of the control, throughout
different population infection rate or different connectivity across group A and group B.
Graph 1: by applying mod.1, the meansquareerror could be improved by roughly 16% to 28%.
Graph 2: by applying mod.1, the meansquareerror could be improved by roughly 19% to 35%.
8.
● Sensitivity to seed
The sensitivity of to seed is measured by the standard deviation of a massive quantity (1000) of pˆ pˆ
generated from rather small sample size (200). It turns out that the standard deviation of mod.1 is smaller
than the control, indicating less sensitivity.
However, it turns out that the difference is not significant in practical senses. As mentioned by
Heckathorn(1997), as the recruiting goes on, the demographic distribution of the sample will converge
towards the distribution in the population and thus stabilize. Therefore, the practical benefit of less
sensitivity to seed, meaning less mixing time of the Markov Chain process, is to reduce cost by having less
sample size yet achieving the same estimation (see Graph 3).