Epidemiological Studies Insights

Basic Infection Control Measures: Errors, Bias & Confounders

• Learning Objectives (ILOs): Understand errors in epidemiological studies, identify type 1 and type 2 errors, define bias and confounders, explore bias types, and learn to avoid bias in epidemiological studies.

Introduction

• Central Goals of Epidemiological Studies: Obtaining precise population parameter estimates and establishing causal associations between exposures and outcomes.
• Threats to these goals: Different errors that can arise at any stage of an epidemiological study.

Errors in Epidemiological Studies

• Types of Errors: Random, systematic, and confounders.
  • Random errors: Affect participants equally, irrespective of exposure, arising from random variation or chance.
  • Systematic errors (Bias): Errors that modify results differently among groups, affected by factors like disease, exposure, or inherent differences in participant groups.
  • Confounders: Variables associated with both exposure and outcome but not an intermediate step on the causal pathway.

Random Error and Bias

• Study Components and Errors: The study plan, random error and bias, and actual study influence the truth in the study and the study findings compared to the truth in the universe.
• Errors & Bias Importance: Errors and biases can affect any study part, and biases and various study biases may occur in every step of study design, analysis, and interpretation.
• Importance of Research: Maximizing strengths and minimizing threats at every study stage helps produce inferences that are not significantly affected by errors, to make inferences about a subject or phenomenon only if those biases are sufficiently small.

Random Errors

• Effect: Affect participants equally regardless of exposure, tending to cancel out within groups over many observations.
• Impact on Analysis: Modifying the analysis less significantly than systematic errors.
• Minimizing: Improving sampling methods, study design, and sample size; using validated, regularly calibrated instruments; taking multiple measurements to calculate averages.
• Management: Improving sampling, study design, sample size, measurement tools and methods.

Systematic Errors (Bias)

• Nature: Predictable or systematic deviations from the truth.
• Impact on Study: Can change the magnitude of estimated associations significantly.
• Outcome Affected Groups: Can be more prevalent in one group of participants, influenced by factors like disease, exposure, or pre-existing characteristics.

Types of Selection Bias

• Berkson's Bias: Incorrect classification of participants, preferential enrollment based on characteristics, or unequal allocations within the sample.
• Loss to Follow-up: Participants missing study follow-up appointments, potentially differing significantly from those who regularly attend, potentially influencing risk factor exposure and disease status.
• Non-Response Bias: Selective non-participation of participants with specific characteristics, potentially influencing the study findings.
• Membership Bias: Subjects choosing to be part of a specific group with shared attributes that might differ significantly from other populations.

Information Bias

• Recall Bias: Inaccurate recall of past exposures or disease status details; cases may be more likely to recall details than controls.
• Interviewer Bias: Interviewers may subtly influence responses, potentially leading to different responses from cases and controls.
• Observer Bias: Observers might differentially observe outcomes among exposed and unexposed groups, influenced by preconceived notions about associations.
• Respondent Bias: Outcome information is impacted by participants' self-assessments.

Temporal Bias

• Nature: Inaccurate temporal sequencing of cause and effect, often appearing in cross-sectional and case-control studies.

Length Bias

• Nature: Longer preclinical phases of diseases, potentially affecting screening outcomes.
• Effect: Active screening may identify more early-stage cases than later-stage cases if undetected disease phases are longer than the exposure latency period.

Lead Time Bias

• Nature: Time of diagnosis advanced by early detection and screening.
• Effect: Misleads to believing survival rate is increased versus a longer time of having the condition, influenced by early diagnosis methods.

Compliance Bias

• Nature: Differential compliance to treatment (for example, taking one dose versus multiple daily doses).

Confounding Variables

• Definition: Characteristic of observation units, associated with both exposure and outcome, not an intermediary on the causal pathway.
• Effect on Estimates: Third factor not considered, creating the illusion of association between two factors when one exists only because of the third factor.
• Control Strategies: Restriction (restricting groups to factors), matching (similar characteristics between groups), stratification (divide study into subgroups), multivariable analysis (factors in a model).

Type 1 & Type 2 Errors

• Type 1 Error: Rejecting a true null hypothesis (false-positive).
• Type 2 Error: Failing to reject a false null hypothesis (false-negative).
• Minimization Strategies: Increasing sample size, which reduces likelihood of these errors, so a larger sample is more representative of the population. Also, bias can cause false-positive or false-negative results.

Control of Bias

• Strategies: Valid data collection tools, blinding of analysts to case status, validation of definitions, minimizing time between events and data collection. Check of response with objective measurements, standardized calibrations, periodic reviews of procedures, protocols.

Additional Considerations

• Important Notes: Minimizing errors by balancing threats and strengths. Methods to avoid large errors affecting conclusions are valuable.