Lawrence: /* 7. Conclusion */

2025-06-04T11:55:02Z

7. Conclusion

← Older revision		Revision as of 11:55, 4 June 2025
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	= Per-Protocol (PP) Analysis =		= Per-Protocol (PP) Analysis =

	'''Per-Protocol (PP) Analysis''' evaluates the effect of an intervention among participants who fully adhered to the assigned treatment protocol. In contrast to Intention-to-Treat (ITT) analysis, which includes all randomized participants regardless of adherence, PP analysis estimates the efficacy of an intervention under ideal conditions.		'''Per-Protocol (PP) [[Analysis]]''' evaluates the effect of an intervention among participants who fully adhered to the assigned treatment protocol. In contrast to Intention-to-Treat (ITT) analysis, which includes all randomized participants regardless of adherence, PP analysis estimates the efficacy of an intervention under ideal conditions.

	== 1. When to Use Per-Protocol Analysis ==		== 1. When to Use Per-Protocol Analysis ==
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	* When assessing the true biological or mechanistic effect of an intervention.		* When assessing the true biological or mechanistic effect of an intervention.
	* In studies where adherence is essential to achieving the treatment effect (e.g., dietary or behavioral trials).		* In studies where adherence is essential to achieving the treatment effect (e.g., dietary or behavioral trials).
	* As a complement to ITT analysis for sensitivity or subgroup analysis.		* As a complement to ITT analysis for sensitivity or [[subgroup analysis]].

	''Example:'' In a diabetes drug trial, the ITT population includes all randomized participants, while the PP analysis includes only those who took ≥80% of their prescribed doses.		''Example:'' In a diabetes drug trial, the ITT population includes all randomized participants, while the PP analysis includes only those who took ≥80% of their prescribed doses.
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	* '''Higher risk of bias''': Excluding non-adherent participants may introduce selection bias.		* '''Higher risk of bias''': Excluding non-adherent participants may introduce selection bias.
	* '''Loss of randomization benefits''': Treatment groups may become unbalanced after exclusions.		* '''Loss of [[randomization]] benefits''': Treatment groups may become unbalanced after exclusions.
	* '''Limited generalizability''': Findings apply only to those who followed the protocol and may not reflect real-world effectiveness.		* '''Limited generalizability''': Findings apply only to those who followed the protocol and may not reflect real-world effectiveness.

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	* Clearly define the adherence criteria used.		* Clearly define the adherence criteria used.
	* Report the number and proportion of participants excluded from the ITT population.		* Report the number and proportion of participants excluded from the ITT population.
	* Compare PP and ITT results as part of a sensitivity analysis.		* Compare PP and ITT results as part of a [[sensitivity analysis]].


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	Per-Protocol analysis estimates the ideal efficacy of an intervention but comes with a higher risk of bias. It should be interpreted cautiously and reported alongside ITT analyses as part of a comprehensive trial analysis. Pre-specifying adherence criteria is critical to maintain transparency and minimize bias.		Per-Protocol analysis estimates the ideal efficacy of an intervention but comes with a higher risk of bias. It should be interpreted cautiously and reported alongside ITT analyses as part of a comprehensive trial analysis. Pre-specifying adherence criteria is critical to maintain transparency and minimize bias.

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			=== Bibliography ===

			# Gupta SK. Intention-to-treat concept: a review. ''Perspectives in Clinical Research''. 2011;2(3):109–112. Discusses both intention-to-treat and per-protocol approaches.
			# Montori VM, Guyatt GH. Intention-to-treat principle. ''CMAJ''. 2001;165(10):1339–1341. Includes discussion of when per-protocol analysis may be considered.
			# White IR, Horton NJ, Carpenter J, Pocock SJ. Strategy for [[intention-to-treat analysis]] in randomised trials with missing outcome data. ''BMJ''. 2011;342:d40. Reviews limitations and alternatives including per-protocol.
			# Hernán MA, Hernández-Díaz S, Robins JM. Randomized trials analyzed as observational studies. ''Annals of Internal Medicine''. 2013;159(8):560–562. Critiques naive per-protocol analysis and suggests causal modeling approaches.
			# Piantadosi S. Clinical Trials: A Methodologic Perspective. 3rd ed. Wiley; 2017. Chapter on protocol adherence and analysis strategies, including per-protocol and as-treated analysis.

