Applying Evidence in the Clinical Setting Archives

Statistical Correlation Tests, Bias, Risk & More

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Statistical Correlation Tests

Statistical correlation tests are performed with the aim of finding out whether a relationship exists between variables, and then determining the magnitude and action of that relationship. In other words, correlation is a statistical measure that expresses the extent to which two variables are linearly related. It’s commonly used for describing simple relationships without making a statement about cause and effect.

If two variables tend to move up or down together, they are considered to be positively correlated. However, if they tend to move in opposite directions, they are considered to be negatively correlated.

Parametric tests are based on an assumption that the data being analyzed follows a normal distribution. The more data points a distribution has, the more it can approach a normal distribution. Lack of data points would require the use of non-parametric tests.

Non-parametric tests a.k.a. distribution-free tests are methods of statistical analysis that do not require a distribution to meet the required assumptions to be analyzed, especially if the data is not normally distributed.

Student T-Test

statistical correlation tests — Retrieved from https://researchbasics.education.uconn.edu/t-test/ on 11th March 2023

UNPAIRED T-TEST – testing two means

The unpaired t-test a.k.a. independent t-test is a statistical test which aims to determine whether there is a difference between two unrelated groups. The unpaired t-test is used to make a statement about the population based on two independent samples. To make this statement, the mean value of the two samples is compared.

ANOVA – testing more than two means

ANOVA a.k.a. Analysis of Variance, is a statistical test used to investigate the difference between the means of more than two groups. A one-way ANOVA uses one independent variable, whereas a two-way ANOVA uses two independent variables.

CHI-SQUARED TEST – testing the association between two categorical variables

A chi-squared test is a statistical test that is used to compare observed and expected results. The main aim of the chi-squared test is to identify whether a disparity between actual and predicted data is due to chance or to a link between the variables under consideration.

Choosing the Right Test

Bias Potential Sources

Bias is the systematic deviation from the truth. Different sources of bias may include:

selection or sampling bias – inadequate selection of study participants usually due to high non-response rate, inadequate follow-up, inadequate sampling method use, or inadequate controls or comparisons groups
confounding bias – existing differences between comparison groups in one or more parameters which may be directly associated with the outcome and the candidate risk factor in question
instrumental bias – faulty measurement instrumentation due to lack of calibration, inaccurate diagnostic tests, etc
information bias – systematically incorrect measurements or responses, or from differential misclassification of disease or exposure status of participants eg. due to questionnaire ambiguity or insensitivity
systematic bias – one observer may underestimate readings, leading to his respondents having lower readings than those observed by someone else
respondent bias – misunderstandings, lack of interest, or recall issues in the unaffected group
random bias – observer may underestimate or overestimate measurements, which mistakes tend to even out on averaging

Calculating Statistical Risk

In statistics, the risk for a particular group to develop a disease refers to the rate of disease in the group concerned.

RISK DIFFERENCE / ABSOLUTE RISK – the excess risk that exposed individuals have.

RISK RATIO – the measurement of the risk in the exposed group as a multiple of the risk in the unexposed group.

ODDS RATIO – odds refer to the chance of developing the disease rather than not developing the disease. Odds Ratio refers to the chances of developing the condition for an exposed individual relative to an unexposed individual.

The Relevance of Testing the Sensitivity & Specificity of a Screening Diagnostic Test

Screening programs need to provide diagnostic tests with the least disturbance possible for the individual, yet with enough sensitivity and specificity to detect the disease in question. Assessing the sensitivity and specificity of a test requires its outcome to be compared against a gold standard eg. comparing the Faecal Occult Blood Test sensitivity and specificity to a colonoscopy, in this case considered to be the gold standard.

Multi-Variate Analysis

Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable, while the variable you are using to predict the other variable’s value is called the independent variable.

Logistic regression is a statistical analysis method to predict a binary outcome eg. yes or no, based on prior observations of a data set. A logistic regression model predicts a dependent data variable by analyzing the relationship between one or more existing independent variables. This technique results in an odds ratio rather than a rate of change per unit change in the independent variable.

Poisson Regression models are best used for modeling events where the outcomes are counts. Or, more specifically, count data: discrete data with non-negative integer values that count something, like the number of times an event occurs during a given time-frame or the number of people in line at the grocery store. This technique results in a risk ratio instead of a rate of change per unit change in the independent variable.

Example of Poisson Regression – Retrieved from https://sherrytowers.com/2018/03/06/poisson-regression/ on 15th March 2023

Survival Analysis is concerned with studying the time between entry to a study and a subsequent event. Originally the analysis was concerned with time from treatment until death, hence the name, but survival analysis is applicable to many areas as well as mortality.

