What is Statistics? Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 1 / 23

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1 What is Statistics? Some definitions of Statistics: The science of Statistics is essentially a branch of Applied Mathematics, and maybe regarded as mathematics applied to observational data. Fisher The science and art of dealing with variation in such a way as to obtain reliable results. Mainland Statistics is concerned with the inferential process, in particular with the planning and analysis of experiments or surveys, with the nature of observational errors and sources of variability that obscure underlying pattern. Kruskal Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 1 / 23

2 What is Statistics? (cont d) Good statistical practice is equally demanding of appreciation of factors outside the formal mathematical structure, essential though that structure is. Finney Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 2 / 23

3 An Example of Practice: Clinical Trials Clinical trials are carefully planned and monitored experiments in human patients for the purpose of evaluating one or more potentially beneficial therapies 1946: trials initiated in U.K. for treating tuberculosis early efforts by Eastern Cooperative Oncology Group developing new methodology for randomized clinical trials multi-center clinical trials could enroll enough patients to carry out rigorous clinical research currently large clinical trials in cancer, AIDS, cardiovascular disease, diabetes, Alzheimer s disease, etc. Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 3 / 23

4 Population and Sample Statistical concepts: Population: total set of individuals (objects, animals, or people) of interest in a study, eg. all people with lung cancer Sample: a subset of the population from which the data is collected, eg. all heart disease patients coming to UCSD Medical Center Use the results observed in the sample to estimate certain characteristic of the population The sample should be representative of the population, so that the inference (conclusion) is O.K. Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 4 / 23

5 Polio vaccine trials Example: 1954 polio vaccine trials Polio was never a common disease, but was one of the most frightening. It struck young children the hardest. It was responsible for 6% of the deaths in the age group 5 to 9 in the early 1960 s. It left many children crippled, and also many children who could only survive on a respirator. With the involvement of President Franklin Roosevelt, a polio victim himself, a large research effort was developed to find a vaccine. Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 5 / 23

6 Polio vaccine trials (cont d) In 1954, a vaccine developed by Jonas Salk was shown in preliminary testing to be safe and effective Two large, national clinical trials (involving about 2,000,000 polio-free children) were undertaken to determine if the vaccine was really effective. Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 6 / 23

7 Polio vaccine trials (cont d) The statisticians had to decide Study design: whom to sample; how to sample; how many to sample. Analysis plan: how to analyze the data to answer the question of interest; for example, a t-test is often used to see if a treatment is effective. Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 7 / 23

8 Randomization Two trials on young school children Trial A: 2nd graders parent consent Y. vaccine They were compared with 1st & 3rd graders = no significant difference Later discovered that parents giving consent were in higher socio-economic level and the children were at higher risk for polio. Trial B: Y. 1st 3rd graders parent consent randomize to vaccine or placebo Showed effectiveness of vaccine. Randomization ensures that risk factors are balanced across the groups, at least in large enough samples. Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 8 / 23

9 Polio vaccine trials (cont d) Results of trial B Treatment N # polio rate (/100,000) Vaccine 200, Placebo 201, Further refinements of the Salk vaccine cut the rate of polio to zero. Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 9 / 23

10 Questions for Thought What are the variations (uncertainties) in the data? Is 41 per 100,000 significantly different from 81 per 100,000? What are the probability distributions involved here? Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 10 / 23

11 Another example: observational studies In some studies, we cannot randomize due to ethical or other reasons. Example: should a pregnant woman take any medication or vaccine? Clinical trials typically exclude pregnant women Existing data are fuzzy, about the dangers of using key medications going without key medications Examples: H1N1 flu: pregnant women are at high risk for complications Asthma: attacks can lead to babies with low birth weights, and/or birth defects There often exist lab or animal data Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 11 / 23

12 An Early Study on Prozac (Fluoxetine) Prozac was the most commonly used antidepressant drug in the US Between 1989 and 1995, through the California Teratogen Information Service (CTIS) 228 pregnance women taking Prozac and 254 women not taking Prozac were followed prospectively for pregnancy outcomes: Women having taken Prozac do not have increased risk of spontaneous abortion (SAB) or major fetal anomalies; Women who took Prozac in 3rd trimester are at increased risk for perinatal complications Published in the New England Journal of Medicine, Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 12 / 23

13 Organization of Teratology Information Specialists (OTIS) OTIS is a North American network of university or hospital based teratology information services since More than 20 sites; Counsels 70, ,000 pregnant women and health-care providers every year mainly by telephone on drug and other exposures in pregnancy and lactation: prescription meds, over-the-counter meds, vitamins, herbal preparations, illicit drugs, alcohol, caffeine, tobacco, pesticides, occupational exposures, infectious or chronic disease, medical procedures, cosmetics, etc. Invites some callers to enroll in research studies where the women and their babies are followed over time. Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 13 / 23

14 Causal inference These prospective studies are observational by nature, so there may well be risk factors that are unbalanced between the exposure groups. Without randomization, often one can only establish association, but not causality; eg. does moderate drinking of wine reduce the risk of non-alcoholic fatty liver disease? Often there is confounding, which is the major challenge in observational studies for drawing causal inference. For example, from the recent H1N1 vaccine data, there is often a healthy behavior effect, i.e. women who seek to take vaccine tend to pay more attention to their own health in general. Such behavior might make the vaccine appear protective against adversaries during pregnancy. Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 14 / 23