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			''Adapted for educational use. Please cite relevant trial methodology sources when using this material in research or teaching.''

Lawrence: Created page with "= Per-Protocol (PP) Analysis = '''Per-Protocol (PP) Analysis''' evaluates the effect of an intervention among participants who fully adhered to the assigned treatment protocol. In contrast to Intention-to-Treat (ITT) analysis, which includes all randomized participants regardless of adherence, PP analysis estimates the efficacy of an intervention under ideal conditions. == 1. When to Use Per-Protocol Analysis == PP analysis is particularly useful in specific scenarios..."

2025-03-27T22:03:49Z

Created page with "= Per-Protocol (PP) Analysis = '''Per-Protocol (PP) Analysis''' evaluates the effect of an intervention among participants who fully adhered to the assigned treatment protocol. In contrast to Intention-to-Treat (ITT) analysis, which includes all randomized participants regardless of adherence, PP analysis estimates the efficacy of an intervention under ideal conditions. == 1. When to Use Per-Protocol Analysis == PP analysis is particularly useful in specific scenarios..."

New page

= Per-Protocol (PP) Analysis =

'''Per-Protocol (PP) Analysis''' evaluates the effect of an intervention among participants who fully adhered to the assigned treatment protocol. In contrast to Intention-to-Treat (ITT) analysis, which includes all randomized participants regardless of adherence, PP analysis estimates the efficacy of an intervention under ideal conditions.

== 1. When to Use Per-Protocol Analysis ==

PP analysis is particularly useful in specific scenarios:

* When assessing the true biological or mechanistic effect of an intervention.
* In studies where adherence is essential to achieving the treatment effect (e.g., dietary or behavioral trials).
* As a complement to ITT analysis for sensitivity or subgroup analysis.

''Example:'' In a diabetes drug trial, the ITT population includes all randomized participants, while the PP analysis includes only those who took ≥80% of their prescribed doses.

== 2. Key Considerations ==

* '''Higher risk of bias''': Excluding non-adherent participants may introduce selection bias.
* '''Loss of randomization benefits''': Treatment groups may become unbalanced after exclusions.
* '''Limited generalizability''': Findings apply only to those who followed the protocol and may not reflect real-world effectiveness.

== 3. Steps for PP Analysis ==

1. '''Define adherence criteria''': This should be outlined in the study protocol. Examples include minimum medication adherence (e.g., ≥80%), attendance at a specified number of therapy sessions, or completion of follow-up assessments.
2. '''Exclude non-adherent participants''': Participants who crossed over to another group, missed major follow-up points, or deviated from the protocol are excluded.
3. '''Analyze the per-protocol population''': Conduct statistical analysis using only those who met the adherence threshold.

== 4. Statistical Methods for PP Analysis ==

* '''Continuous outcomes''': Use t-tests, linear regression, or mixed-effects models.
* '''Binary outcomes''': Apply logistic regression or estimate relative risk.
* '''Time-to-event outcomes''': Use Kaplan–Meier survival analysis or Cox proportional hazards models.

== 5. PP vs. ITT Analysis ==

{| class="wikitable"
|+ '''Comparison of Per-Protocol and Intention-to-Treat Analysis'''
! Aspect !! Per-Protocol (PP) !! Intention-to-Treat (ITT)
|-
| Population || Only adherent participants || All randomized participants
|-
| Effect Estimate || Efficacy (ideal conditions) || Effectiveness (real-world)
|-
| Bias Risk || Higher (due to exclusions) || Lower (preserves randomization)
|-
| Clinical Relevance || Lower || Higher
|}

== 6. Reporting PP Analysis in RCTs ==

* Clearly define the adherence criteria used.
* Report the number and proportion of participants excluded from the ITT population.
* Compare PP and ITT results as part of a sensitivity analysis.

== 7. Conclusion ==

Per-Protocol analysis estimates the ideal efficacy of an intervention but comes with a higher risk of bias. It should be interpreted cautiously and reported alongside ITT analyses as part of a comprehensive trial analysis. Pre-specifying adherence criteria is critical to maintain transparency and minimize bias.

Per-protocol analysis - Revision history

Lawrence: /* 7. Conclusion */