Example of Survival Analysis – Retrieved from https://www.graphpad.com/guides/survival-analysis on 15th March 2023

Relative Survival is defined as the ratio of the proportion of observed survivors in a cohort of cancer patients to the proportion of expected survivors in a comparable set of cancer free individuals. The formulation is based on the assumption of independent competing causes of death.

Example of Relative Survival Analysis – Retrieved from https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0202044 on 15th March 2023

Meta-analysis is a research process used to systematically synthesise or merge the findings of single, independent studies, using statistical methods to calculate an overall or ‘absolute’ effect. Meta-analysis does not simply pool data from smaller studies to achieve a larger sample size.

Example of Meta-Analysis – Retrieved from https://www.mdpi.com/2624-8611/4/4/49 on 15th March 2023

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Statistical Considerations Central Tendency Null Hypothesis & More

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Statistical considerations for research should include careful statistical planning and use of the right statistical tests for data analysis to ensure a successful research project.

Central Tendency – Mean, Median & Mode

Central Tendency

The phrase ‘Central Tendency’ refers to a single value which aims to describe a set of data through the identification of the central position within the same set of data. The mean, which at times is referred to as the average, is most commonly considered to be the measure of central tendency, however, there are also the median and the mode which can be considered as measures of central tendency. Which measure is valid depends on the conditions under which they are being evaluated.

medical statistics — Retrieved from https://danielmiessler.com/blog/difference-median-mean/ on 23rd November 2022

The Mean

The mean is the average, where all values are added together and then divided by the number of values.

The Median

The median is the middle value found within the list of values. To find the median you need to list all values in numerical order from smallest to largest, and then identify the value within the middle.

The Mode

The mode is the value occurring most often. This means that if in a particular list of values no number is repeated, there would be no mode for that particular list.

The Variance & Standard Deviation

The variance is a calculation of the normal distribution spread in a set of variables, in other words, a measure of dispersion. The standard deviation is the square root of its variance.

Hypothesis Testing Statistical Considerations

Define the Null Hypothesis – no difference between the groups being compared
Define an Alternative Hypothesis – existing difference between the groups being compared; defined difference should be clinically significant
Calculate a p value – the probability of obtaining the results observed if the null hypothesis is true
Based on the p value, accept or reject the Null Hypothesis
If the Null Hypothesis is rejected, accept the Alternative Hypothesis

NOTE: the size of an expected difference (priori) should be defined prior to the data collection period.

The Null Hypothesis

Studies always start out with the assumption that the difference between the groups being compared will be non-existent a.k.a. null, hence why this is called the Null Hypothesis. Studies aim to have enough evidence to accept or reject this null hypothesis.

Unfortunately, errors may be made in accepting or rejecting the null hypothesis. To prevent such errors, the researcher should aim to have a sample size which is large enough.

The Confidence Interval & P-Value

The phrase confidence interval refers to the range of values which a specific statistic, most commonly being a mean or proportion of the population, can have in the reference population with a specific probability. Confidence intervals help in clinical trial data interpretation by determining upper and lower bounds on the likely size of any true effect.

The p-value determines whether trial results could have occurred by chance.

Confidence intervals are usually preferred to p-values since they provide a range of possible effect sizes in relation to the data, whilst p-values provide a cut-off beyond which we assert that the findings are statistically significant.

A confidence interval which embraces the value of no difference between treatments shows that treatment being investigated is not significantly different from the control.

The cut-off point for rejecting the null hypothesis is arbitrary, a typically being equivalent to 0.05

If p = 0.01, the chance of obtaining the same results as the experiment is 1%, meaning that it is very unlikely, thus we reject the null hypothesis.

If p = 0.7, then the chance of obtaining the same results as the experiment is 70%, thus, we accept the null hypothesis.

NOTE: bias must be assessed before confidence intervals are interpreted, since biased studies can be misleading even when very large samples and very narrow confidence intervals were involved.

(Davies and Crombie, 2003)

Errors & Power Statistical Considerations

Type 1 (Alpha) & Type 2 (Beta) Errors in Statistics

Power statistical considerations

Power is determined by sample size, magnitude of difference sought, and by the arbitrary. For example, a pilot study with a small sample size would have low power. Power desired is usually 0.80

Reference

Davies, H.T.O. & Crombie, I.K. (2003). What are confidence intervals and p-values? What is…? Series. Edition 2009. Hayward Communications Ltd. Hayward Group Ltd. Retrieved from http://www.bandolier.org.uk/painres/download/whatis/What_are_Conf_Inter.pdf on 12th February 2023

Kirkwood, Betty R. (2003). essential medical statistics. Blackwell Science, Inc., 350 Main Street, Malden, Massachusetts 02148–5020, USA: Blackwell. ISBN978-0-86542-871-3.

