Choosing the Right Modeling Strategy: Explanatory, Exploratory, and Predictive Approaches in Clinical Research

Table: Modeling Strategies in Clinical Research

Dimension	Explanatory Model	Exploratory Model	Predictive Model
Primary Purpose	Test a causal hypothesis	Discover associations	Forecast outcomes
Main Research Question	“Does X (and only X) cause Y?”	“What variables are associated with Y?”	“What combination of Xs best predicts Y?”
Focus of Analysis	One primary exposure (X)	Multiple candidate exposures (Xs)	Multiple predictors
Use of Prior Hypothesis	Required	Not required	Often not required
Treatment of Confounders	Mandatory adjustment by context (not p-values)	Not addressed	Not relevant
Variable Selection Allowed?	No	No	Yes (univariable screening, forward/backward selection)
Model Type	Full model only	Full model only	Parsimonious model preferred
Causal Interpretation	Yes	No	No
Predictive Performance Measured?	No	No	Yes (e.g., AUC, calibration, accuracy)
Acceptable Variable Removal?	No	No	Yes
Model Evaluation Metrics	Effect estimates, confidence intervals	Contribution patterns (e.g., coefficients, p-values)	Discrimination, calibration, overall prediction accuracy
Typical Use Case	Hypothesis-driven clinical trial analysis	Exploratory cohort or registry data analysis	Clinical risk prediction tools, machine learning models

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Introduction

Statistical modeling is a central technique in clinical research for uncovering associations, testing hypotheses, and predicting future outcomes. However, the choice of modeling strategy should reflect the study’s primary objective. Different purposes—such as explaining causal mechanisms, exploring patterns in the data, or predicting outcomes—require distinct methodological approaches. Understanding these strategic frameworks ensures the analytic method is aligned with the research question.

This article outlines three core modeling strategies: explanatory, exploratory, and predictive. Each strategy is discussed in terms of its intent, structure, and appropriate use cases, along with guidance on how to handle variable selection, confounding, and performance evaluation.

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Explanatory Modeling: Testing Causal Hypotheses

Purpose and Focus

Explanatory models aim to assess whether a particular exposure or independent variable—designated here as “X”—causes a specific outcome, “Y.” This approach is most appropriate when the goal is causal inference. The analysis is focused on a predefined exposure of interest, and all other variables are treated as potential confounders that must be accounted for to isolate the effect of “X.”

Key Characteristics

• Single Focal Predictor: The model is centered around one primary independent variable.

• Causal Logic: It seeks to determine if the exposure causes the outcome, not merely whether they are associated.

• Contextual Confounding Control: Adjustment for confounders is determined based on clinical, epidemiological, or theoretical understanding, not automated statistical criteria.

• Full Model Requirement: All variables deemed necessary for confounding control must be included; reduced models are discouraged.
  

Methodological Rules

• No Variable Selection Procedures: Techniques like univariable screening, forward selection, or backward elimination are inappropriate.

• No Model Simplification: The model must retain all necessary variables regardless of statistical significance.

• No Performance Evaluation Metrics: Predictive performance (e.g., accuracy or AUC) is irrelevant; the priority is unbiased estimation of causal effects.
  

Illustrative Scenario

Suppose a researcher wants to determine if a specific prenatal supplement causes reduced incidence of neonatal jaundice. The model would adjust for known confounders like gestational age, birth weight, and maternal health—regardless of their statistical significance—because these factors could bias the causal relationship between the supplement and jaundice.

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Exploratory Modeling: Identifying Potential Associations

Purpose and Focus

Exploratory models are hypothesis-generating tools used when the relationships between multiple variables and an outcome are not well understood. These models do not aim to establish causation but rather to identify factors that may be associated with a given outcome.

Key Characteristics

• Multiple Candidate Predictors: The model includes several “X” variables, with no single focal predictor.

• No A Priori Hypotheses: Variables are included to explore possible associations without prior assumptions.

• No Control for Confounding: Since the model is not intended for causal inference, adjusting for confounders is unnecessary.
  

Methodological Rules

• Full Model Approach: Like explanatory models, exploratory models retain all candidate variables without reduction.

• No Selection Procedures: Variable screening or elimination techniques are not employed.

• Performance Metrics Not Used: The goal is understanding patterns, not making predictions.
  

Illustrative Scenario

A public health researcher investigating which social or behavioral factors are linked to poor medication adherence among patients with hypertension might include variables such as income, education level, perceived stress, number of daily pills, and access to healthcare. No specific causal hypothesis is tested; instead, the aim is to uncover potentially meaningful associations for future study.

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Predictive Modeling: Forecasting Future Outcomes

Purpose and Focus

Predictive models are designed to generate accurate forecasts of an outcome based on multiple input variables. These models are commonly used in clinical decision support, risk stratification, and early warning systems. Here, the priority is predictive accuracy, not causality or explanatory clarity.

Key Characteristics

• Multivariable Input Set: Several predictors are considered simultaneously to optimize prediction.

• No Interest in Causality: Relationships are assessed based on their predictive contribution, not causal structure.

• No Requirement to Control Confounding: Since the goal is not causal interpretation, confounders are not specifically identified or adjusted for.
  

Methodological Flexibility

• Variable Selection Encouraged: Methods such as univariable screening, forward selection, and backward elimination are acceptable.

• Model Parsimony Favored: Simpler models with fewer predictors are preferred when they retain sufficient predictive power.

• Performance Evaluation Required:

  • Discrimination: The model’s ability to distinguish between outcomes (e.g., area under the ROC curve).

  • Calibration: How closely predicted probabilities match observed outcomes.

  • Overall Performance: Measures like the Brier score or cross-validated accuracy.
    

Illustrative Scenario

A hospital develops a model to predict the likelihood of ICU readmission within 48 hours after discharge. Variables might include age, vital signs, recent laboratory results, and comorbidities. Using automated selection and performance testing, the final model includes the most predictive subset and is validated using a separate patient dataset.

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Conclusion

Selecting the appropriate modeling strategy is a critical decision in clinical research design. Explanatory models are best for testing specific causal hypotheses, exploratory models are useful for discovering new associations, and predictive models are tailored for accurate forecasting. Each strategy requires distinct rules about variable inclusion, confounding control, and performance assessment. By aligning modeling approaches with research goals, investigators can produce findings that are not only statistically sound but also scientifically meaningful and clinically actionable.

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