15 Data Collection Standardized and structured telephone intake interview: medical history, pregnancy history, pregnancy weight and height, occupation, education, income, ethnicity, current use of meds, vitamins, herbal supplements, alternative therapies, alcohol, caffeine, tobacco, illicit drugs, other environmental or occupational exposures, current pregnancy complications including illnesses. may also include maternal nutrition, timing of exposure to the fetus, blood level of the exposure, etc. Pregnancy diary, to record additional exposures or events. Interim phone interviews in 2nd and 3rd trimester to update exposures, prenatal test results, contact information, whether the pregnancy has ended before the expected due date, etc. Outcome phone interview, any interim time point if the pregnancy has ended, or within 4 weeks after the expected due date: birth date, mode of delivery, birth size, birth complications, hospital stay, pregnancy weight gain, additional exposures and prenatal test results, and release of medical records. Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 15 / 23

16 Data Collection (cont d) Dysmorphology examination, within 6 months after live birth: examiners blinded to exposure status; standardized checklist of minor malformations; independent confirmation of certain defects; body measurements, compared to National Center for Health Statistics growth curves or other publications weight, length, head circumference, palpebral fissure length, inner canthal distance, ear length, philtral length, etc. Follow-ups at 1 year of age and later, with growth measurements and neurobehavioral testing, imaging, etc. Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 16 / 23

17 Flu Vaccine Study Since the H1N1 flu pandemic, which caused complications in pregnancies, the Vaccines and Medications in Pregnancy Surveillance System (VAMPSS) was funded to evaluate, among other exposures, the safety of seasonal influenza vaccine during pregnancy. Between , 1730 pregnant women were recruited in the US and Canada; among them 1263 were exposed to an influenza vaccine, and 467 were unexposed to any influenza vaccine. The results were published in Vaccine Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 17 / 23

18 Vaccine Study Results Birth defects Unexposed Exposed 1st crude RR Adjusted RR a trimester (95%CI) (95%CI) Live births 12/409 25/ (2.9%) (5.6%) (0.97, 3.75) All pregn. 13/427 26/ (3.0%) (5.7%) (0.97, 3.59) a No adjusted RR was calculated because no confounder was identified. Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 18 / 23

19 Vaccine Study Results Spontaneous abortion SAB: loss of fetus by 20 weeks of gestation (afterwards it is called still birth ) Unexposed Exposed 1st crude RR Adjusted RR a trimester (95%CI) (95%CI) SAB 15/267 9/253 KM estimate 15.9% 14.4% %CI (8.9%, 27.5%) (6.6%, 29.6%) (0.39, 2.04) (0.47, 2.65) a Adjusted for pre-pregnancy body mass index and influenza season year. Note that the KM estimate rates of SAB are much higher than the simple rates like 15/267. This reflects sampling bias, i.e. sample that is not representative of the population; in this case women with early abortion tend not to be captured in the study. Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 19 / 23

20 Vaccine Study Conclusion A moderately elevated relative risk (RR) for major birth defects, although the 95% confidence interval (CI) included 1; no meaningful evidence of increased risk for spontaneous abortion or preterm delivery following exposure to the seasonal influenza vaccine. Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 20 / 23

21 Biased sample is a usual feature of big data Online surveys: responders are often self selected; a few years ago (Dec 2013), A&E briefly fired Duck Dynasty actor Robertson for antigay comments based on Tweeter data, only to find that these are not the regular watchers of the camo-wearing Louisiana clan whose members openly celebrate being rednecks. (Source: NY Times Magazine article Big (Bad) Data ) Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 21 / 23

22 Big biomedical data Example: -omics data (genomic, proteomic, metabolomic, microbiom, etc.) generated by high throughput technology A typical question: is there a list of features (genes, etc.) that are most likely related to the experimental conditions? different tissue types: normal vs diseased or different treatments, time points Analysis of such experiments is nontrivial because of the large numbers of predictors and many sources of variation. Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 22 / 23

23 Electronic health records (EHR) data As a demonstration project for using EHR for clinical decision making - link the national Surveillance, Epidemiology and End Results (SEER) Program database with the federal Medicare database; contains prostate cancer patients of age 65 or older; 57,011 patients diagnosed ; 8971 binary insurance claim codes (disease diagnoses, surgical procedures, hospitalization and outpatient activities, etc.); additional clinical and demographical variables. Goal: predict cancer versus non-cancer mortality. Ronghui (Lily) Xu (UCSD) An Introduction to Statistics 23 / 23

NEURODEVELOPMENT OF CHILDREN EXPOSED IN UTERO TO ANTIDEPRESSANT DRUGS

NEURODEVELOPMENT OF CHILDREN EXPOSED IN UTERO TO ANTIDEPRESSANT DRUGS ABSTRACT Background Many women of reproductive age have depression, necessitating therapy with either a tricyclic antidepressant drug