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Case Control Studies Critical Appraisal

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Case Control Studies are typically observational studies commonly used to outline factors related with certain diseases or outcomes. Selection of participants is done on the basis of an experienced outcome. However, to introduce the control aspect within the study, other participants are selected at random from the population without having experienced that outcome. In both the cases and controls participants, exposure is assessed retrospectively through medical records and interviews.

Retrieved from https://www.pinterest.com/pin/408912841140782725/ on 26th February 2023

Hierarchy of Evidence

Case Control Studies Participant Selection Criteria

CASES

clear inclusion/exclusion criteria to ensure homogeneity
cases should ideally be representative of the cases within the target population for external validity purposes
cases should be sourced from the community, clinic, or hospital
accurate diagnosis is important so as not to dilute the cases group with those who do not actually have the disease in question

CONTROLS

controls should be selected from the same population, and may include individuals at risk of developing the outcome
same inclusion/exclusion criteria but without the outcome should be used, with the emphasis being on comparability of cases and controls
accurate classification of controls should be ensured; if confounders are known, they should be matched through a matched study, otherwise, confounders need to be considered in data analysis, and a bigger sample would be required

Matching

Matching is an attempt to ensure comparability between the cases and controls. Matching reduces variability and systematic differences caused by extraneous variables a.k.a. confounders (such as age, gender and race), which may be related to the risk factor.

Bias

INTERVIEWER BIAS – interviewer asks the leading questions, which are different from those used for the control group.

DATA QUALITY – incomplete or inaccurate data

RECALL BIAS – Participants with the disease (CASES) are more likely to recall and report exposure due to having experienced the outcome

Advantages VS Disadvantages

ADVANTAGES	DISADVANTAGES
ideal when seeking possible causes of rare outcomes and outcomes with long latency	may be difficult to select appropriate controls group
does not require a large group of participants	extraneous variables a.k.a. confounder control may be incomplete
relatively quick since the outcome would have already occurred	difficult to validate information
multiple exposures or risk factors can be examined	susceptible to recall bias
relatively inexpensive

Performing Case-Control Studies

cases are identified
control group individuals with similar characteristics but without the outcome in question are identified
exposure is measured retrospectively in both groups
occurrence rate of exposure in cases is compared to the occurrence rate of exposure in control
results are typically obtained through odds ratios or relative risk: show occurrence in exposed is divided by occurrence in non-exposed; if value is 1 = no difference; if value is >1 = risk is higher in exposed; if value is <1 = risk is higher in non=exposed

Cohort Study VS Case Control Study

Retrieved from https://twitter.com/medicine20102/status/682169574859620352 on 26th February 2023

CASP Tool for Case Control Studies

CASP Tool for case-control studies can be accessed here.

To view blogpost featuring Cochrane videos on all types of studies please click here.

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Cohort Studies Critical Appraisal

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Cohort Studies are observational studies on groups of people with defined characteristics in which outcomes related to particular exposure (or lack thereof) are compared. Cohort Studies are usually indicated in studies where manipulated exposure is considered to be unethical (eg. no group of people should be asked to smoke for the purpose of outcome comparison). Similarly, these are observational studies, thus they lack the opportunity to control or prevent the expected outcome.

Hierarchy of Evidence

Cohort Studies Advantages & Disadvantages

Cohort Studies need to include a control group – a group which is not exposed to the risk factor of interest. Participants are selected based on their exposure status at the start of the study, and exposed and unexposed groups need to be selected from the same population.

Advantages

exposure to the risk factor of interest is measured prior to disease onset, which reduced bias
rare exposures can be examined by appropriate selection of study cohorts
multiple outcomes can be studied for a single type of exposure
calculates incidence and relative risk of disease in both exposed and unexposed participants over time

Disadvantages

changes in the participants’ exposure status and diagnostic criteria that may happen over time can affect the individuals’ classification based on exposure and disease status; the researcher should think about what measures may need to be taken if the participants change their patterns throughout the study period
risk of information bias – outcome may be influenced by information on the participant’s exposure status
loss of follow-ups may introduce attrition bias, where the characteristics of drop-outs and those completing the study may be significantly different, leading to a reduction in the validity of the study
expensive and time consuming

Preventing Loss to Follow Up

During the recruitment process, the researcher should obtain all information required so that the participant can be easily contacted. In addition, the researcher should exclude participants that are likely to be lost (eg. a prospective participant may have plans to move to another country).

During the follow-up period, the researcher should maintain regular contact through different means, and possibly provide tokens or gifts to encourage continued participation.

