CANCER TRIAL METHODS

Question 1

CANCER CELLS WEIGHT AND AMOUNT AT DIAGNOSIS AD AT THE NEAR DEATH STAGE

Answer 1

E.G. LUNG CANCER

• A CANCER NODULE AS SMALL AS 1g CAN BE DETECTED (CCA 10^9 CELLS ARE NEEDED BEFORE DETECTION IS POSSIBLE)
  DIAGNOSIS TRESHOLD: 1cm
• WHEN AT THE NEAR-DEATH STAGE, WIGHT AROUND 1 KG (CCA 10^12 CELLS)

Question 2

CURRENT CANCER TREATMENTS

Answer 2

• SURGERY
• CHEMOTHERAPY
• RADIOTHERAPY
• HORMONE THERAPY
• IMMUNOTHERAPY
• BIOLOGICAL THERAPIES
• BONE MARROW TRANSPLANT
• CRYOABLATION (a treatment to kill cancer cells with extreme cold; during cryoablation, a thin, wandlike needle (cryoprobe) is inserted through your skin and directly into the cancerous tumor0

Question 3

OBJECTIVES OF CANCER TREATMENT

Answer 3

• DESTROY/KILL ALL CANCER CELLS (A POSIBLE OUTCOME OF ACHIEVING COMPLETE CURE)
• DESTROY/KILL MOST CANCER CELLS (A POSSIBLE OUTCOME OF PROLONING SURVIVAL TIME)
• DESTROY/KILL SOME CANCER CELLS (OUTOMES SUCH AS ELIMINATING SYMPTOMS OR PRESERVING QUALITY OF LIFE)

Question 4

ASSESSING TRETMENT EFFICACY; TUMOUR RESPONSE

Answer 4

ASSESSED USING RECIST (RESPONSE EVALUATION CRITERIA IN SOLID TUMOURS) CRITERIA:

COMPLETE RESPONSE: DISAPPEREANCE OF ALL SIGNS OF DISESE
PARTIAL RESPONSE: A REDUCTION OF TUMOUR VOLUME BY AT LEAST 30%
STABLE DISEASE: NO SIGNIFICANT CHANGE
DISEASE PROGRESSION: AN INCREASE IN TUMOR VOLUME BY AT LEATS 20% OR NEW METASTASES

Question 5

ASSESSING TRETMENT EFFICACY; SURVIVAL TIME

Answer 5

OVERALL SURVIVAL; SURVIVAL TIME FROM THE START OF TREATMENT
DISEASE-FREE SURVIVAL; SURVIVAL TIME PRIOR TO TUMOUR RELAPSE AFTER RADICAL TREATMENT
(EITHER CAN BE TERMED THE ‘PRIMARY OUTCOME’ - MOST DESIRED OUTCOME, DEPENDING ON THE OBJECTIVE OF THE CLINICAL TRIAL)
PROGRESSION FREE SURVIVAL; SURVIVAL TIME PRIOR TO TUMOUR PROGRESSION

Question 6

HISTORICAL CONTROL GROUP

Answer 6

A control group that is chosen from a group of patients who were observed at some time in the past or for whom data are available through records.

Question 7

P VALUE

Answer 7

• A MEASURE OF THE STRENGTH OF EVIDENCE PROVIDED BY DATA CONCERNING THE EFFECTIVENESS OF TREATMENT (MEASURE THE LEVEL OF A TRUE TREATMENT EFFECTS)
• PROBABILITY OF OBTAINING THE OBSERVED DATA (OR MORE EXTREME) WHEN THE NULL HYPOTHESIS IS CORRENT (TREATMENT DOESN’T WORK)
• BETWEEN 0 AND 1, THE CLOSER THE VALUE IS TO 0, THE MORE INCREASING IS EVIDENCE THAT TERATMENT WORKS

Question 8

PROBLEMS WITH INTERPRETATION OF P VALUE?

Answer 8

• NOT INFORMATIVE ENOUGH (E.G. P-VALUE OF 0.04 DOES NOT TELL US ANYHTING MUCH DIFFERENT THAN A P-VALUE OF 0.06)
• CAN’T TELL US MUCH ABOUT WHETHER THE TREATMENT WORKS
• NOT ENOUGH ON ITS OWN
• STATISTICAL VERSUS CLINICAL SIGNIFICANCE? INFO ABOUT: COST? SAFETY?
• NEED TO WEIGH UP EVIDENCE FROM STUDY AGAINST RESULTS FROM OTHER STUDIES!

Question 9

FACTORS ASSOCIATED WITH AN INCREASED RISK OF BREAST CANCER

Answer 9

• BEING FEMALE
• INCREASING AGE
• PERSONAL HISTORY OF BREAST CONDITIONS
• PERSONAL HISTORY OF BREAST CANCER
• FAMILY HISTORY OF BREAST CANCER
• INHERITED MUTATIONS IN BRCA GENES
• RADIATION EXPOSURE
• OBESITY
• BEGINNING PERIOD AT A YOUNGER AGE (BEFORE 12)
• BEGINNING MENOPAUSE AT AN OLDER AGE
• HAVING 1ST CHILD AT AN OLDER AGE
• HAVING NEVER BEEN PREGNANT
• HORMONE REPLACEMENT THERAPY
• CONTRACEPTIVE PILL
  (HORMONE BASED RISK FACTORS; LONGER EXPOSURE TO ESTROGEN)
• DRINKING ALCOHOL

Question 10

WHAT IS POWER IN STATISTICS?

Answer 10

Power is the probability of rejecting the null hypothesis when, in fact, it is false.
Power is the probability of making a correct decision (to reject the null hypothesis) when the null hypothesis is false.
Power is the probability that a test of significance will pick up on an effect that is present.
Power is the probability that a test of significance will detect a deviation from the null hypothesis, should such a deviation exist.
Power is the probability of avoiding a Type II error. (FALSE NEGATIVE)

Question 11

FORMULA FOR POWER IN STATISTICS?

Answer 11

Mathematically, power is 1 – beta. The power of a hypothesis test is between 0 and 1; if the power is close to 1, the hypothesis test is very good at detecting a false null hypothesis.

BETA = FALSE NEGATIVES, I.E. TYPE II ERROR

Beta is commonly set at 0.2, but may be set by the researchers to be smaller.

Consequently, power may be as low as 0.8, but may be higher. Powers lower than 0.8, while not impossible, would typically be considered too low for most areas of research.

Question 12

TYPE I VS TYPE II ERROR

Answer 12

In statistical hypothesis testing, a type I error is the mistaken rejection of an actually true null hypothesis (also known as a “false positive” finding or conclusion; example: “an innocent person is convicted”), while a type II error is the mistaken acceptance of an actually false null hypothesis (also known as a “false negative” finding or conclusion; example: “a guilty person is not convicted”).

TYPE I = ALPHA
TYPE II = BETA

Question 13

WHAT INFLUENCES POWER IN STATISTICS?

Answer 13

Significance level (or alpha)
Sample size
Variability, or variance, in the measured response variable
Magnitude of the effect of the variable

Question 14

WHAT IS DELTA IN STATISTICS?

Answer 14

Delta is the overall change in a value. (THE TRETAMENT EFFECT)