HR = 1 means equal efficacy of the experimental (E) and control (C) treatments. If the experimental treatment is better than the control, then the HR (E versus C) <1. If the experimental treatment is worse than the control, then the HR (E versus C) >1.
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What is biostatistical statistics?
Introduction Statistics is basically a way of thinking about data that are variable. This article deals with basic biostatistical concepts and their application to enable postgraduate medical and allied science students to analyze and interpret their study data and to critically interpret published literature.
What are the steps involved in biostatistics?
Biostatistics mainly consists of various steps like generation of hypothesis, collection of data, and application of statistical analysis. To begin with, readers should know about the data obtained during the experiment, its distribution, and its analysis to draw a valid conclusion from the experiment.
What data should postgraduate students be aware of in biostatistics?
The postgraduate students should be aware of different types of data, measures of central tendencies, and different tests commonly used in biostatistics, so that they would be able to apply these tests and analyze the data themselves.
How do you calculate the overall treatment effect?
The sum of the regression coefficient for the treatment variable and the regression coefficient for the interaction between the treatment variable and time then reflects the overall treatment effect.
How do you calculate treatment effect?
When a trial uses a continuous measure, such as blood pressure, the treatment effect is often calculated by measuring the difference in mean improvement in blood pressure between groups.
How do you measure the efficacy of a treatment?
The randomized controlled trial (RCT) is the most reliable methodology for assessing the efficacy of treatments in medicine. In such a trial a defined group of study patients is assigned to either receive the treatment or not, or to receive different doses of the treatment, through a formal process of randomization.
How is treatment effect size determined?
The best estimate of the treatment's effect is simply the difference in the means (or, in some trials, the medians) of the treatment and control groups.
How do you calculate needed for treatment?
CalculationThe number needed to treat is the inverse of the absolute risk reduction (ARR).The ARR is the absolute difference in the rates of events between a given activity or treatment relative to a control activity or treatment, ie control event rate (CER) minus the experimental event rate (EER), or ARR = CER - EER.More items...•
How efficacy is calculated?
This is calculated by comparing the number of cases of disease in the vaccinated group versus the placebo group. An efficacy of 80% does not mean that 20% of the vaccinated group will become ill.
Is effectiveness the same as efficacy?
Efficacy is the degree to which a vaccine prevents disease, and possibly also transmission, under ideal and controlled circumstances – comparing a vaccinated group with a placebo group. Effectiveness meanwhile refers to how well it performs in the real world.
How do you calculate the effect size?
Generally, effect size is calculated by taking the difference between the two groups (e.g., the mean of treatment group minus the mean of the control group) and dividing it by the standard deviation of one of the groups.
What is treatment effect statistics?
Treatment effects can be estimated using social experiments, regression models, matching estimators, and instrumental variables. A 'treatment effect' is the average causal effect of a binary (0–1) variable on an outcome variable of scientific or policy interest.
Why do we calculate effect size?
Effect size tells you how meaningful the relationship between variables or the difference between groups is. It indicates the practical significance of a research outcome. A large effect size means that a research finding has practical significance, while a small effect size indicates limited practical applications.
How do you calculate number needed to treat and harm?
Number need to harm is calculated in the same way as number needed to treat: divide 1 by the absolute risk increase. References: IBIS investigators. First results from the International Breast Cancer Intervention Study (IBIS-I): a randomised prevention trial.
How do you calculate RR from NNT?
The RR = (8/1000) / (10/1000) = 0.8 making the RRR = (1-0.8/1)=0.2 or 20%. Although this sounds impressive, the absolute risk reduction is only 0.01-0.008=. 002 or 0.2%. Thus the NNT is 1/0.002=500 patients....Toolkit.YesNoNot Exposed109901 more row
Why is biostatistics important?
It is important in calculating the sample size, determining the power of a study, and assessing the statistical significance of the results. The p-value is very commonly reported as an outcome of statistical hypothesis tests.
What is statistically significant?
It is important to remember that a statistically significant result was initially meant to be an indication that an experiment was worth repeating. If the replication studies also yielded statistically significant results, then the association is unlikely due to mere chance.
What does p-value mean in statistics?