Prospective VS Retrospective Cohort Studies

In Prospective Cohort Studies, participants are identified at the time of exposure. They are followed up over time until outcome occurs.

Advantages: Prospective Cohort Studies are designed with specific data collection methods.

Disadvantages: Such studies entail a long indefinite follow-up period until an outcome occurs. They are susceptible to loss of follow-up, and are usually expensive.

In Retrospective Cohort Studies, the chosen participants would have already been exposed to and subsequently experienced an outcome. Thus, outcome data measured in the past is then reconstructed for analysis.

Advantages: Retrospective Cohort Studies are cheaper and quicker than prospective studies, and make use of past data, which can be accessed immediately.

Disadvantages: Such studies are susceptible to both recall bias and information bias, and may be subjected to incomplete, inaccurate, or inconsistent data due to limited control over data collection.

Cohort Studies Critical Appraisal

Casp Tool

CASP Tool for Cohort Studies Critical Appraisal can be found here.

To view blogpost featuring Cochrane videos on all types of studies please click here.

Types of Statistical Tests Used in Cohort Studies

Risk Ratio (RR)
Odds Ratio (OR)
Confidence Interval (CI)

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Systematic Reviews and Meta Analysis Critical Appraisal

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A systematic review is a summary of clinical literature which, through explicit reproducible methods, performs a comprehensive and systematic literature search and critical appraisal of individual studies. Systematic reviews may incorporate statistical techniques with which they combine valid and similarly designed studies together to produce an average estimate of effect based on all reviewed studies – this is what we refer to as a meta-analysis.

Hierarchy of Evidence

How To Perform Systematic Reviews & Meta Analysis

Step 1 – develop a question
Step 2 – define the criteria for inclusion of studies
Step 3 – perform a systematic search through available literature to find eligible studies (fitting all PICO elements)
Step 4 – review methods and results of the selected studies
Step 5 – assess heterogeneity (variation) between studies
Step 6 – apply statistical methods to produce a summary result

Critically Appraising a Review

check whether the author/s specified their clinical query
check whether each key element of the question (PICO) is clear and specific
scrutinise the search strategy that was used – were boolean operators used? Was the author/s’ search extensive and comprehensive enough? If not, the study should be revised and retested. The search should include a good keyword combination, good databases, grey literature (so as to prevent publication bias), limiters, and more than one person in the search (prevents bias – sometimes it’s difficult to assess whether or not a study should be used or not, hence 2 or more should be included in this process (if consensus is not met)
check whether the author/s applied any quality criteria in study selection

Publication Bias

In order to avoid publication bias, authors of reviews should look into books, grey literature, and unpublished material eg. conference proceedings, dissertations etc. This is because one should keep in mind that positive and statistically significant results are more likely to be published and be included in scientific journals easily found in electronic databases.

Critically Appraising a Meta Analysis

Heterogeneity testing

test for clinical and statistical heterogeneity
use various tests such as chi square test, and give p value. If p value is large, difference between results is not significant

statistical calculations

use fixed or random effect models
random effect models are used to make up for heterogeneity, usually needing a large sample but providing conservative results
NEED to qualitatively examine the potential cause of heterogeneity prior to deciding to pool results

sensitivity analysis

check on the impact of low quality papers on the overall result
1st analysis requires the exclusion of dubious studies
2nd analysis requires the re-inclusion of dubious studies to see if they affect the overall result

PRISMA Diagram

When researchers choose to perform a systematic review, they are required to present the whole search process within a PRISMA diagram. Any grey literature or manually retrieved literature should also be included in the PRISMA diagram (sample PRISMA flow diagram can be found below…)

PRISMA Flow Diagram – Retrieved from https://esraeurope.org/prospect/procedures/oncological-breast-surgery-2019/evidence-review-process-9/prisma-flow-diagram/ on 22nd February 2023

PRISMA, a.k.a. Preferred Reporting Items for Systematic Reviews and Meta-Analyses, is an evidence-based minimum set of items for reporting in systematic reviews and meta-analyses (sample PRISMA checklist can be found below…)

CASP Tool for Systematic Reviews

CASP tool for systematic reviews can be found here.

To view blogpost featuring Cochrane videos on all types of studies please click here.

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Randomised Controlled Trials Critical Appraisal of RCTs

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Randomised Controlled Trials are quantitative experimental studies in which individuals are allocated to receive one of multiple clinical interventions or a control at random. Interventions that individuals may be subjected to in Randomised Controlled Trials include treatment (or placebo), prevention strategies, screening programs, diagnostic tests, different settings, or educational models.

RCTs are excellent at providing answers to questions seeking the effectiveness of different interventions.

