Self-controlled Studies Alec Walker September 2016

Blocking the effect of confounders Randomization Self-matching Self-matching Instruments

Roadmap for self-matching Implications of rates that vary within person Case-crossover studies Self-controlled case series

Example of heterogeneous daily risks Rash in children before and after the 12-month-MMR (mumps, measles, rubella) vaccination A positive vaccine-related effect, peaking Day 9 A positive office-visit effect at Day 0 A negative pre-visit (anticipatory) effect at Days -4 through -1 A negative post-screening effect at Days 1-3 A downward age-trend A step effect up following visit A day of-the-week effect, with peaks at 7-day cycles before and after immunization Brookhart MA, Walker AM, Lu Y, Polakowski L, Li J, Paeglow C, Puenpatom T, Izurieta H, Daniel GW. Characterizing vaccine-associated risks using cubic smoothing splines. Am J Epidemiol 2012 Nov 15;176(10):949-57

A model for transient risks Maclure M. Am J Epidemiol 1991:133:144-53.

A model for transient risks Hypothesized curve of incidence rate in temporal relation to exposure Approximating step function Exposure Baseline Incidence Baseline Incidence Risk Periods Ref. Maclure M. Am J Epidemiol 1991:133:144-53.

A model for transient risks of rash Associated with the MMR vaccine given at 12 months of age. 0 10 20 Day

A model for transient risks of rash Associated with the MMR vaccine given at 12 months of age. Each interval of daily risk that is modeled as being internally homogenous could create a definition of person time. 0 10 20 Day

A model for transient risks of rash Associated with the MMR vaccine given at 12 months of age. Not yet vaccinated >14 days after vaccine Each interval of daily risk that is modeled as being internally homogenous could create a definition of person time. 0 10 20 Day It looks like there might be some natural reference levels within person.

Example Celecoxib, Naproxen and GI Bleeding in the treatment of pain versus Naproxen versus No Treatment Example Celecoxib, Naproxen and GI Bleeding in the treatment of pain MD-perceived risk of peptic ulcer & bleeding (PUB) High risk for peptic ulcer and bleeding makes treatment with naproxen inadvisable. Celecoxib would be better. No treatment at all is safest but on the average less effective. True risk of PUB PUB Hospital Admission

Example Celecoxib, Naproxen and GI Bleeding in the treatment of pain versus Naproxen No Treatment Example Celecoxib, Naproxen and GI Bleeding in the treatment of pain MD-perceived risk of peptic ulcer & bleeding (PUB) The physician’s belief about patient risk, not the true risk, is what affects the choice of therapy. True risk of PUB PUB Hospital Admission

Example Celecoxib, Naproxen and GI Bleeding in the treatment of pain versus Naproxen No Treatment Example Celecoxib, Naproxen and GI Bleeding in the treatment of pain MD-perceived risk of peptic ulcer & bleeding (PUB) Both the true risk and the prescriber-perceived risk are likely to be largely inaccessible to the investigator. True risk of PUB PUB Hospital Admission

Intractable “confounding by indication”? Celecoxib or naproxen or no Rx PUB Risk factors for PUB Both the true risk and the prescriber-perceived risk are likely to be largely inaccessible to the investigator. Physician perception Patient signs & symptoms

Matching on the individual Celecoxib or naproxen or no Rx PUB Risk factors for PUB Matching on person would mean that all comparisons are within person and therefore at a common level of physician perception. Physician perception Patient signs & symptoms

Time-varying exposures Cohort Study Calendar Time

Time-varying exposures and matching on individual Individually Matched Cohort Study Cohort Study Calendar Time Calendar Time

Time-varying exposures and matching on individual Individually Matched Cohort Study Cohort Study Calendar Time Calendar Time Drop person hisotries when there the individual does not suffer an event is a consequence of extreme stratification. With the individual as the unit of stratum formation, all the individuals for whom no event is recorded (read: all strata in which no event occurs) drop out of the analysis. This will turn out to have enormous implications for data gathering and cost.

Compare different risk periods within individuals Between-person confounding Does not exist because there are no comparisons between persons Time-dependent confounding If risk factors can vary over time in synchrony with exposure status, there could still be time-dependent confounding. We’ll have to consider control for this when we go into the analysis.