Though p-value is an important statistical indicator, it has often been misinterpreted. For instance, if p-value is 0.05, that does not mean that the finding is clinically relevant. A finding will have clinical significance only if the endpoint being studied actually has an impact on how a patient would be treated or diagnosed. Sometimes, it is impossible to directly study the variable that is relevant, so another variable or trait has to be used. A result involving this secondary trait may be of limited clinical value. When the finding is statistically significant, but the effect size is small, this may not have a large impact on the problem that you are studying.
How does power affect sample size?
Power has an impact on sample size. Usually, studies are designed to have at least 80% power. This means that you will have an 80% chance of detecting an association if one exists. This will result in a smaller sample size than the one required for 95% power, implying higher the power, larger the sample size.
What is the first step in designing a clinical study?
One of the early steps in designing a clinical study is determining the sample size. If this calculation is not done correctly, it may result in quantitative research that is unable to detect the true relationship between the predictor and outcome variables. The sample should be of appropriate size and representative of the population being studied.
Why is adherence to treatment important?
Optimal treatment adherence is essential for the management of chronic conditions and the effectiveness of prescribed therapies. A World Health Organisation (WHO) report underlines the fact that adherence to chronic treatments is as low as 50% [1].
What is treatment adherence?
The current definition of treatment adherence, as given by the WHO, is “the extent to which a person’s behavior- taking medication, following a diet, and/or executing lifestyle changes- corresponds with the agreed recommendations from a healthcare provider” [1].
Is the adherence method supplementary data?
The method is appropriate for the measurement of adherence to one drug therapy regimen only and it offers no supplementary data on the additional causes of non-adherence and does not report on any patterns of non-adherence. It is also quite expensive, and could be viewed as interventional by some patients.
Why do policy makers want to know how common a condition or disease is?
It allows them to plan and budget for treatment facilities, supplies of medication, rehabilitation personnel. There are two broad answers to the question, “How common is condition X?” and, interestingly, both of these use the exact same SAS procedures.
How many variables does the transpose step have?
The TRANSPOSE step above transposes the data set by age group so that each age group has four variables and outputs the results to a dataset named ‘transf’. The dataset created is shown below.
Abstract
Hazard ratios (HRs) are used commonly to report results from randomized clinical trials in oncology. However, they remain one of the most perplexing concepts for clinicians. A good understanding of HRs is needed to effectively interpret the medical literature to make important treatment decisions.
WHAT IS A HAZARD RATIO?
Hazard ratios are frequently used to estimate the treatment effect for time-to-event end points, such as overall survival (OS) and progression-free survival (PFS), in oncology randomized clinical trials (RCTs).
WHY ARE HAZARD RATIOS USEFUL?
The log-rank and Wilcoxon tests are commonly used to compare the entire survival data over the duration of the trial, between treatment arms; they do not compare the medians or time point estimates.
WHAT ARE THE LIMITATIONS?
Correct interpretation of a HR is based on the assumption that the ratio of the hazard rates at each time interval is approximately constant during the study. This is also known as the “Proportional Hazards” (PH) assumption.
HOW TO INTERPRET A HAZARD RATIO
As discussed earlier, a simplistic interpretation is that if the HR (E versus C) is <1, then the experimental treatment is better than the control and vice versa if HR (E versus C) >1. The following examples illustrate more detailed explanations and common pitfalls.
SUMMARY
The Cox proportional hazards model is used to analyze survival data. It provides a HR to assess the relative efficacy of the experimental treatment compared with the control treatment over the duration of the RCT.
ACKNOWLEDGMENTS
The authors thank Nancy Iturria for generating simulated survival data to create the survival curve figures and Jonathon Denne and Mauro Orlando for critically reviewing the article and providing helpful comments.
Attributable proportion
The attributable proportion, also known as the attributable risk percent, is a measure of the public health impact of a causative factor. The calculation of this measure assumes that the occurrence of disease in the unexposed group represents the baseline or expected risk for that disease.
Vaccine efficacy or vaccine effectiveness
Vaccine efficacy and vaccine effectiveness measure the proportionate reduction in cases among vaccinated persons. Vaccine efficacy is used when a study is carried out under ideal conditions, for example, during a clinical trial.