Hierarchy of evidence

Characteristics of Randomised Controlled Trials

include one (or more) treatment arm and a control arm
participants are allocated at random to one of the groups
both the participants and the researchers remain unaware of which intervention was allocated to the groups
each group is treated identically (except the intervention being investigated)
participants are analysed within their assigned group, irrespective of the allocated intervention
analysis aims to estimate the size of the difference in predefined outcomes between the groups

What Makes RCTs Superior To Other Research Methods?

In RCTs, which are experimental studies, exposure is controlled by the researcher. On the other hand, in cohort and case control studies, which are observational studies, the researcher follows the outcomes related to exposures over which they had no control.

Whilst other study designs can point out associations between an intervention and an outcome, they are not able to exclude the effect of an extraneous factor linked to both.

Randomised Controlled Trials ~ Parallel Design vs Crossover Design

There are 2 types of RCTs:

Parallel Design: in this type of RCT, each group of participants is exposed to one of the interventions only; intervention assigned to each group is determined at random
Crossover Design: in this type of RCT, each participant is subjected to all the interventions within the study in successive periods; first intervention allocated is determined at random

Blinding – Single vs Double

In the ideal RCT, both the participants and the clinicians are left unaware of which participants are receiving which intervention. This is what we refer to as blinding. Blinding reduces the risk of ascertainment bias (when some members of the target population are more likely to be included in the sample than others) and observation bias (when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study). An RCT without any type of blinding is referred to as an open trial.

Retrieved from http://regulatoryworld.blogspot.com/2014/08/clinical-trials-at-glance-part-1_31.html on 18th February 2023

Ethical Considerations

Prior to the start of a Randomised Controlled Trial, there needs to be genuine doubt on whether one intervention is superior to another, as well as a balance between any potential risks to the participants and potential benefits to future patients.

Thus, one needs to keep in mind that it is unethical to expose participants to a presumably inferior or potentially harmful intervention. It is also unethical to deprive patients from an intervention which is presumed to be beneficial.

RCTs Limitations

recruitment difficulties
blinding difficulties
costly and time consuming
requires large sample size to detect small intervention effects
not ideal for screening or interventions with rare outcomes or outcomes emerging after long intervals
randomisation may not be possible due to patient preferences or clinicians’ decisions

Critically Appraising Randomised Controlled Trials

critical appraisal of randomised controlled trials depend on reporting accuracy
results tend to emphasise statistical significance instead of clinical importance
small sample size leads to insufficient ability in detecting significant differences
poor randomisation reporting
poor blinding
no follow up

CASP TOOL for RCT

CASP Tool for critically appraising Randomised Controlled Trials can be found here.

To view blogpost featuring Cochrane videos on all types of studies please click here.

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Critical Appraisal of a Research Study

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What are the key concepts of critical appraisal? This blogpost features a very good series of critical appraisal training videos published by Cochrane Mental Health on Youtube (full credits can be found at the bottom of this blogpost).

Hierarchy of Evidence

Critical Appraisal Introduction

In this first video, the key concepts of critical appraisal are introduced, and the learning objectives for the series are clearly defined.

Systematic Reviews and Meta Analysis

In this second video, we are introduced to the fundamentals of systematic reviews and the ways in which we can apply the critical appraisal concepts of validity, trustworthiness of results, and value and relevance using the CASP checklist. The full text version of the study by Hay et al (2019) mentioned in this video can be found here.

Randomised Controlled Trials

In this third video, we are introduced to the fundamentals of randomised controlled trials and the ways in which we can apply the critical appraisal concepts of validity, trustworthiness of results, and value and relevance using the CASP checklist. The full text version of the study by Sugg et al (2018) mentioned in this video can be found here.

Cohort Studies Critical Appraisal

In this fourth video, we are introduced to the fundamentals of cohort studies and the ways in which we can apply the critical appraisal concepts of validity, trustworthiness of results, and value and relevance using the CASP checklist. The full text version of the study by Gerhard et al (2015) can be found here.

Case Control Studies

In this fifth video, we are introduced to the fundamentals of case control studies and the ways in which we can apply the critical appraisal concepts of validity, trustworthiness of results, and value and relevance using the CASP checklist. The full text version of the study by Drucker et al (2018) can be found here.

Cross Sectional Studies

In this sixth video, we are introduced to the fundamentals of cross-sectional control studies and the ways in which we can apply the critical appraisal concepts of validity, trustworthiness of results, and value and relevance. The full text version of the study by Boden et al (2010) can be found here.

Diagnostic Studies Critical Appraisal

In this seventh and last video, we are introduced to the fundamentals of diagnostic studies and the ways in which we can apply the critical appraisal concepts of validity, trustworthiness of results, and value and relevance using the CASP checklist. The full text version of the study by Hollis et al (2018) can be found here.