Comparative vs. Crossover designs Treatments are compared between individuals. Baseline comparability of groups is sought through random allocation (RCT) or through careful subject selection and covariate control. Randomized trial Cohort Study “Self-controlled” analyses, in which treatments are compared within individuals, are common in intervention trials, but are also possible in purely observational studies. The design greatly enhances the baseline similarity of comparison groups. Today we need to consider another type of trial as the counterpart for our observational studies, the cross-over design. Randomized trial Cross-over design Cohort Study with Changing Exposure

In sum When risk varies within individual, it may be possible to construct an analysis that restricts all the comparisons between risk levels to within- person comparisons. With restriction to within-person comparisons, Between-person differences in time-invariant risk factors cannot cause confounding Data collection is restricted to those individuals who experience an event Time-dependent confounding is still a threat.

Roadmap More implications of rates that vary within person Case-crossover studies Self-controlled case series

Case-Crossover mechanics Use only for events that may be triggered by a discrete exposure. For each case, identify the presence or absence of the exposure during the postulated trigger period. Identify the “expected” exposure in the trigger period from Usual frequency of exposure over a long time Documented occurrence of exposure during one or more comparable times in the past Summarize Observed vs. Expected across all cases using an appropriate matched analysis.

Match analyses on person Recall that individual histories drop out of a matched analysis when there is no event. Case-crossover studies are matched cohort studies that take personal matching to its limit: The individual forms the unit of stratification, and the comparison is between different exposure windows within individuals. Time  26

If it’s difficult to ascertain exposure at all times in the past, Sample This becomes a case-control study nested within a matched cohort. Time  27

Person-days not sampled drop out of consideration Case-crossover studies are matched cohort studies that take personal matching to its limit: The individual forms the unit of stratification, and the comparison is between different exposure windows within individuals. Time  28

Final product: Matched sets of person-days Case-crossover studies are matched cohort studies that take personal matching to its limit: The individual forms the unit of stratification, and the comparison is between different exposure windows within individuals. Time  29

Case and control windows Case window: period preceding the event during which the exposure may have altered the risk Control window(s): periods of the same length as, and not overlapping with, the case window that provide an estimate of the expected frequency of exposure for each case. The core study technique is to identify cases, then ascertain exposure status in the case window and at earlier points in time – the control windows. 30

Case and control windows: Matched sets of person-days Case window Case-crossover studies are matched cohort studies that take personal matching to its limit: The individual forms the unit of stratification, and the comparison is between different exposure windows within individuals. Time  31

Estimating the relative risk Case Window Control Window Exposed Exposed Yes No f10 f01 For dichotomous exposures Form the matched 2x2 table Place each case according to exposures in the case and control windows Mantel-Haenszel odds ratio for matched sets reduces to ratio of counts in discordant exposure windows: ( f10 / f01 ) when there is one control Concordant case-control windows are uninformative Dichotomous exposures and two risk periods can be handled as described in the preceding paragraph. Polytomous, or continuous exposure measures and multiple risk periods require a conditional logistic regression analysis. Risk periods of varying length can be analyzed using the techniques of stratified analysis of person-time data. The matched analysis may also be stratified by fixed personal characteristics to look for factors that modify the effect of the exposure on the risk of outcome.

Estimating the relative risk Case Window Control Window Exposed Exposed Yes No f10 f01 For dichotomous exposures Form the matched 2x2 table Place each case according to exposures in the case and control windows Mantel-Haenszel odds ratio for matched sets reduces to ratio of counts in discordant exposure windows: ( f10 / f01 ) when there is one control Concordant case-control windows are uninformative For polytomous and multiple exposures (covariates) Conditional logistic regression is used when There are several different exposures or exposure levels There are concurrent time-varying confounders Dichotomous exposures and two risk periods can be handled as described in the preceding paragraph. Polytomous, or continuous exposure measures and multiple risk periods require a conditional logistic regression analysis. Risk periods of varying length can be analyzed using the techniques of stratified analysis of person-time data. The matched analysis may also be stratified by fixed personal characteristics to look for factors that modify the effect of the exposure on the risk of outcome.

Celecoxib or naproxen or no Rx Example Celecoxib or naproxen or no Rx PUB Risk factors for PUB Matching on person would mean that all comparisons are within person and therefore at a common level of physician perception. Physician perception Patient signs & symptoms

