To be able to compare death rates, you need deed to standardise death rates, to reflect differences in age distribution between countriesFor example the median age in Sudan is 19 years and the median age in England is nearly 41 years. Clearly simply comparing crude mortality rates without standardisation will not give you a fair … Continue reading Mortality Rates and Standardisation

# Author: ClinicalOncologySpR

# Statistical tests and comparing variables

Decision on which statistical test to use for hypothesis testing depends on:Type of data (continuous or categorical)Whether the groups are independent or pairedWhether the data is normally distributed or notNumber of groups CONTINUOUS OUTCOMES Paired groups:Repeated data on the same groups of patients e.g. before & after intervention CATEGORICAL OUTCOMES E.g. percentages or proportionsWill usually … Continue reading Statistical tests and comparing variables

# Statistics in Clinical Trials/Metanalysis

Sample size calculations Sample size calculations need to be done before data collectionSample size calculations are done in a statistics packageFactors that influence sample size calculation:Expected effect size - if expected effect size is ↑, can have ↓ sample size to detect differenceExpected variability (SD) of data - look at previous trials for approximationSignificance level … Continue reading Statistics in Clinical Trials/Metanalysis

# Observational Studies/Epidemiology

Observational studies include: cohort; case-control; and cross sectional studiesThey are non-randomised & non-experimental Cohort studyProspective Used to determine aetiology & natural history of diseaseDefine cohort --> assess risk factors to follow --> follow forward in timeUsed to calculate risk ratioRisk ratio indicates increased/decreased risk of disease associated with factor of interest AdvantagesDisadvantagesTime sequence can be reviewedCostly Provides … Continue reading Observational Studies/Epidemiology

# Clinical Trials/Ethics

Clinical Trials Interventional/experimental trialsUsually include randomisationThey are prospective (individuals are followed forward from some point in time)Causal analysis – test causal relationship between exposure of interest & outcome Phases of clinical trials Translational pathway of clinical trials: Pre-clinical trials:Before testing in humansHelps decide whether the drug is ready for clinical trials (from ‘bench to bedside’)Look … Continue reading Clinical Trials/Ethics

# Survival analysis

Time to event (TTE) dataAnalysis of data from a point in time until a particular event. In many oncological studies event is death or event is disease progression.It is not normally distributedIt is continuous data Survival data/analysis is a type of TTE data/analysis Data may often be censoredRight censored – patients who have not reached … Continue reading Survival analysis

# Statistical Inference

Statistical inference is the process of hypothesis testing and using data to make conclusions about characteristics of populations. Hypothesis testing Null hypothesis (H0) – assumes no effect in the populationAlternative hypothesis (H1) – if the null hypothesis is not true Alternative hypotheses can be:One sided – state the direction of effectTwo sided – do not … Continue reading Statistical Inference

# Bias and Confounding

Bias Occurs when there is a systematic difference between the results obtained from a study and the true population parameter – it can over- or under- estimate the true effect and thus create an incorrect association or conceal a real oneBias can occur at any stage of the research process from planning to publicationTypes of … Continue reading Bias and Confounding

# Types of Data and summarising Data

Types of Data Types of data include:Non-numerical/qualitative/categoricalWhen a variable/observation can only belong to a distinct categoryIncludes nominal & ordinal NominalUnordered & mutually exclusive categories. Not possible to rank Examples – alive/dead, blood groupOrdinalOrdered & mutually exclusive categories. Can be ranked. Difference or ‘gap’ between values can be ill-defined, Examples – mild/moderate/severe Numerical/quantitativeWhen a variable/observation takes … Continue reading Types of Data and summarising Data

# Normal and non-normal distributions

Normal/gaussian distributionsThe mean is the peak of curve – it is symmetrical around its meanThe standard deviation determines width of curve (the larger the SD, the wider the curve)Normal distribution tends towards infinity (i.e. the line never reaches the axis)Reference range for a sample = mean +/- 2 standard deviation Non-normal distribution/skewness: NEGATIVE SKEW or … Continue reading Normal and non-normal distributions