Reference

The above embedded videos are part of a project which was developed to enhance research use and development across two NHS Trusts.

The project founding partners were:

Cochrane Common Mental Disorders
Northumberland, Tyne and Wear NHS Foundation Trust (NTW), UK
Tees, Esk and Wear Valleys NHS Foundation Trust (TEWV), UK

Delivery of the project was supported by the Centre for Reviews and Dissemination at the University of York.

Funding Acknowledgement: The production of the critical appraisal modules was jointly funded by:

Economic and Social Research Council (ESRC), UK – as part of the University of York ESRC Impact Acceleration Account (ES/M500574/1)
Northumberland, Tyne and Wear NHS Foundation Trust (NTW), UK
Tees, Esk and Wear Valleys NHS Foundation Trust (TEWV), UK
University of York, UK

Cochrane Review Group Funding Acknowledgement: The National Institute for Health Research (NIHR) is the largest single funder of the Cochrane Common Mental Disorders Group.

Disclaimer: the views and opinions expressed herein are those of the module authors and do not necessarily reflect those of the ESRC, NIHR, the National Health Service (NHS), the Department of Health and Social Care or the University of York.

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Cross Sectional Study Critical Appraisal

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A cross sectional study is observational in nature. It involves collection of information about a population at a particular point in time.

A descriptive cross sectional study would assess distribution and frequency eg. measuring the prevalence of cancer amongst a defined population
An analytical cross sectional study would examine the association between variables to identify determining factors related to health eg. examining the association between living a sedentary lifestyle and having hypertension

Hierarchy of evidence

Advantages

affordable – a cross sectional study requires no follow-ups since only one set of data is analysed, making this a low-cost research method
efficient – a cross sectional study is ideal for studying exposures or conditions that are reasonably common, and which require only one-time assessment
no risks – this type of study requires no long-term considerations.
potential completeness – due to easily accessed key data points

Disadvantages

collecting data at one point in time leads to limited causation testing especially where exposure and/or outcome are expected to change over time
needs to be an adequate representation of the population being studied
requires a larger sample size for accuracy basis
bias may affect results if for example incomplete responders are related to a specific group
may result in an association, however such association may not be the reason for the association
unable to measure incidence

Critically Appraising a Cross Sectional Study

When critically appraising a cross sectional study you need to focus on the following:

Sampling
Non-response
Methods used for measuring variables of interest
Controlling for confounders in the analysis

Sampling

note sampling bias – population needs to be clearly identified since final results will be inferred onto the target population
consider choice of sampling frame – how was the sample selected from the actual population? Remember that when it comes to measuring prevalence, the actual population is of utmost importance. Thus, consider sampling procedure used eg. random vs convenience sampling, using inclusion or exclusion criteria etc
consider the procedure used for the selection of participants – was inclusion/exclusion criteria used? And was the sample taken at random or was it convenience sampling?
consider sampling size – ideally, previous studies performed within the same area should be sought so that the occurrence frequency within the sample reflects the occurrence within the target population
consider expected precision of results – rare occurrence and precise results require a bigger sample

Non-Response

respondents may differ from non-respondents – respondents are more likely to be interested in the subject being studied, which may lead to more adherence to suggestions/requirements. Thus, replacing non-respondents to increase the sample size may still not bypass the sampling bias resulting from no response
researchers are required to report the response rate as well as to compare the characteristics of both the respondents and non-respondents

Controlling Confounders

A confounding factor is a third variable in a study which examines a possible cause-and-effect connection. It is related to both the supposed cause and supposed effect of the study. At times it is difficult to separate the true effect of the independent variable from the effect of the confounding variable.

Whilst performing a research study, it is important that potential confounding variables are identified and a plan is drawn so that their impact is reduced.

Example of a Cross Sectional Study

Breast feeding and obesity: cross sectional study (full reference in the references section): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC28161/

Appraisal Tool for Cross Sectional Studies (AXIS)

Cross Sectional Study Appraisal Checklist

Retrieved from https://bmjopen.bmj.com/content/bmjopen/6/12/e011458/DC2/embed/inline-supplementary-material-2.pdf?download=true on 17th December 2022

NOTE: To view blogpost featuring Cochrane videos on all types of studies please click here.

