Analysis SectionAnalysis SectionResearch Design

Protocol OverviewProtocol OverviewBackground 4-5

pages

Question/Objective/Hypothesis 4 lines

Design 4-20 lines

Study Population 0.5-1 page

Measurement 3.5-4 pgs.

Outcomes

Exposures/predictors

Confounders

Analysis 0.5-1 page

Other (ethics/procedure/expected contribution)

0.5-1 page

Expected Components of the Expected Components of the Analysis Section: Primary Analysis Section: Primary ObjectiveObjective Primary statistical approach to address study

objective/ hypothesis (main results table)◦ Statistical assumption assessment-changes in main

approach Model-Fitting Issues

◦ Inclusion of predictor and confounding variables◦ Linearity assessment◦ Effect modification assessment (interactions)◦ Quality of Model Fit◦ Management of cluster/correlation in the data◦ Sensitivity analysis◦ Multiple testing/Missing value management

Relevance / Quality of Prediction Assessment◦ Proportion of Variance Explained◦ Area under the Curve◦ Population Attributable Risk/Benefit

Expected Components of the Expected Components of the Analysis Section: Secondary Analysis Section: Secondary IssuesIssuesAssessment of “Within Study”

Methodological Issues◦ Unblinding◦ Randomization integrity◦ Inter-rater, intra-rater agreement◦ Equivalence of data collection at different test sites,

time periods◦ Reliability of data-collection methods,

questionnaires◦ Construct/content validity of measures

Analysis of Secondary Objectives◦ Main methodological approach◦ Predictors, outcomes and confounders

Examples of Expected Examples of Expected ComponentsComponentsExpected Components

Example

Restate Primary Study Objective

• To test the association between “x” and “y”

• To estimate the incidence of” x”• To estimate the reproducibility of “q”

Main approach to the analysis

•..multivariate logistic regression•..intra-class correlation

Model-fitting issues-inclusion of predictors and potential confounders

•All predictors and potential confounders included..•Full model fit with backward elimination based on p-value > 0.1

-linearity •..a quadratic term will be added to assess linearity

-effect modification • to assess the hypothesized modification in the association between n”x” and “y” by “R”, an interaction term between “x” and ” R” will be included in the model and the Likelihood Ratio Test will be used to determine if it improves model fit

Define Main Approach to the Define Main Approach to the Analysis and 2 Issues in Analysis and 2 Issues in Model-Fitting & Within Study Model-Fitting & Within Study Analysis Analysis

ObjectiveTo determine whether outcomes after in-hospital cardiac arrest differ duringnights and weekends compared with days/evenings and weekdays.Design and SettingWe examined survival from cardiac arrest in hourly time segments,defining day/evening as 7:00 AM to 10:59 PM, night as 11:00 PM to 6:59 AM, andweekend as 11:00 PM on Friday to 6:59 AM on Monday, in 86 748 adult, consecutivein-hospital cardiac arrest events in the National Registry of Cardiopulmonary Resuscitationobtained from 507 medical/surgical participating hospitals from January 1, 2000,through February 1, 2007.Main Outcome MeasuresThe primary outcome of survival to discharge and secondaryoutcomes of survival of the event, 24-hour survival, and favorable neurologicaloutcome were compared using odds ratios and multivariable logistic regression analysis.Point estimates of survival outcomes are reported as percentages with95%confidenceintervals (95% CIs). JAMA. 2008;299(7):785-792

Working Discussion Working Discussion ExampleExampleComponent JAMA. 2008;299(7):785-792

Main Approach to the Analysis PredictorOutcomeConfoundersAnalysis Method

Issue #1 Model-Fitting

Issue #2 Model-Fitting

Within-Study Methodological Analysis

Issue #1

Issue #2

JAMA. 2008;299(7):785-JAMA. 2008;299(7):785-792792

To examine the association betweenhour of day and outcomes, we usedevent hour as our exposure variable.The model included prospectively designated,clinically important potentialconfounders or their class (sex, race, illnesscategory, combination of preexistingcondition and cause variables, interventionsin place at time of event,weekend, hospital size, event location,monitored status, witnessed status,first documented rhythm, initial orsubsequent VT/VF, CPR duration, delayin defibrillation, delay in CPR, delayin vasopressor use, use of epinephrine,and time from hospital admissionto event).

Main Analytic Approach

We examined 7 variables for evidenceof effect modification by including theirinteractions with time of day in logisticregression models (first documentedrhythm, event location,whether the event was monitored,whether the event was witnessed, delayin defibrillation, race [specificallyblack vs white], and illness category).

Prospectively designated clinicallyimportant variables (age, Hispanic ethnicity,month of year, other cardiac arrestmedication use, and induced hypothermia)were then entered into astepwise multivariable logistic regressionfor the primary end point of survivalto hospital discharge. The criterionfor the stepwise selection ofvariables was P.25.

Model Fitting Plan

Effect Modification Assessment