Pharmacoepidemiology and Drug Safety 2011; 20: 763–771 Risk of hospitalization for upper gastrointestinal adverse events associated with nonsteroidal anti‐inflammatory drugs: a nationwide case‐crossover study in Taiwan Chia‐Hsuin Chang1,2†, Hsi‐Chieh Chen1†, Jou‐Wei Lin3, Chuei‐Wen Kuo4, Wen‐Yi Shau5 and Mei‐Shu Lai1* 1Institute of Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan 2Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan 3Cardiovascular Center, National Taiwan University Hospital Yun‐Lin Branch, Dou‐Liou City, Yun‐Lin, Taiwan 4National Health Insurance Mediation Committee, Department of Health, Executive Yuan, Taipei, Taiwan 5Division of Health Technology Assessment, Center for Drug Evaluation, Taipei, Taiwan ABSTRACT Purpose This study aimed to evaluate the risks of upper gastrointestinal (GI) adverse events across a variety of oral and parenteral coxibs and nonselective nonsteroidal anti‐inflammatory drugs (nsNSAIDs) in the general population of Taiwan. Methods In a case‐crossover study, all patients aged ≥20 years who were hospitalized for upper GI adverse events (peptic ulcer and bleeding; gastritis and duodenitis) in 2006 were identified using the International Classification of Diseases, 9th Revision, Clinical Modification diagnosis codes from inpatient claims from the Taiwan National Health Insurance Database. For each patient, the case period was defined as 1–30 days and the control period as 31–60 days before the date of hospitalization. Outpatient pharmacy prescription database was searched for individual NSAID use during the case and control periods. A conditional logistic regression model was applied, and adjusted self‐matched odds ratios (OR) and their 95% confidence intervals (95%CI) were reported. Results A total of 40 635 patients hospitalized for upper GI adverse events were included. The adjusted OR was 1.52 (95%CI: 1.27–1.82) for celecoxib and 2.56 (95%CI: 2.44–2.69) for oral nsNSAIDs. The ORs were above 2 for oral piroxicam, diclofenac, ketorolac, ketoprofen, acemetacin, and naproxen and were around 1.5 for tiaprofenic acid, indomethacin, mefenamic acid, and ibuprofen. Higher risks were evident for parenteral NSAIDs, in particular ketorolac with an OR of 5.76 (95%CI: 5.14–6.44). Conclusion Use of celecoxib and all nsNSAIDs studied was associated with a greater risk of upper GI toxicity as compared with nonuse. Parenteral NSAIDs posed a higher risk, but celecoxib, ibuprofen, and mefenamic acid posed a lower risk than other NSAIDs. Risk of hospitalization for upper GI adverse events associated with nonsteroidal anti‐inflammatory drugs… Chia‐Hsuin Chang, Hsi‐Chieh Chen, Jou‐Wei Lin, Chuei‐Wen Kuo, Wen‐Yi Shau and Mei‐Shu Lai Purpose This study aimed to evaluate the risks of upper gastrointestinal (GI) adverse events across a variety of oral and parenteral coxibs and nonselective nonsteroidal anti‐inflammatory drugs (nsNSAIDs) in the general population of Taiwan. Methods In a case‐crossover study, all patients aged ≥20 years who were hospitalized for upper GI adverse events (peptic ulcer and bleeding; gastritis and duodenitis) in 2006 were identified ... For each patient, the case period was defined as 1–30 days and the control period as 31–60 days before the date of hospitalization. Outpatient pharmacy prescription database was searched for individual NSAID use during the case and control periods. A conditional logistic regression model was applied ... Results A total of 40 635 patients hospitalized for upper GI adverse events were included. The adjusted OR was 1.52 (95%CI: 1.27–1.82) for celecoxib and 2.56 (95%CI: 2.44–2.69) for oral nsNSAIDs… Conclusion Use of celecoxib and all nsNSAIDs studied was associated with a greater risk of upper GI toxicity as compared with nonuse… Pharmacoepidemiology and Drug Safety 2011; 20: 763–771

Control and case windows in Chang et al When an individual experiences no event, his/her record does not enter into the anlaysis X Control Case Windows X Control Case Windows Key Celecoxib Naproxen No Therapy Event X

Pharmacoepidemiology and Drug Safety 2011; 20: 763–771 Case Window Control Window Exposed Exposed Yes No f10 f01 Pharmacoepidemiology and Drug Safety 2011; 20: 763–771

Control Window Exposed Case Window Control Window Exposed Exposed Yes No 413 f01 Pharmacoepidemiology and Drug Safety 2011; 20: 763–771

Pharmacoepidemiology and Drug Safety 2011; 20: 763–771 Case Window Control Window Exposed Exposed Yes No 413 232 Pharmacoepidemiology and Drug Safety 2011; 20: 763–771

Pharmacoepidemiology and Drug Safety 2011; 20: 763–771 Case Window Control Window Exposed Exposed Yes No 413 232 RRmatched = 413/232 = 1.78 Pharmacoepidemiology and Drug Safety 2011; 20: 763–771