References

BMJ Open (2016). Appraisal Tool for Cross-Sectional Studies (AXIS). Retrieved from https://bmjopen.bmj.com/content/bmjopen/6/12/e011458/DC2/embed/inline-supplementary-material-2.pdf?download=true on 17th December 2022

von Kries, R., Koletzko, B., Sauerwald, T., von Mutius, E., Barnert, D., Grunert, V., & von Voss, H. (1999). Breast feeding and obesity: cross sectional study. BMJ (Clinical research ed.), 319(7203), 147–150. https://doi.org/10.1136/bmj.319.7203.147

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Introduction to Medical Statistics

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Statistics VS Medical Statistics

Statistics are quantities or sets of quantities which one can calculate from observed data. Thus, unless they are ratios, statistics should be reported in units. Medical statistics is a subdiscipline of statistics. Medical statistics can assist researchers in answering healthcare-related challenging questions.

“It is the science of summarizing, collecting, presenting and interpreting data in medical practice, and using them to estimate the magnitude of associations and test hypotheses. It has a central role in medical investigations. It not only provides a way of organizing information on a wider and more formal basis than relying on the exchange of anecdotes and personal experience, but also takes into account the intrinsic variation inherent in most biological processes.”
Kirkwood, 2003.

Population VS Sample

In relation to statistics, the term population refers to a well defined group of subjects that a researcher chooses to investigate about a particular issue. The size of such a population may be known or unknown, but when the study population is too big to be investigated fully, sampling becomes needed.

A sample is a feasible number of subjects chosen to represent a population, thus, the sample involved in the study needs to be as representative as possible to the target population. This can be achieved by:

selecting an adequate sampling population
using randomly selected participants rather than convenience sampling

Simple Random Sampling

Simple random sampling is a sampling method in which all members of a population have an equal chance of being chosen to participate in the study sample.

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Stratified Random Sampling

In stratified random sampling, the population is stratified into defining blocks eg. gender and age.

Weighted Sampling

In weighted random sampling the subjects are weighted and the probability of each item to be selected is determined by its relative weight. This allows the sample to be more representative of the population.

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Cluster Sampling

In cluster sampling, random groups of individuals are recruited for the study sample.

Convenience Sampling a.k.a. Opportunity Sampling

In this type of sampling, no consideration is taken with regards to representation. Thus, all members of a population that a researcher can access have the opportunity to be recruited.

Snowball Sampling

When recruiting members into a sample population becomes difficult, researchers revert to snowball sampling, where recruits are asked to suggest friends who may be willing to participate in the study.

Sampling Used in Qualitative Studies

Sampling used in qualitative studies is usually either purposeful sampling or theoretical sampling:

PURPOSEFUL SAMPLING – the researcher seeks individuals who can provide the required data
THEORETICAL SAMPLING – the researcher uses a sampling method which, although similar to purposeful sampling, also includes changing and/or adapting the participants’ selection throughout the study based on results obtained from previous participants

NOTE: sample size does not matter in qualitative studies, since the aim is to acquire in-depth understanding of a phenomena.

Data Collection Variables in Medical Statistics

Variables are characteristics, numbers, or quantities which can be measured or counted. Some examples of variables include age, sex, blood pressure results, oxygen saturation levels etc.

Categorical Variables a.k.a. Qualitative Variables

Data collection in qualitative studies typically takes place during in-depth interviews such as one-to-one interviews or focus group interviews, and in some cases, non-structured observation may also be involved.

Categorical variables give qualitative information about the subject being investigated. Thus, possible responses in this variable are not numerical in nature, but instead are different categories related to the subject.

Categorical variables can also be divided into two:

Nominal Variable – a variable with a number of categories eg. occupation
Binary Variable – a variable with only two possible responses eg. yes or no

Continuous variables a.k.a. Quantitative Variables

Continuous variables give quantitative information about the subject in question. Thus, continuous variable responses can be any quantities within a set interval of values. Some examples would be age and BMI.

Data collection in quantitative studies may include:

readily available data such as data related to hospital activity, registers, prevalence and determinants
self-administered questionnaires which may include numerical scales
structured interviews through phone, electronic media, or face to face interviews, all of which allow an element of explanation and feedback between the researcher and the participant
structured observation which typically happen during observation schedules within a particular setting

Ordinal Variables a.k.a. Discrete Variables

Ordinal variables give limited quantitative information because responses achieved are numerically related to each other, yet have to be one within a limited number of values.

Data Analysis

Descriptive Statistics

Descriptive statistics feature a summary of data in a clear, concise and easy-to-understand way, usually through a numerical approach.

Inferential Statistics

Inferential statistics are statistics which, after being calculated from a sample, inferences are made on the original population using the same statistics.

Reference

Kirkwood, Betty R. (2003). essential medical statistics. Blackwell Science, Inc., 350 Main Street, Malden, Massachusetts 02148–5020, USA: Blackwell. ISBN978-0-86542-871-3.

