Editing Randomized controlled trial (section)

== Analysis of data ==
The types of statistical methods used in RCTs depend on the characteristics of the data and include:
* For [[dichotomous]] (binary) outcome data, [[logistic regression]] (e.g., to predict sustained virological response after receipt of [[peginterferon alfa-2a]] for [[hepatitis C]]<ref name="Manns-2001">{{Cite journal |vauthors=Manns MP, McHutchison JG, Gordon SC, Rustgi VK, Shiffman M, Reindollar R, Goodman ZD, Koury K, Ling M, Albrecht JK |date=September 2001 |title=Peginterferon alfa-2b plus ribavirin compared with interferon alfa-2b plus ribavirin for initial treatment of chronic hepatitis C: a randomised trial |journal=Lancet |volume=358 |issue=9286 |pages=958–965 |doi=10.1016/S0140-6736(01)06102-5 |pmid=11583749 |s2cid=14583372}}</ref>) and other methods can be used.
* For continuous outcome data, [[analysis of covariance]] (e.g., for changes in blood lipid levels after receipt of [[atorvastatin]] after [[acute coronary syndrome]]<ref name="Schwartz-2001">{{Cite journal |vauthors=Schwartz GG, Olsson AG, Ezekowitz MD, Ganz P, Oliver MF, Waters D, Zeiher A, Chaitman BR, Leslie S, Stern T |date=April 2001 |title=Effects of atorvastatin on early recurrent ischemic events in acute coronary syndromes: the MIRACL study: a randomized controlled trial |journal=JAMA |volume=285 |issue=13 |pages=1711–1718 |doi=10.1001/jama.285.13.1711 |pmid=11277825 |doi-access=free}}</ref>) tests the effects of predictor variables.
* For time-to-event outcome data that may be [[Censoring (statistics)|censored]], [[survival analysis]] (e.g., [[Kaplan–Meier estimator]]s and [[Cox proportional hazards model]]s for time to [[coronary heart disease]] after receipt of [[Hormone replacement therapy (menopause)|hormone replacement therapy in menopause]]<ref name="Rossouw-2002">{{Cite journal |vauthors=Rossouw JE, Anderson GL, Prentice RL, LaCroix AZ, Kooperberg C, Stefanick ML, Jackson RD, Beresford SA, Howard BV, Johnson KC, Kotchen JM, Ockene J |date=July 2002 |title=Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results From the Women's Health Initiative randomized controlled trial |journal=JAMA |volume=288 |issue=3 |pages=321–333 |doi=10.1001/jama.288.3.321 |pmid=12117397 |s2cid=20149703 |doi-access=free}}</ref>) is appropriate.

Regardless of the statistical methods used, important considerations in the analysis of RCT data include:
* Whether an RCT should be stopped early due to interim results.  For example, RCTs may be stopped early if an intervention produces "larger than expected benefit or harm", or if "investigators find evidence of no important difference between experimental and control interventions."<ref name="Moher-2010" />
* The extent to which the groups can be analyzed exactly as they existed upon randomization (i.e., whether a so-called "[[intention-to-treat analysis]]" is used). A "pure" intention-to-treat analysis is "possible only when complete outcome data are available" for all randomized subjects;<ref name="Hollis-1999">{{Cite journal |vauthors=Hollis S, Campbell F |date=September 1999 |title=What is meant by intention to treat analysis? Survey of published randomised controlled trials |journal=BMJ |volume=319 |issue=7211 |pages=670–674 |doi=10.1136/bmj.319.7211.670 |pmc=28218 |pmid=10480822}}</ref> when some outcome data are missing, options include analyzing only cases with known outcomes and using [[Imputation (statistics)|imputed]] data.<ref name="Moher-2010" /> Nevertheless, the more that analyses can include all participants in the groups to which they were randomized, the less bias that an RCT will be subject to.<ref name="Moher-2010" />
* Whether [[subgroup analysis]] should be performed.  These are "often discouraged" because [[multiple comparisons]] may produce false positive findings that cannot be confirmed by other studies.<ref name="Moher-2010" />