Case-Crossover design options Mueller JE. Am J Cardiol 2000;86(suppl):14F–18

In sum Case crossover studies compare exposure at the time of an event to the historical frequency of exposure in the same person Very efficient, not always feasible Gives mathematical meaning to: “Were you doing anything unusual just before you got sick?” Works for easily ascertained, intermittent exposures whose associated risks rise and fall quickly Requires that the onset time of the outcome event can be pinned down with a precision that is substantially tighter than the width of the exposure window

Roadmap More implications of rates that vary within person Case-crossover studies Self-controlled case series

Cohorts with changing exposure and continuing follow-up  End of f/u  Event Time 

Match analyses on person Just as in the rationale for case-crossover studies, individual histories drop out of a matched analysis when there is no event.  End of f/u  Event Time  46

Self-controlled case series Look at the affected individuals’ entire exposure record Exposures quantified by person time Events modeled using a rate model Could be accomplished with Mantel-Haenszel techniques for stratified cohort analyses in the case of two exposure levels More generally accomplished by a regression that is Conditional (i.e. stratified on person) Log-linear, estimating ratio measures Poisson (events in the numerator) With “offsets” for the volume of person-time

Self-controlled case series Should events terminate follow-up? In the original formulations, they did not Developed for vaccine studies Air pollution Post-event follow-up is OK when The event itself cannot affect exposure status Post-vaccine time windows in someone already vaccinate Environmental exposures Events terminating follow-up now increasingly accepted Assumptions violated, but practical effect appears to be small

Whitaker HJ, Farrington CP, Spiessens B, Musonda P Whitaker HJ, Farrington CP, Spiessens B, Musonda P. Tutorial in biostatistics: the self-controlled case series method. Statistics in Medicine. 2006; 25(10): 1768-97. Age in days 

“Based on evidence from previous studies, and on knowledge of the time taken by the mumps virus to replicate, we choose the risk period to contain days 15–35 inclusive after receipt of MMR.” Whitaker HJ, Farrington CP, Spiessens B, Musonda P. Tutorial in biostatistics: the self-controlled case series method. Statistics in Medicine. 2006; 25(10): 1768-97. Age in days 

Age group 1 366-547 days Age group 2 548-705 days Age in days  Whitaker HJ, Farrington CP, Spiessens B, Musonda P. Tutorial in biostatistics: the self-controlled case series method. Statistics in Medicine. 2006; 25(10): 1768-97. Age in days 

Age group 1 366-547 days Age group 2 548-705 days Whitaker HJ, Farrington CP, Spiessens B, Musonda P. Tutorial in biostatistics: the self-controlled case series method. Statistics in Medicine. 2006; 25(10): 1768-97.

Age group 1 366-547 days Age group 2 548-705 days Whitaker HJ, Farrington CP, Spiessens B, Musonda P. Tutorial in biostatistics: the self-controlled case series method. Statistics in Medicine. 2006; 25(10): 1768-97.

Age group 1 366-547 days Age group 2 548-705 days Whitaker HJ, Farrington CP, Spiessens B, Musonda P. Tutorial in biostatistics: the self-controlled case series method. Statistics in Medicine. 2006; 25(10): 1768-97.

Age group 1 366-547 days Age group 2 548-705 days Whitaker HJ, Farrington CP, Spiessens B, Musonda P. Tutorial in biostatistics: the self-controlled case series method. Statistics in Medicine. 2006; 25(10): 1768-97.

Age group 1 366-547 days Age group 2 548-705 days Whitaker HJ, Farrington CP, Spiessens B, Musonda P. Tutorial in biostatistics: the self-controlled case series method. Statistics in Medicine. 2006; 25(10): 1768-97

Homogeneous exposure periods are distinct records Whitaker HJ, Farrington CP, Spiessens B, Musonda P. Tutorial in biostatistics: the self-controlled case series method. Statistics in Medicine. 2006; 25(10): 1768-97

Conditional Poisson regression Whitaker HJ, Farrington CP, Spiessens B, Musonda P. Tutorial in biostatistics: the self-controlled case series method. Statistics in Medicine. 2006; 25(10): 1768-97

Murphy et al. N Engl J Med 2001;344:564-72

Murphy et al. N Engl J Med 2001;344:564-72

In sum: Self-controlled case series Self-controlled case series compare exposure at the time of an outcome event to the distributions of exposure in the same person. Unmeasured covariates that do not vary over time within person do not confound the estimate of relative risk. Self-matched studies work well for intermittent exposures whose associated risks rise and fall quickly. The case-crossover study is a special example of self-controlled case series in which Event terminates follow-up Exposure history may be sample rather than exhaustively tabulated

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