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«Outline Designing Clinical Research Studies Statistical Data Analysis Designing Clinical Research Studies Study objective Target population Evidence ...»

Study Design and

Statistical Analysis

Anny H Xiang, PhD

Department of Preventive Medicine

University of Southern California

Outline

Designing Clinical Research

Studies

Statistical Data Analysis

Designing Clinical Research Studies

Study objective

Target population

Evidence to support the study

Study design

How big the study should be?

What data to collect

Follow-up?

Data management, monitoring, quality control

Data analysis plan

What is the Study Objective?

Comparing two treatments in disease control?

Identifying prognostic factors for disease development?

Appropriate research questions that can be answered Translated into hypotheses (primary and secondary hypotheses)

Primary hypothesis :

The most interested question Capable of being adequately answered What is the Target Population?

Defined by the research questions (e.g., diabetes prevention) Ideal to study entire target population Valid if randomly select samples from the target Practically restricted to what’s available Impact generalization of the study results Minimize possible bias associated with sample selection Example 1: TRIPOD Study (Diabetes, 2002) Objective: To test whether treatment of insulin resistance using TZD can delay or prevent type 2 diabetes Population: High-risk for diabetes (not diabetes yet) Hispanic women with prior GDM, at least 18 years old Example 2: GDM cohort Study (Diabetes, 1999) Objective: To identify antepartum characteristics that predict the development of diabetes 11-26 months after index pregnancy in women diagnosed with GDM Population: Hispanic women with GDM who were not diabetic at postpartum visit What Evidence to Support the Study PubMed, MEDLINE, etc., literature search and review Pilot study Clinical evidence/experience Meta analysis results What Type of Study Design is Appropriate?

Clinical trials?

Epidemiological study?

A clinical trial is an experimental study, require special consideration of ethics (DSMB) Epidemiological study is an observational study Types of Clinical Study Design Clinical trials Randomized controlled trial Non-randomized controlled trial Historical controls/database Cross-over design Factorial design Single arm study: Phase I & II Clinical Trial Phases

Phase I:

Establish maximum dosage

Phase II:

Look for preliminary evidence of efficacy and side effect

Phase III:

Treatment is compared to control to demonstrate efficacy

Phase IV:

Post marketing surveillance Types of Clinical Study Design (continued) Observational studies Case-control study Cross-sectional study Longitudinal cohort study Case series Case report

–  –  –

Meta Analysis A formal process with statistical methods to combine all the data from studies involving similar participants and similar objectives in a single analysis to reach a conclusion on the overall results

–  –  –

Designed to demonstrate equivalence of two treatments In difference trials, failure to reject the null (e.g., p0.05) Does not mean the two are equal Can only conclude that the evidence is inadequate to conclude they are unequal Need an equivalency trial to prove equivalency

–  –  –

A good standard treatment must exist A new one can be as effective as the standard but with Less side effects Less cost Greater convenience Higher quality of life Sample size is much larger than difference trial because a very small difference is compared Randomization for Clinical Trials Reduce bias Achieve balance Quantify error attributable to chance (random)

–  –  –

Fixed allocation randomization Simple randomization Blocked randomization Stratified randomization Adaptive randomization Baseline adaptive randomization Response adaptive randomization

–  –  –

TRIPOD study: Randomized controlled clinical trial comparing two interventions (troglitazone vs. placebo) GDM cohort study: Epidemiological observational prospective cohort study to identify antepartum predictors for diabetes 11-26 months after pregnancy How big the study should be?

Key to the study power to detect the effect Small effect requires large sample size Power to detect the primary hypothesis should be 80% Pilot study does not require 80% power, mainly for preliminary data and feasibility How big the study should be?

Estimated based on three parameters:

Type I error (false positive, 0.05) Type II error (false negative, 0.20) Effect size Effect size defined differently for different statistical methods Sample size is generally estimated by the primary hypothesis Design a Confirmative Phase III Trial Try to limit to one primary response variable Type I error increase if multiple responses, need to consider it in sample size estimation Hard to make conclusion if inconsistent results for multiple response variables

–  –  –

TRIPOD study: The sample size was projected to provide power 80% to detect a 20% difference in cumulative diabetes incidence rates between treatment groups at a median follow-up of 42 month with a type I error of 0.05 (n=266) GDM cohort study: The sample size was estimated based on the relative risk 2-4 in developing diabetes comparing the highest to the lowest quartile of the baseline predictor variable





What Data to Collect?

Defined by research questions Should collect at least all the measurements needed to test each of the hypotheses, including prognostic and confounder measures Can include other measures if minimal impact to the study (for future hypothesis exploration) All measurements should be assessed unbiased (blinding data assessor and subjects if needed)

–  –  –

What is a surrogate measure?

Substitute measure to the clinical outcome When do we use it?

For rare event (sample size) For difficult to measure clinical outcome A good surrogate exist Prefer a continuous measure

–  –  –

What is a good surrogate measure?

Pathway to clinical event Changes is highly correlated with real clinical outcome of interest Can be assessed with little error Acceptable by scientific and medical communities Acceptable by subjects Cost-effective Example: IMT, CD4, PSA

Follow-up?