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Literature Searching Strategies For Dissertation Writing

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When searching through literature searching strategies for the purpose of writing your dissertation, you need to seek a good strategy which is both comprehensive and systematic. A systematic collection of observations from research subjects (such as demographic characteristics, physical characteristics, biological markers, behaviours, or feelings, emotions or views) aiming to create information about these subjects is otherwise referred to as research. This can be performed in the following order:

Reflect on potential research areas or questions which are of interest to you
Carry out simple searches, both on Google and in textbooks so as to obtain general knowledge on the subject of your interest
Attempt to develop your research question; you may find the need to refine your question at a later stage or even restart your search from scratch to change your chosen subject
Seek assistance by experts in the field of your interest and discuss related information sources
Carry out advanced electronic research
As part of the selection process, search manually through resulting key studies so as to confirm their relevance to your PICO question
At this stage you should now have a clear idea of which relevant studies you can use for your own review
Seek once again your chosen expert in the same field of study to confirm whether your refined idea is appropriate and relevant to the local scenario and clarify any related questions

Study Approaches and Designs

Every research study aims to answer a research question, which in itself determines the best approach and design to be used.

CHOOSING THE BEST DESIGN:

EXPERIMENTAL DESIGN – Randomised Control Trial (RCT)
OBSERVATIONAL DESIGN – Cross Sectional, Cohort, and Case Control Study

CHOOSING THE BEST APPROACH:

QUANTITATIVE APPROACH – emphasises on objective quantifiable measurements of attributes, aiming to generalise to a wider population; this approach involves theory testing and numerical data collection which can be analysed using statistical techniques
QUALITATIVE APPROACH – emphasises on subjective measures which may be varied or may change over time; this approach, which usually relies heavily on data interpretation, involves theory development, commonly including data in words and narratives such as perceptions and experiences aiming to understand or explain a typical behaviour.

NOTE: in qualitative research, rigor influences the validity of the produced results, which in turn determines how useful the evidence produced is, in terms of evidence based practice.

Literature Searching Strategies

Carrying out an Electronic Search

To carry out an electronic search you should search for articles within electronic databases which provide access to various electronic journals eg. International Journal of Nursing Studies and Journal of Nursing Education. Such journals include a number of publications a.k.a. articles.

The efficacy of an electronic search depends on how well your research question has been designed, how extensive was your search in relation to words and phrases used, the use of search tools such as Truncations and Boolean Operators, the use of good databases, and your review of literature search strategies until you are happy with your end results.

Choosing Search words and/or Phrases

A well designed research question should feature PICO elements…

Retrieved from https://libguides.cdu.edu.au/c.php?g=167917&p=3738712 on 19th November 2022

Search terms used can be in the form of single words or phrases. Phrases should be put in inverted commas. Always keep in mind that search engines provide you ONLY with articles containing the words you use in your searches.

Finding synonyms for each of the PICO components may be facilitated by:

brainstorming
thesaurus
MeSH browser
taking ideas from previously written related articles
using all word options including words containing hyphenations, alternative spelling and abbreviations

Additional Search Tools

Boolean Logic Operators

Use of Boolean Logic Operators AND, Or, and NOT:

AND combines words/phrases together so that both appear within one article found by a search.

Example: a search for ‘needles AND fear’ will find only those articles that contain both the words needles and fear.

OR enables a selection of any one of a number of specified words in a list.

Example: behavioural OR behavioral

NOT excludes specific words so articles containing them will not be identified.

Example: ‘fear of needles NOT fear of hospitals’

Truncation

Truncation helps search all the variations of a word without writing them.

Example: Child* picks up child, children, childhood etc

Wildcard

Wildcard helps you identify alternative spellings of the same word easily.

Example 1: An?emia would pick up anaemia and anemia

Example 2: H?emoglobin would pick up haemoglobin and hemoglobin

Phrase Searching

Phrase searching through the use of inverted commas helps you pick up articles containing your chosen phrase only.

Example: “pressure sores” picks up the phrase as written and not where both words are used separately

Searching within a Database

When conducting an electronic search, you can use databases that facilitate your work. Universities tend to subscribe to a substantial number of databases which include a wide variety of articles across different fields of study. For students following a course at the University of Malta there are a good number of databases that students can use for their literature searching strategies.

After finding a database to search in:

use limiters – eg. ticking peer reviewed articles increases the likelihood of finding articles which are of good quality
choose date/s – ideally limit your search to the last 3 years; if no interesting articles come up, widen your search to the last 5 years or more if need be
do not use ‘Full text’ as a limiter
do not use unnecessary limiters
combine keywords in your searches using Boolean Operators
use other search tools as mentioned further above to help define your searches
stop searching only when you have exhausted all possible literature searching strategies for relevant content

NOTE: Keep a record of ALL searches you apply, including implemented changes, as well as the results obtained with each of your searches!

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