Needed for clinical trials Needed for observational prospective longitudinal studies May schedule intermediate visits to capture changes (more information) Need to monitor Attrition rate Missing visits Protocol compliance (clinical trials) Adverse event (clinical trials)

–  –  –

Questions for data analysis:

I. Study design & analysis variables II. Analysis questions (hypotheses) III. What statistical test to use?

I. Study Design & Analysis Variables Study design Know the study design and how data are collected Are observations independent, paired or repeated?

For non-independent observations, data correlation needs to be adjusted (e.g., multiple observation per subject, family members) Analysis Variables Are the variables continuous, categorical or discrete?

–  –  –

Continuous variables: can be measured as precisely as instrumentation permits (e.g., age, BMI, glucose) Discrete variables: countable (e.g., treatment group, diabetes, contraceptive methods) Continuous variables can be “discretized” (e.g., age 50 vs 50, for clinical interpretation) Discrete measures can be made continuous (e.g., scores ranked from 1 to 10)

–  –  –

Translate the research questions into statistical hypothesis (null vs. alternative) What are the dependent variables, independent variables, adjustment variables?

1 or 2-sided test? What type I error?

–  –  –

Null hypothesis: hypothesis that not of interest For difference trial: no difference, no effect, no change, etc For equivalency trial: has difference Alternative hypothesis: hypothesis of interest For difference trial: has effect, change, etc For equivalency trial: no difference Dependent vs. Independent Variables Dependent variables: outcomes, endpoints, response (e.g., diabetes development) Independent variables: treatment, groups, risk factor, casual variables Adjusted variables: confounders, variables which could bias the estimate of the relationship between the dependent and independent variables

–  –  –

1-sided test: tested parameter under alternative hypothesis is either greater than or less than what under the null, but not both (1 direction only) 2-sided test: tested parameter under alternative hypothesis can be greater than or less than what under the null (both directions) Type I error: probability of rejecting the null when null is true (false positive rate), defines the significance cut point (commonly used 0.05) III. What statistical tests to use?

Dependent variable is dichotomous Dependent variable is continuous Dependent variable is time to event III. What statistical tests to use?

Dependent variable is dichotomous (e.g., diabetes status)

Independent variable is dichotomous (e.g. tx group):

Chi-square, Fisher’s exact, McNemar’s test

Independent variable is discrete 2 categories:

Chi-square, Fisher’s exact, McNemar’s test

–  –  –

III. What statistical tests to use?

Dependent variable is dichotomous (e.g., diabetes status)

Independent variable is continuous (e.g., age):

–  –  –

III. What statistical tests to use?

Dependent variable is continuous (e.g., glucose, HbA1c) Independent variable is dichotomous (e.g., tx

group):

T-test, Wilcoxon test

Independent variable is discrete with 2 categories:

–  –  –

Assumption for t-test, ANOVA: normal distribution III. What statistical tests to use?

Dependent variable is continuous (e.g., glucose, HbA1c)

Independent variable is continuous (e.g., age):

Correlation, linear regression

Covariate adjustment:

–  –  –

Assumption: normal distribution III. What statistical tests to use?

Dependent variable is time to event (e.g., time to development of diabetes)

Independent variable is dichotomous (e.g., group):

Logrank test

Independent variable is categorical (2):

–  –  –

III. What statistical tests to use?

Dependent variable is time to event (e.g., time to development of diabetes)

Independent variable is continuous (e.g., age):

Cox regression

–  –  –

GDM cohort study:

Dependent variable: Diabetes status 11-26 months after index pregnancy Independent variable: Antepartum characteristics Adjusted covariates: Variables in the final model were adjusted for each other to assess independent prediction Analysis approach: logistic regression

–  –  –

Dropouts, non-compliance Intent-to-treat vs. response-to-treat analysis Missing data MCAR, MAR, non-ignorable Hard to test missing data mechanism Perform sensitivity analyses Interaction - Subgroup analysis Multiple comparison issue

–  –  –

Repeatedly testing using the same data will increase chance finding – increase type I error

In decreasing order of conservativeness:

Bonferroni multiple comparison procedure Scheffe S test Tukey HSD (honestly significant difference) test

Fisher’s LSD test (least significant difference):

Did not provide appropriate type I error protection Final Recommendation for Conducting Clinical Research Inclusion of the biostatistician from study design through manuscript preparation Good communication & collaboration between clinical investigators and biostatistician is essential Biostatistician should make effort to understand the disease under investigation Clinical investigator should also make effort to understand the biostatistics

–  –  –

PM510: Introduction to Biostatistics (Fall, Spring and Summer) PM512: Introduction to Epidemiology (Fall and Spring) PM523: Clinical Trials (Spring) PM599: Biomedical Informatics (Summer) Information: Mary Trujillo: 323 442-1810

–  –  –

Provide training to medical students, fellows, faculty and health professionals to appropriately conduct medical and translational research One-year of didactic course work One year of mentor-supervised research leading to grant proposals or peer-review paper(s)

–  –  –

Research tracks depend on research interests of applicant Patient-oriented translational research Community-based intervention trials Design, conduct and analysis of clinical trials Epidemiology and disease etiology Molecular biology Cell biology Others





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